Dreamforce ’26 From the Roundtables: 8 Takeaways for Sales Cloud and Account Engagement Teams

Dreamforce ’26 From the Roundtables: 8 Takeaways for Sales Cloud and Account Engagement Teams

by | Oct 6, 2026

Written by: Carol Springer – Co-Founder Gabriel Sales, Head of Salesforce, Account Engagement (Pardot) & Marketing Cloud Next Consulting

What we heard from Salesforce. What we learned from 100+ of your peers at two dozen Dreamforce roundtables. And what it means for the front line professionals who run the systems and the leaders who fund them.

 

Short Summary:

Dreamforce 2026 was built for the enterprise. After two dozen roundtables with 100+ Salesforce peers, we found most mid-market teams are using generative AI but not agents yet, held back by data readiness, admin bandwidth and unclear pricing. Here are our 8 takeaways and a 90-day plan.

Executive Summary and Overview

The goal of this article is to share what we learned at Dreamforce and how it impacts Mid Market Enterprise and Small Businesses’ Sales and Marketing Teams. AI is complicated so it’s a pretty long article because it’s designed to contextualize what we have learned at Dreamforce and then after Dreamforce testin some of the tools.  Our goal is to help you plan for both short term and long term success.

We’ll start with high level tech news, key take aways, why you should care, and what to do now and next year.

Then we’ll dive deeper into each technology take away and what matters most for Sales and Marketing Leaders, Admins and end users of Sales Cloud and Marketing Cloud Account Engagement (Pardot).

Our Approach to Dreamforce This Year

Beyond the Keynotes and watching demos, most of our time at Dreamforce was spent at two dozen customer roundtables which are much different from a normal breakout session. A breakout session is usually a 40-minute presentation or demo of a specific product followed by a few questions. A Salesforce roundtable is almost like a mini focus group/networking event: most are about an hour, with a small group of 5-8 peers per table, and a host who sets the topic, speaks for 10 minutes and then gets out of the way. You hear admins, marketing ops managers, and sales and marketing leaders compare what they’ve tried: what worked, what stalled, and what they’d do differently. Nobody is selling anything. It was invaluable helping us to understand the difference between the hype, the potential and what’s happening with AI both inside and outside the Salesforce Eco-system.

Main Strategic Considerations for Mid-Market Enterprise and Small Business Sales and Marketing Teams

1. Agentic AI is getting closer, and it’s easy to feel behind. You’re not. Everyone is talking about Agentic but very few Mid-Market and SMBs are executing using Agents for marketing and sales, especially at scale.  Learn More →

2. Let “Headless” Salesforce play out. It’s in very early beta so you should let the Enterprise Market make the investments to improve it for use by marketing and sales organizations. Right now, for the Mid Market Enterprise and SMB, “Headless” is more about how you can reach your data for generative AI through your preferred AI tools. Learn More →

3. Salesforce Foundations is also a new option for experimenting with Agentic for smaller companies. It will give you an easy way to test Agentic AI without fully committing to Data 360. But be wary of pricing issues long term.  Learn More →

4. In 2027 both Mid Market Enterprises and SMBs should prioritize getting your data and tech stack ready. You need to  have your own clean, complete and proprietary data. This will be the top driver of competitive advantages after these Agentic Salesforce tools come out of beta. Learn More →

5. Salesforce AI pricing should also become clear and predictable at the same time sometime in 2027.  Understanding pricing is still a total mess unless you move to the “Unlimited” which is probably temporary.

Most Important Salesforce Technology Takeaways for Marketing and Sales Teams

1. Salesforce went “Headless”. Your CRM data can be used by Claude, Slack, and other AI tools your team already uses. Whether AI answers can be trusted now depends on the data behind them. Learn More →

2. Salesforce isn’t building one giant AI model. It launched Koa. And it’s also offering choice. You don’t have to pick the “winning” AI. Your own marketing, sales and customer data is the competitive advantage that will last. Learn More →

3. Salesforce AIForce now has “job-ready” agents with names. Piper helps qualify inbound leads and Hunter works outbound pipeline. Both are dependent, to some degree, on your existing scoring processes. Learn More →

4. Marketing got almost nothing new, and that’s no reason to panic (or move to Marketing Cloud Next or Data 360 yet). Account Engagement is still fully supported. Prepare your data now and let your business case, and data readiness, decide when you move to Marketing Cloud Next. Learn More →

5. Pricing is still a black box and this creates anxiety for everyone. This make it difficult to plan and next to impossible to calculate future ROI against existing costs.  Learn More →

6. The Enterprise is deploying Agentic AI, but most Mid Market companies aren’t. You’re on the normal curve for your size. Build a baseline now so you can prove ROI when you do deploy. Learn More →

7. Data readiness is the roadblock, and Salesforce also stated this multiple times at Dreamforce. Even Salesforce cleaned its own data before moving it into Data 360. Learn More →

8. The naming whiplash and confusion continues. Agentforce Sales is Sales Cloud again, and Agentforce Marketing is Marketing Cloud again. Judge products by what they do, not by what they’re called this quarter.  Understanding what you are currently subscribed to is even trickier.  They are giving a lot of seats and usage away for free before we know what it will cost. Learn More →

What should Mid-Market and Small Business Salesforce teams do in the next 90 days?

Start now with three moves: take inventory of what you already own, clean up your Sales Cloud and Account Engagement data, and run one low-cost AI pilot against a measured baseline. Here is how we would sequence it, plus how to plan for 2027.

The general strategic stance

  • You’re not behind. Mid-market and SMB teams that use the next 12 months to get their data, processes and ROI tracking ready will be the ones that capture value once agents mature and pricing settles.
  • Your competitive advantage is your own clean, complete, proprietary data, not which AI model you pick. Models will keep changing. Your data only gets better if you invest in it.
  • Let the enterprise pay to perfect “Headless” Salesforce and the new agents. Your job is to be ready to adopt them quickly when they’re proven.
  • Stay on Account Engagement unless your business case says otherwise. Prepare as if you’ll move to Marketing Cloud Next, but let data readiness and ROI set the date.

Your 90-day plan, starting now:

  • Days 1–30: Know what you have.
    • Inventory your current Salesforce contract, licenses, credits and AI tools, including renamed products and anything you got for free.
    • Name an executive sponsor from both marketing and sales for data readiness.
    • Turn on Digital Wallet tracking before anyone tests agents.
    • Gabriel Sales can help with a Free Salesforce Technology Health Check to see where you stand and what to fix first.
  • Days 31–60: Get a baseline and clean up.
    • Run a full data readiness review: duplicates, incomplete records, stale records, and sync errors between Account Engagement and Sales Cloud. Gabriel Sales can help with a Data Readiness Audit.
    • Retire unused fields, old flows and stale automation rules in both systems. Our Account Engagement Audit can help.
    • Document your lead flow and hand-off rules.
    • Record your current pipeline, conversion and rep-productivity numbers so you have something to measure AI against.
  • Days 61–90: Run one cheap, high-impact pilot.
    • Test one or two low-cost use cases on your most mature, repeatable process. Our pick is meeting prep (“prep me for my meeting with [Company]”) through Claudeforce or Agentforce Coworker. You can also test both to track spend.
    • Log every task and the credits it uses, and compare the results against your baseline.
    • If you use Apollo for prospecting, put Hunter on your evaluation list. Salesforce named Apollo a flagship data partner for Hunter, so Apollo’s contact data and prospecting work directly inside the agent (SalesTechStar). Hunter is scheduled for general availability in November 2026.

2027 by quarter

  • Q1: Form a data governance council, with business owners for leads, contacts, accounts and opportunities and admins as custodians. Scale the pilot only if it showed measurable ROI.  Keep tabs on the Winter 27 marketing features release when it comes out.
  • Q2: Start moving clean data into Data 360 for your most mature use case so you are ready to test.
    • Pilot Hunter for outbound.
    • Consider Piper only if your scoring and hand-off can’t already do the job.
  • Q3: Review the Winter ’27 marketing features once they’re out of beta. Build the business case for or against Marketing Cloud Next using real consumption and ROI data from your pilots.
  • Q4: Scale the one or two pilots that proved ROI, and walk into renewal with your consumption data and your questions in hand.

How to win against similar-sized competitors

  • Most of your peers are stuck on “the data isn’t ready.” Fixing yours over the next six months puts you ahead when agents come out of beta.
  • Pick use cases by cost against impact, not by what looked best in a keynote. Cheap, high-value generative wins now beat expensive agents that run on bad data.
  • Benchmark and be prepared to build the ROI story as you go. You need to be ready with the business case when the CFO or board asks.

Most of these recommendations are complimented by our evolving and proprietary Automation and Sales Enablement Maturity Matrix. Watch a 3 minute video here.

Introduction and Overall Impression of Dreamforce 2026

Going to an event like this in the emerging age of Agentic AI was a bit overwhelming because the event blends two very different realities. There is the future state represented by what Enterprise companies are in the process of doing and what they aspire to do with AI. And there is what is ready for use right now.

Salesforce’s Enterprise Customers are working with them to co-create Agents right now.  They can do this because they have the most data, and the budgets to fund this work. They also have the blue-chip consulting firms like IBM, Deloitte and BCG to steward strategy, and the seasoned data scientists to secure it.  There was a great deal of real innovation using AI but it also felt like there was a lot of posturing about what’s possible.

The second reality is what is currently available and possible for the Mid-Market and Small Business. Both in terms of budget and safe/cost effective access to technology.

The messaging almost always skewed to the Enterprise because that’s where billions of dollars are spent in large chunks. So it’s easy to think everyone is already deploying agentic AI.  The Enterprise is making real Agentic progress and it’s costing them a great deal of money. So the disparity is real, It’s easy for Mid-Market and SMB companies to walk out of a Keynote and Fortune 500 demo at the Moscone Center feeling like everyone has already successfully deployed Agents and that you are woefully behind. You’re not behind. Which we quickly discovered at the roundtables.

In our roundtable conversations with 100+ marketing and sales peers at two dozen roundtables almost no one in the Mid-Market was running agents yet. Most companies are using generative AI.  Very few are using agentic AI. What we heard from multiple Mid-Market and SMB sales and marketing professionals was the same two problems: “the data isn’t ready”, and the “team doesn’t have the bandwidth to learn these technologies”. Everyone we heard from was at various stages of figuring this out.

As a side note what we did hear from the handful of companies that were using Agentic was that they were using it for customer support.

When we got back from Dreamforce we looked at the statistics to see if what we heard and experienced was supported by market studies. The stats confirmed what we learned/heard from peers at the show.

  • On generative AI, the Mid-Market is keeping pace: 94% of Mid-Market companies already use it (Kaufman Rossin, May 2026).

The gap opens up when AI moves from drafting, researching and summarizing to agents that take action on their own.

  • McKinsey’s latest State of AI found that 40% of organizations with more than $1 billion in revenue are scaling AI agents, up from 27% a year earlier, while the share at smaller organizations stayed flat at 22%.
  • 66% of customer service organizations now use agentic AI (Salesforce)
  • 13% of marketers are currently using agentic (Salesforce)
Salesforce State of Marketing (10th edition) chart showing the share of marketing organizations using each type of AI: generative AI at 56–72% but agentic AI at only 9–17%, by high, moderate and underperformers.

Salesforce State of Marketing (10th edition) chart showing the share of marketing organizations using each type of AI: generative AI at 56–72% but agentic AI at only 9-17%, by high, moderate and underperformers.

Key Salesforce Technology Takeaways from Dreamforce

Takeaway 1: What is Salesforce AIforce, and what does “Headless” Salesforce mean?

AIforce is Salesforce’s new framework that lets your team use Salesforce data and workflows from inside Claude, Slack and other tools, and “headless” means Salesforce is separating your data from its screens, so you no longer have to log into Salesforce to work with your CRM.

The headline of the show was “AIforce.” Salesforce summed it up this way: “With AIforce, people don’t have to go to login to Salesforce to get work done. Salesforce comes to them in Claude, Slack, Lightning, or wherever they want to work” (Salesforce).

Right now four things make that work:

Claudeforce (Salesforce in Claude) connects Anthropic’s Claude to your Salesforce org so a rep can ask questions and take action. It has 37 prebuilt sales skills and is available to all customers in beta.

Slackforce turns a prompt in Slack into a live, interactive view of your Salesforce data that your team can filter and explore.

Agentforce Coworker is an AI assistant built into the Salesforce Lightning screens your team already uses.

How to think about “Headless” for your team?

Think of Salesforce as two layers. One is the database where your accounts, contacts, opportunities, and campaign history live. The other is the screen/tool/user interface (UI) you click through to see and update your Salesforce data. Going headless means Salesforce is separating the two. Your data stays in Salesforce, but the “head,” the place where you see and work with the data on your screen can be Claude, Slack, or a custom app. Salesforce is betting that its long-term value is being the trusted system of record underneath whichever AI tools your company chooses.

Gabriel Sales believes this is probably true.  It’s the best of both worlds now.  Your team can access secure and protected data with the AI chat interfaces your teams want to use. Below are two quick video examples.

Claudeforce Demo: Using Salesforce Data Inside Claude

(Watch Time 90 Sec) Watch how we connect Claude to our own Salesforce org and ask questions about our CRM data in plain English, with no Salesforce screens required. A quick look at what “headless” Salesforce means for sales and marketing teams.

Agentforce Coworker Demo: Salesforce’s AI Assistant at Work

(Watch Time 2 Min) See Agentforce Coworker answer a real question about our pipeline and test data inside Salesforce, and what that one request cost in credits. A practical first look for Sales Cloud and Account Engagement teams.

Specific New Considerations

For Marketing Leaders & Sales Leaders

The question you’ll be asked is changing. It used to be “how do we get reps to log into Salesforce?” Now it’s also “can we trust what the AI says when it pulls from Salesforce?” For example, if a rep asks Claude for last quarter’s pipeline and the answer is built on duplicate accounts and stale opportunity stages, the AI will give them the wrong number quickly and confidently. As you upgrade the tools become powerful, but the challenge will become a data quality problem, and it’s worth budgeting for that before you upgrade seats.  In parallel it’s also going to be critical that reps continue to add data and their inputs into the system.  Making administration as seamless as possible will be critical.

For Salesforce Admins & Account Engagement End Users

Permissions and field-level security (the settings that control who can see and edit each field) now decide what an AI will say to a salesperson in Slack or Claude. Before anyone turns on the beta, review which profiles and permission sets will connect, confirm sensitive fields are locked down, and retire the fields nobody uses. The AI will read whatever the user can see, including the fields that have been wrong for years.  Salesforce is changing some of these processes and degrading others.

Takeaway 2: What is Salesforce Koa, and which AI models does Salesforce work with?

Koa is Salesforce’s first AI model built specifically for CRM work, and rather than betting on a single model, Salesforce works with several, including Anthropic’s Claude, Google’s Gemini and NVIDIA’s Nemotron, which Koa is built on.

Salesforce announced Koa, its first AI model built just for CRM work, like updating records and moving deals forward. It’s built on NVIDIA’s Nemotron. It was trained on models and not real customer data, and Salesforce says Koa makes three times fewer mistakes on those tasks than other leading AI models. It’s still in early testing with select customers.

Salesforce also expanded its partnerships with Google Cloud, bringing Gemini Enterprise into the mix (Salesforce). Between this, and its Anthropic partnership, Salesforce wants to be the trusted data and secured layer that works with many models. It will partner with AI companies to build its own models.

Specific New Considerations

For Marketing Leaders & Sales Leaders

This lowers your risk of betting on a single AI vendor. You don’t have to wait to pick the “winning” model to get value from Salesforce. And you don’t need to spend a ton of time on model comparisons. Models will keep changing (and improving). Your advantage is your own proprietary customer data, so that’s where the investment belongs.

For Salesforce Admins & Account Engagement End Users

More models means more governance questions: which model is handling which data, and where does that data go? You don’t need all the answers today, but start a simple inventory of every AI tool that already touches your Salesforce and Account Engagement data, including connected apps, browser extensions, and integrations. You’ll likely find more than you expect. UpGuard found that 8 out of 10 employees use unauthorized AI tools at work (UpGuard).  Salesforce is trying to be diligent about what tools get access to your CRM data.

Takeaway 3: What are Salesforce’s AIForce Hunter and Piper agents?

Hunter and Piper are two of Salesforce’s seven new “job-ready” Agentforce agents: Piper engages and qualifies inbound leads from your website and inbox, and Hunter researches prospects and runs outbound outreach.

Salesforce introduced seven prebuilt agents that companies can tailor to how they work, down to giving each one its own name (Salesforce). “Hunter” and “Piper” apply directly to anyone running Account Engagement and Sales Cloud.

Piper is an inbound pipeline agent that engages, qualifies, and converts leads that come in through your website and inbox. It’s generally available now. It takes customers 45 to 60 days to deploy it (on average).  Your current scoring maturity and processes will impact success.

Hunter works outbound pipeline, from research through outreach. It’s in pilot, with general availability planned for November 2026.

Long term costs are not clear.

The other five (Casey, Fin, Paige, Carter, and Marshall) cover customer service, internal IT and HR requests, commerce, and supply chain. No Marketing Cloud–specific agent was announced on stage, and no pricing was shared for any of them.

Specific New Considerations

For Marketing Leaders & Sales Leaders

Piper sits alongside Account Engagement (Pardot) for scoring, grading, and assignment rules. Someone on your team is going to ask whether an agent should qualify leads before sales ever sees them. That’s not a technology decision. It’s a hand-off design decision. Agree on what a qualified lead is, who owns it, and how fast sales has to respond before you let an agent make that call at scale. If you already have strong lead scoring and workflows this could be redundant.  We are not sure of its impact on tokens/pricing.  And remember a broken agent with incomplete data will scale broken process.

For Salesforce Admins & Account Engagement End Users

Document your current lead flow before anyone pilots Piper: scoring thresholds, grading profiles, automation rules, assignment rules, and how an MQL reaches a rep (completion actions, notifications, queues). If an agent enters that flow, you need to know which rule fires first and where a lead could get routed twice. And if sales doesn’t trust your MQLs today, an agent won’t fix that. It will just deliver untrusted leads faster.

Takeaway 4: Is Account Engagement (Pardot) going away? Do I need to Move to Marketing Cloud Next?

No. Salesforce still fully supports Account Engagement, and every Account Engagement customer gets Marketing Cloud features through Account Engagement + with no migration required. So you don’t need to move to test some of the new features.

If you run marketing with Account Engagement on Salesforce, there were not a lot of new updates.  The limited marketing news showed up in the teaser/preview for the Winter ’27 release. This included a Marketing Goals Agent, now in pilot, that turns a goal, a budget, and an audience into a recommended plan (Salesforce Winter 2027 Release Notes). There are new features planned if you are moving from “Marketing Cloud Engagement” aka ExactTarget to “Next”.

There is one quiet change that matters more for most of our Mid-Market and SMB marketing peers. Salesforce reinforced that every Account Engagement customer automatically gets access to Marketing Cloud Next features.

  • Today if you have “Pardot Growth”, you can get Marketing Cloud Next Growth
  • if you have Advanced Edition, you get Marketing Cloud Next Advanced.

To use Marketing Cloud Next you are required to activate Data 360 (was called Data Cloud).  To do this effectively you need clean and complete data. Data360 is included in your current Account Engagement license with a finite amount of data credits. Salesforce is making it easy to test.

And here’s what we heard at the tables. A couple of people were in the middle of setting up Data 360. No one we talked to had moved completely to Marketing Cloud Next yet, they were testing. And those that have moved have been frustrated by Salesforce’s support. Gabriel Sales implemented three instances ourselves. But it’s early, and for most Account Engagement teams, waiting until after the Winter Release announcement is probably the right move.

Specific New Considerations

For Marketing Leaders 

Don’t let the lack of marketing news turn into complacency. Ask a simple question: would unified customer data and AI-driven marketing change our pipeline enough next year to justify a move? If the answer is yes, start preparing your data and your hand-offs now so the move is faster and cheaper when you make it. If the answer is not yet, put that energy into getting more out of the Account Engagement you already pay for. Either way, the preparation work is the same. Better processes and cleaner data will continue to be rewarded.

For Salesforce Admins & Account Engagement End Users

Clean up everything you’d have to migrate anyway: unused custom fields, stale lists, old automation rules, duplicate prospects, and sync errors between Account Engagement and Sales Cloud. That work pays off in Account Engagement today and makes any future move to Marketing Cloud Next faster.

Takeaway 5: How much does Agentforce cost? How Flex Credits and multipliers work?

Salesforce hasn’t announced pricing for AIforce, Claudeforce or the new agents; Agentforce itself runs on Flex Credits at a list price of $500 per 100,000 credits, or about $0.10 per standard action, but the number of credits each task uses (the multiplier) varies by task and can change.

Salesforce has not put a price on AIforce, Claudeforce and the new job-ready agents. It’s simply available to all customers in beta (Salesforce).  Pricing came up at almost every table we sat at, and it was the hardest question for anyone to answer. If you can’t predict the cost, you can’t calculate ROI against what you’re spending today, and you can’t build a budget your CFO will sign.

Here’s why it’s so hard to pin down. Agentforce is priced on consumption through Flex credits and Einstein credits, a prepaid pool of credits that you spend as agents work. The list price is $500 per 100,000 credits, and a standard Agentforce action uses 20 credits, or about $0.10 per action (Salesforce Agentforce pricing). That sounds simple. The catch is the multiplier: how many credits each type of activity uses. In our pilots, we have seen both Units and Einstein Requests consumption. All have different names with various multipliers. Here at Gabriel Sales we are tracking various types of inquiries with our AI tools (both Claudeforce and Coworker) and recording the consumption to help us  understand what the “token” cost is. Good news, after several months of experimenting, we are under 5% of our allotted “test credits” with Foundations.

How to Track Salesforce AI Costs in Digital Wallet

(Watch Time 2 Min)  A quick walkthrough of Salesforce Digital Wallet, where to see the credits your AI tests consume, and how we log each test to forecast Agentforce and Einstein costs before they surprise you.

Here’s one example.  (It’s wild and everything depends on the tool you use. And there are multiple tools you can use to get the same job done.) Salesforce’s own Flex Credits Rate Card sets the multiplier for every type of usage. A standard agent action is 20 credits. The same action through Slackbot is 40. Unifying customer records in Data 360 is 75,000 credits per million rows processed, which works out to roughly $375 per million rows at list price. The rate card also states: “Usage types, tiers, and associated multipliers may be updated from time to time.” And unused credits don’t roll over. They have to be used before your order ends. In other words, the price per action can change without the price per credit changing. We’ve already seen three versions of the rate card this year (February, mid-August, and August 31).

Another challenge and risk we heard at a couple of the tables is some of the flex credits and multipliers are not appearing in the Digital Wallet real time.  There were a couple of teams that experienced a 24-hour lag. In one case a complex developer workflow and overhaul consumed almost an entire monthly budget, and they did not discover this for about 48 hours, resulting in a substantial bill as they continued to work on other projects.  Salesforce did refund.

Claude is another outlier.  We are currently testing both Claudeforce (through the connected MCP) and Agentforce Co-Worker. Sometimes for the same task.  It appears at least right now Claude is more cost effective and has no effect on any Salesforce credits.  We are not optimistic that this will continue.

And the tasks we are running (see below) pale in comparison to the complexity of what Salesforce is promising in the Winter Release 27 for Marketers. We have just started this monitoring so there is not much there yet. We will share what we learn. Stay tuned.

Below is a quick intro video to your Digital Wallet and an example of how we are logging our activity to track for future budgeting.

How to Track Salesforce AI Costs in Digital Wallet

Gabriel Sales’ Salesforce Digital Wallet showing Einstein Requests consumed over 30 days of AI testing: 10,280 requests from 1,028 units at a 10x multiplier.

Screenshot of Gabriel Sales’ Salesforce Digital Wallet showing Einstein Requests consumed over 30 days of AI testing: 10,280 requests from 1,028 units at a 10x multiplier.

Digital Wallet Dashboard Example

Gabriel Sales’ AI usage log recording each Claudeforce and Agentforce Coworker test, the credits it used and the suspected source, to forecast Salesforce AI costs.

Gabriel Sales’ AI usage log recording each Claudeforce and Agentforce Coworker test, the credits it used and the suspected source, to forecast Salesforce AI costs.

Specific New Considerations

For Marketing Leaders & Sales Leaders

Treat AI pricing  as usage-based utility. Your bill depends on how much you use and on rates the vendor can adjust, so get the terms in writing before you commit and/or agree to an upgrade.

The reality we all face is that until Anthropic and/or OpenAI go public costs won’t be clear.  So Salesforce costs with their new “Headless” framework will eventually be like forecasting energy. Fundamentally energy costs and token costs are the same thing.   If you are up for a renewal, ask your Salesforce sales rep four questions:

  1. What is our current rate card, and how much notice do we get before a multiplier changes?
  2. What happens to unused credits?
  3. Is there a cap or price lock for the term?
  4. If we sign an Agentic Enterprise License Agreement (AELA), what does renewal look like?

Start with a small, well-defined pilot so you learn your real consumption before you sign anything large. Set your ROI baseline before the pilot starts, so you can prove what the spend  returned.

For Mid Market and Small Businesses the right use cases are going to make all the difference.  For example if your scoring and hand off is already solid Piper may not make sense from a usage standpoint .  But using Claudeforce for pre-call prep is low cost with high impact output.

Finally calculating ROI once the costs are clear will be easy to justify against sales productivity, conversations “Opportunity” lift and cost reductions. As discussed at the top of this article you need to benchmark now so you can measure against it later.  Gabriel Sales is learning to monitor consumption by tasks so we can help you calculate the front end consumption costs. This will be a big focus for Gabriel Sales throughout 2027.

 

For Salesforce Admins & Account Engagement End Users

You will be the first to see what AI costs, so set up visibility before anyone turns agents on. Decide or find where your org tracks credit consumption (your Digital Wallet, Salesforce’s credit-usage dashboard). Agree on a monthly check-in with your budget leader, and set alerts if your edition supports them. When you build or test agents and Data 360 jobs, keep a simple log of what each one does and roughly how many credits it uses. That log turns a black box into a number your leader can budget against. Be careful with high-volume jobs like data unification and streaming pipelines, which consume credits by the million rows. Clean and de-duplicate your data first, so you’re not paying to process records you should have deleted.

Takeaway 6: Are Most Mid-Market Enterprises and Small Businesses using Agentic AI yet?

Not yet. At two dozen Dreamforce roundtables with 100+ peers, almost no mid-market team was running agents in sales or marketing, and the research agrees: 94% of mid-market companies use generative AI (Kaufman Rossin, 2026), but only 22% of organizations under $1 billion in revenue are scaling AI agents (McKinsey, 2026).

We spent most of our time at Dreamforce in two dozen community and customer roundtables, talking with 100+ peers, on agentic AI and data, and they were the best use of our time at the show. If you go next year, prioritize them.

The top takeaway from those sessions: the Enterprise is running/experimenting with agentic AI, and most mid-market companies are not there yet. Most of the people we talked to were using generative AI for drafting emails, content creation, summarizing calls, and doing research. No one we talked to (outside of the Fortune 500 and a few AI start-ups) was using agents that take action on their own for Sales, with the exception of third party tools like Apollo and Clay for GTM. However, some had made real progress using AI in customer service and support. In casual conversations with 100+ professionals, we heard four trending themes:

  • Admins don’t have the support or the time to get to AI with everything else on their plate.
  • Their data isn’t ready.
  • Almost everyone who felt they were making progress and getting closer to being able to use agentic AI had one thing in common: their CMO and their CRO or CSO were jointly committed to getting the data cleaned up.
  • A handful were pulling together a data governance council, so a business leader was accountable for working with the admin team on data cleanup and process. (More on that in Takeaway 7.)

We did some fact checking when we got back and the research lines up with what we heard.

  • AI use grew among firms with 20 or more employees but didn’t change significantly among firms with fewer than 20 (US.Census Bureau).
  • McKinsey’s 2026 State of AI found that 40% of respondents from organizations with more than $1 billion in revenue report scaling AI agents, up from 27% a year earlier, while the share at smaller organizations stayed flat at 22% (McKinsey).
  • Marketing is further behind than sales and service. Salesforce’s State of Marketing found that only 13% of marketers are currently using Agentic AI across all platforms (Salesforce) While 66% of customer service organizations now use it, up from 39% the year before (Salesforce).
  • Sales sits somewhere in between: 54% of sellers say they’ve used agents (Salesforce). “Used” isn’t the same as deployed at scale, but it tells you some of your sales reps are probably already experimenting.

We also sat in on a handful of Enterprise presentations. Some Enterprise companies were using agentic AI at the very top of the funnel, but most were applying it deeper in the business: insights, supply chain, and customer service, where the data is far richer.

For the Enterprise, the tone shifted from Hype to the Need for ROI proof

The Agentforce keynotes talked more about proof than anything else.  We are assuming that’s because more than 30,000 customers are now live on Agentforce (not clear on how that is calculated and what it is used for, our guess is customer service). Salesforce did introduce an Agentic ROI Playbook and a first look at Agent Optimizer, which finds problems in live agents, suggests fixes, and tests changes before they go out (Cloudgaia keynote recap).

Salesforce repeatedly said humans must stay in the loop. Its Agentic Enterprise Index reported that across its customer base, organizations increased activated agents by nearly 3x in the past year. Once again almost none of this is occurring in Sales and Marketing silos yet.  And most of these Agents are being deployed in the enterprise.

Our hope is that by the time mid-market and small businesses need to fully commit, there’s real clarity on costs and the agents have been honed.

 

Specific New Considerations

For Marketing & Sales Leaders

You’re not behind. You’re on the normal curve for your size. But the Enterprise is setting the expectation that AI has to show a return, not just a pilot, and you can’t prove a return without a baseline. That means clean attribution, a defined marketing-to-sales hand-off, and reports your CFO trusts. Build that baseline now, so that when you do deploy an agent, you can show exactly what it changed. And look at the pattern from our tables: the teams making progress had marketing and sales leadership jointly sponsoring the data work. That sponsorship is the one thing only you can provide.

For Salesforce Admins & Account Engagement End Users

If you don’t have the time, say so, with specifics. Write down what’s on your plate and what AI-readiness work would require, and take it to your leader. That turns “we’re too busy” into a resourcing decision someone can make. When you do start, start where your data is richest, which is usually service cases and Sales Cloud activity, not the top of the funnel. And keep taking the generative AI wins that are available now, like drafting, summarizing, and research, while the agent conversation matures.

Takeaway 7: Why is data readiness the biggest barrier to Salesforce AI?

Because AI is only as good as the data it reads: duplicate, incomplete and disconnected records produce confident wrong answers, and Salesforce said repeatedly at Dreamforce 2026 that a company’s own clean data is its most durable advantage.

Your Data is critically important. Most marketers and sales professionals want to avoid or have never dealt with the technical aspects of data management and data hygiene.  You can no longer sit on the sideline and you need to become involved (or at least fluent) in this less than sexy and fun aspect of sales and marketing. It will become core to your sales and marketing success as you deploy Agentic AI.

To help the Enterprise understand and manage its data architecture Salesforce introduced the Trusted Enterprise AI Harness, (SMBs and Mid-Market get “Foundations”, more about that below). It’s worth understanding this because some form of it will eventually get to smaller companies. The AI Harness packages six data capabilities around (context, agency, action, governance, security, and models). It ties all of your data into one view of the customer that can be used by your Agents. Those include:

Data 360 (formerly Data Cloud, which connects and organizes all your customer data into a single reliable source),

Informatica (data quality and integration)

MuleSoft (connecting systems)

Tableau (analytics),

AI Control Plane that shows every agent running across the company

Salesforce stated over and over again that the “models will continue to change,” so a company’s owned and proprietary customer data and the context it can create for your team and agents will be your most durable and lasting competitive advantage.

Sales and marketing leaders and their teams must take ownership of their Sales Cloud and Account Engagement Data immediately.

From Roundtable on Preparing for AI Success

Dreamforce 2026 Sales Cloud roundtable slide: clean data, clear process, complete visibility and seller focus are the building blocks for trustworthy AI agents, automation at scale and predictable growth.

What this means if you’re not an Enterprise

The AI Harness is built for large Enterprises running many agents across many systems. You don’t need it to get started, and nothing in it is aimed at smaller companies yet.

Salesforce does offer smaller teams a way in. You can start with Salesforce Foundations. It is a no-cost add-on that gives you access to Data 360 and Agentforce, with a starting grant of credits that get used up as you use those features (Salesforce).  You may need Enterprise Edition to access it.  As mentioned earlier, we have been testing programs for several months and still at less than 5% of our Foundations allotment.

The core message and direction Salesforce is going applies at any size:  AI works only as well as the data underneath it. In Salesforce’s own small and mid-sized business research they are acknowledging data is the largest challenge for most companies and they are trying to help them understand and address this issue.

  • 84% of SMB leaders say complete, accurate data is increasingly critical to their success (Salesforce).
  • For SMB marketers, messy data (siloed, low quality, or too much to process) is the number one blocker to getting real value out of AI, and only 27% are completely satisfied with their ability to unify customer data (Salesforce).

In our Gabriel Sales Automation & Sales Enablement Maturity Matrix™, this is the Data Enablement pillar, and it’s the one that decides whether everything else works.  And Gabriel Sales has found the same things to be true across our Account Engagement, Sales Cloud and Data Readiness Health Checks in some areas that are frequently overlooked.

Even Salesforce used a Third Party solution to help clean its data before bringing it into Data 360

We sat in on another data roundtable where Salesforce’s own chief admin and chief data architect walked through the two Data 360 instances Salesforce runs internally. The most interesting thing they shared, very candidly, was that they used Snowflake to clean up their data before they moved it into Data 360. They found Snowflake made it easier to clean the data first and then move it. Once it was ready here are the data lakes they created (see image below).

Salesforce’s two internal Data 360 instances, one for customer data and one for employee data, shown at a Dreamforce 2026 data roundtable.

Salesforce’s two internal Data 360 instances, one for customer data and one for employee data, shown at a Dreamforce 2026 data roundtable.

The lesson for everyone: clean before you connect. Connecting messy data into a new platform just gives you messy data in a more expensive place.

A practical framework for getting your team aligned on data

We also attended a roundtable on how to get a team aligned on data, presented by Mehmet Orun. He walked through two frameworks worth borrowing. The first is a data quality management cycle: profile your data to find patterns and problems, clean and standardize it, enrich it from trusted sources, unify customer records across systems, and monitor it so it stays fit for purpose as your business changes.

Getting started with data governance: a business-domain model with an oversight committee, data domain owners, subject matter experts and data custodians.

Getting started with data governance: a business-domain model with an oversight committee, data domain owners, subject matter experts and data custodians.

The second is a business-domain approach to data governance. A small oversight group approves definitions, standards, and rules. Each business area (marketing, sales, sales ops, service, legal) has a data domain owner who is accountable for that data, subject matter experts and analysts who recommend what needs adjusting, and data custodians, usually admins and IT, who carry out the approved standards. This is the structure the teams from Takeaway 6 were putting in place.
Gabriel Sales’ Salesforce Digital Wallet showing Einstein Requests consumed over 30 days of AI testing: 10,280 requests from 1,028 units at a 10x multiplier.

Mehmet Orun presents Salesforce data quality management cycle: profile, clean and standardize, enrich, unify profiles and monitor, presented at a Dreamforce 2026 roundtable.

Mehmet has also created a great Trailhead trail for anyone just getting started: Explore Data Quality Fundamentals.

Specific New Considerations

For Marketing Leaders & Sales Leaders

Make data readiness a named initiative with an owner and a 12-month horizon, and sponsor it jointly across marketing and sales. That was the common thread among the people making progress. Use the domain model above: put a business leader, not an admin, in charge of each data domain that feeds your funnel, starting with leads, contacts, accounts, and opportunities. Your admin team can’t decide what a “qualified lead” or a “clean account” means. The business has to.

For Salesforce Admins & Account Engagement End Users

Start with the first step of the cycle: profile. Build a baseline of duplicates, gap rates on fields your scoring and routing depend on, stale records, and sync errors between Account Engagement and Sales Cloud. Then bring the governance framework to your leadership and position yourself as the data custodian, not the owner of every domain. And take the Salesforce lesson to heart: fix your data at the source before you migrate or fully trust other Agentic tools.

How Gabriel Sales Can Help You On This Data and AI Readiness Journey

Gabriel Sales can help both Small and Mid-Market companies clean, manage and migrate their data, and activate AI in series of steps.  If you are a new client we can start with a Free Salesforce AI Data Readiness Health Check which also includes a quick de-duplication review/analysis. This is run by Gabriel Sales Co-Founder Carol Springer (a certified Salesforce and Account Engagement expert).  That can be followed by a Data Readiness Audit also run by Carol Springer, and Dean Westervelt, a 25-year data scientist veteran.  We then offer data readiness repair and AI activation  and data governance as a managed service.  Finally, when you are ready we can help you launch your Agents or migrate to Marketing Cloud Next.

Takeaway 8: What are Salesforce products called now?

Sales Cloud is Sales Cloud again, Agentforce Marketing is Marketing Cloud again, and Account Engagement’s official name is still Marketing Cloud Account Engagement (formerly Pardot).

Days before the event, Salesforce reversed part of last year’s Agentforce rebrand. As The Next Web put it, Sales Cloud “became Agentforce Sales last autumn, and is Sales Cloud again” (The Next Web). Marketing followed. Salesforce’s own site now says, “Agentforce Marketing was a name used to describe all of Marketing Cloud’s editions. It is no longer in use, and you will instead see Marketing Cloud” (Salesforce). Account Engagement’s official name is still Marketing Cloud Account Engagement, and Salesforce calls Pardot “the legacy product name” (Salesforce).

Agentforce itself isn’t going anywhere. Salesforce reported Agentforce annual recurring revenue of more than $1.5 billion, up over 240% year over year (Salesforce). Only the labels are moving.

However, the labels matter because they are how you are contracted. You  will want clarity on what you already have.  We check this a part of our Free Health Checks. All these changes to names and features can impact what you pay when you turn things on.

Specific New Considerations

For Marketing Leaders & Sales Leaders

Don’t let name changes drive a decision and get clarity before you recontract. When a vendor or partner pitches you, ask what the product does, what data it needs, and what it costs.

For Salesforce Admins & Account Engagement End Users

Expect Setup, help articles, and Trailhead to lag behind the marketing names. When you search for help, try the old and new names together (Pardot, Account Engagement, Agentforce Marketing, Marketing Cloud). A one-page “what’s called what” cheat sheet for your team will save everyone time.  We will create one soon.

FAQs

What were the biggest Salesforce announcements at Dreamforce 2026? 

The headline was AIforce, which lets teams use Salesforce data inside Claude, Slack and other tools through Claudeforce, Slackforce and Agentforce Coworker. Salesforce also announced seven job-ready Agentforce agents, including Piper for inbound leads and Hunter for outbound, the Koa CRM reasoning model, and the Trusted Enterprise AI Harness.

Is Salesforce Account Engagement (Pardot) going away? 

No. Salesforce continues to fully support Account Engagement, and every Account Engagement customer gets access to Marketing Cloud features through Account Engagement + with no migration required. Move to Marketing Cloud Next when your business case and data readiness support it, not because of a keynote.

What is Claudeforce? 

Claudeforce, also called Salesforce in Claude, connects Anthropic’s Claude to your Salesforce org so users can ask questions and take action on CRM data in plain English. It includes 37 prebuilt sales skills and is available to all Salesforce customers in beta.

What does headless Salesforce mean? 

Headless means Salesforce is separating its data layer from its screens. Your accounts, contacts and opportunities stay in Salesforce, but people can work with that data from Claude, Slack or custom apps instead of logging into the Salesforce interface.

How much does Agentforce cost? 

Currently (Oct 2026) Agentforce uses Flex Credits at a list price of $500 per 100,000 credits, and a standard action uses 20 credits, or about $0.10. Salesforce’s rate card sets different multipliers by usage type and says they may be updated from time to time. Pricing for AIforce, Claudeforce and the job-ready agents has not been announced.

Are mid-market companies using agentic AI? 

Mostly not yet. 94% of mid-market companies use generative AI (Kaufman Rossin, 2026), but McKinsey found only 22% of organizations under $1 billion in revenue are scaling AI agents, compared with 40% of larger organizations. Customer service is the leading agentic use case.

What should mid-market Salesforce teams do after Dreamforce 2026? 

Inventory your licenses and credits, clean up Sales Cloud and Account Engagement data, document lead hand-offs, and record an ROI baseline. Then pilot one low-cost use case, such as AI meeting prep in Claudeforce or Agentforce Coworker, and track the credits it uses.

Where To Go From Here

If you take one thing from this Dreamforce ’26 article, make it this: With all the traction being created by Salesforce and their Enterprise customers, AI is going mature faster than most Mid Market and SMB currently have visibility into. You don’t need to deploy Agents now. Waiting until they are more fully baked will make them easier and more cost effective to deploy at scale. It will also be easier to intelligently budget for their deployment. You do need to start thinking about where your team can capture the most value.

That means don’t wait on your data readiness and early pilots. Start with the right type of system improvements in your existing tools now. Keep improving the automation you already have in Account Engagement. Spend the 3 to 12 months cleaning up and connecting your data, and get your leadership team behind this effort, with a leader or leaders willing to be accountable for data. Then pick one or two specific use cases and test them in Claudeforce and/or Agentforce Coworker, where you can measure the before and after.  Build the data, the team and the ROI process.

If you want a second set of eyes on where your data and automation stand today, start with our free Technology Health Check. It’s a no-cost look at your Salesforce and Account Engagement setup that shows you where you are and what to fix first. You can also see how we’ve helped teams like yours in our case studies.

And finally keep an eye out for additional content.   We will continue to keep you apprised of what we learn as we implement these tools for ourselves and our forward leaning clients.

If you have any questions,  just want to explore something or would like to learn more about our free Health Checks and Maturity Benchmarks complete the form below and we can schedule a call.

Contact Us to Learn More

About the Author 

Carol Springer is a Co-Founder. She has been working at the inflection point of Marketing an Sales Operations implementing an optimizing Salesforce and Marketing Automation for the past 15 years. Carol has multiple Salesforce certifications leads the Denver, Colorado  Salesforce Marketers User Group and is a frequent Salesforce Speaker. Prior to focusing on Sales and Marketing Ops Carol was an award winning enterprise sales executiv

Sources & further reading

How AI Is Changing the Way You Need to Build Emails to Reach the Inbox

How AI Is Changing the Way You Need to Build Emails to Reach the Inbox

by | Jun 9, 2026

A practical look at what and how AI Previews being used by the major mailbox providers have done to impact deliverability.  And what you need to do to make sure your campaigns actually reach the inbox.

Short Summary of What You Will Learn: We wrote this article to help you with email deliverability. We explain the impact of AI Previews and how this impacts how to build emails.  We explain how/why its important to lead with real text and a strong subject line. We recap how to pass SPF, DKIM, and DMARC. And finally we discuss other ways to keep your spam-complaint rate under 0.10% to 0.30% and the benefits of data hygiene and well segmented lists.

We have also summarized the action items from our learnings in a full checklist. You can access this checklist in two ways. You can download a PDF for easy access at any time. Or you can reference online in this supporting blog post Checklist for AI Era Inbox.

Background

We just attended the Salesforce Connections conference in Chicago.  At the conference nearly every session addressed new AI capabilities across the Salesforce and Marketing Cloud family (including Sales Cloud, Account Engagement, Slack and  Marketing Cloud Next). There is a great deal to be excited about especially what will be possible with native AI tools in Marketing Cloud Next.

But what stood out as immediately useful/actionable is this: the major mailbox providers have used AI to quietly change what it takes to consistently land your emails in your target’s inbox. There are three main issues that every team needs to immediately address to maintain and ensure successful delivery:

  • AI previews and how they change the way you need to build an email
  • Authentication and sender reputation
  • List quality

As positive news, even with all the changes occurring with AI , Salesforce data continues to confirm that email marketing and lead nurturing still produces one of the best and most cost effective ROIs for B2B companies. In this short article we will explain what has changed and what needs renewed strategic attention/focus to ensure email deliverability.  We will also explain why it matters so you can make the right strategic decisions for your organization.

AI Previews: Build for the Machine That Reads First

Before a single human sees your email, an AI may have already read it and decided what it’s about. Apple Mail uses Apple Intelligence to write a short summary from the first few hundred words of your email and shows it in the inbox in place of your preheader. As an email marketer you can’t override it or turn it off. Gmail offers Gemini-generated summaries as well. These tools also help decide what’s most important inside the message, which means structure, content hierarchy, images, alt text, and the placement of your key information all matter more than they used to.

One useful detail from the sessions: Apple’s summary is built from your live body text and largely ignores your subject line, your preheader, and any text inside image alt tags. So how you assemble the top of your email now shapes the preview your reader sees before they decide to open. The encouraging part is that the same choices that help the AI also make your email more accessible to screen-reader users and easier for busy people to skim. Five practices make the difference. For more, see Salesforce’s tips for navigating AI email summaries and Litmus’s breakdown of what AI summaries mean for marketers.

1. Put the key message in the first few sentences

Lead with the single most important takeaway, such as the offer, the value, the reason to act, in your opening lines, not after a greeting and a paragraph of warm-up.

Why this matters: Apple Intelligence builds its inbox summary from the first few hundred words of your body copy. If those opening lines are “Hi there, hope you’re having a great week” or housekeeping text, that’s what the AI summarizes and that becomes the preview your subscriber judges before opening. Front-load the substance and the AI has something worth showing.

2. Use real text rather than text embedded in images

Keep your core message in live, selectable text. Don’t bake your headline, offer, or key copy into a graphic.

Why this matters: AI can only summarize text it can actually read it. Text trapped inside an image is invisible to it, just as it is to screen readers and, often, to spam filters. Image-only emails give the summary engine almost nothing to work with, and testing shows summary quality drops sharply for them. Don’t lean on alt text as a safety net, either: AI summaries frequently ignore it. Live HTML text is what carries your message.  Alt text remains critical for accessibility, but today’s AI inbox summaries appear to rely primarily on the visible email content rather than the alt text attached to images.

3. Use a clear preheader

Write a specific, benefit-driven preheader that extends your subject line, however, your subject line needs to stand alone in case some never see your preheader.  Do not use “View this email in your browser” or a string of empty filler.

Why this matters: The preheader is still the preview line in many inboxes and for every recipient who isn’t seeing an AI summary. A clear preheader earns more opens, and where AI summaries do appear, a focused top-of-email keeps your signal consistent. It’s a small field that quietly shapes a first impression.

4. Don’t stuff the top of the email with legal text, navigation, or “view online” links

Move boilerplate nav menus, legal disclaimers, “view in browser” out of the opening. Lead with the message; let the housekeeping sit lower or in the footer.

Why this matters: AI reads top-down and weights what comes first, so if the top of your email is a navigation bar and a disclaimer, that’s what can end up in the summary and that will be reader’s first impression. Clearing the top lets both the AI and your reader get to the point.

5. Use semantic structure: headings, bullets, and concise paragraphs

Organize the email with real headings, short paragraphs, and scannable lists rather than one dense block of copy.

Why this matters: Clear structure helps the AI understand your hierarchy and identify what’s most important, and it helps human readers skim and act. It’s also the foundation of accessibility. Screen-reader users navigate by headings so good structure serves the machine, the assistive technology, and the busy reader all at once.

Authentication and Sender Reputation: Now a Gate, Not a Best Practice

SPF, DKIM, and DMARC used to be the kind of technical hygiene that lived on someone’s “we should get to that” list. Not anymore. In plain terms, these are the records that prove an email actually came from you and wasn’t spoofed by someone else. Gmail, Yahoo, and Microsoft now treat them as baseline requirements. And now for higher-volume senders, mail that fails authentication is rejected, not just filtered to spam. A great campaign that fails authentication simply never arrives.

What these terms actually mean

SPF (Sender Policy Framework): a public list of the mail servers allowed to send email on behalf of your domain, so a receiving server can confirm your message came from an approved source. Google’s SPF overview.

DKIM (DomainKeys Identified Mail): a tamper-evident digital signature added to each message, letting the receiving server verify the email genuinely came from your domain and wasn’t altered in transit. Google’s DKIM overview.

DMARC (Domain-based Message Authentication, Reporting & Conformance): the policy that tells receiving servers what to do with mail that fails SPF or DKIM — deliver, quarantine, or reject — and sends you reports showing who is sending under your domain. Google’s DMARC overview.

Missing or misconfigured authentication is the single most common gap we see, and it’s usually invisible until deliverability quietly erodes. If you haven’t confirmed your domain is passing all three, that’s the first thing to check. See Google’s email sender guidelines and Microsoft’s requirements for high-volume senders (enforcement began May 5, 2025).

For a clear breakdown of the new bulk-sender rules and why this matters, see Validity’s guide.

List Quality Protects Everything Discussed Above

All of the above rests on list quality. Because providers reward engaged senders, consistently mailing inactive or invalid contacts actively drags down deliverability for your entire list — your engaged subscribers included. List hygiene has moved from housekeeping to strategy requirement. Regularly removing dead addresses, suppressing chronically unengaged contacts, and segmenting by real activity isn’t about a tidier database for its own sake; it’s about protecting the inbox placement of the people who do want to hear from you. This is also where clean, well-organized data in your CRM and marketing platform pays off directly. Data governance and data hygiene are becoming more important.

The 60-Second Deliverability Checklist

If you only do four things, do these. The rest of this guide explains the why behind each one.

  1. Confirm your domain passes SPF, DKIM, and DMARC (this is now a hard requirement, not a nice-to-have).
  2. Build every email with real text and a clear, standalone subject line.
  3. Write a deliberate preheader — don’t let the platform invent one.
  4. Mail engaged contacts; suppress and remove the rest on a regular schedule.

For a longer more comprehensive checklist download our PDF here or visit our blog post  Or you can reference online in supporting blog post Checklist for AI Era Inbox.

The Bottom Line

AI is changing how people consume email, but deliverability is still won or lost through authentication, sender reputation, engagement, and list quality.

The encouraging part: most teams already own the tools to do all of this, and it’s usually a matter of operationalizing how you build emails, your authentication and DMARC monitoring, and your list hygiene, rather than buying anything new. If you’d like a second set of eyes on where your email program stands today, that’s exactly the kind of behind-the-scenes work we help our clients put in place at Gabriel Sales. We’re always happy to talk it through and staff the resources to help you if necessary.

About the Author 

Carol Springer is a Co-Founder. She has been working at the inflection point of Marketing an Sales Operations implementing an optimizing Salesforce and Marketing Automation for the past 15 years. Carol has multiple Salesforce certifications leads the Denver, Colorado  Salesforce Marketers User Group and is a frequent Salesforce Speaker. Prior to focusing on Sales and Marketing Ops Carol was an award winning enterprise sales executive. 

Contact Us to Learn More

 

 

Sources & further reading

Guide to Finding Salesforce and Marketing Automation Support

Guide to Finding Salesforce and Marketing Automation Support

by | Jun 5, 2026

This guide is crafted and organized to help B2B business leaders that have invested in Salesforce and Marketing Automation decide if outsourcing Salesforce and marketing automation support makes sense for their organization.  After the introduction the framework is divided into 7 sections. We are also including videos from our Educational Series: Marketing Operations Full Funnel Mandate for deeper dives if additional education is required in any one area.

 

Guide Sections Include (Quick Links):

Introduction

You’ve invested in Salesforce. You’ve invested in Marketing Cloud Account Engagement (Pardot) or you’re considering Marketing Cloud Next, Agentforce and Data Cloud. And somewhere between the promise of what those tools were supposed to deliver and the reality of what your team can consistently manage, optimize and execute on any day (week or month) there’s a gap.  And this gap is costing you pipeline, improved spend and revenue at every stage of your GTM and Customer Funnel.

At the same time, leadership is pushing for more. More leads. More qualified opportunities. More automation post close.  More from the technology you’re already paying for. And now, the question isn’t just about marketing and sales output it’s also about implementing AI. Everyone from the board down is asking when your marketing and sales operations will be AI-ready. The honest answer for most mid-market companies is: not yet, and not without significant work on the data foundation that sits underneath everything.

This is the moment most marketing and sales leaders hit the same inflection point: do we build this capability and capacity in-house, or do we buy it, or is it a combination of the two?

This page is designed to help you answer that question honestly with a balanced framework.  If building in-house is the right answer for your situation we will share those conditions too. But if the math points toward outsourcing, you’ll have the business case to make it to your leadership and internal teams with confidence.

Overview: Marketing’s Shifting Role and the Full Funnel Support Mandate 

(5:00 minutes) A quick summary why marketing roles have transformed more in the past five years than the previous fifty, and what it takes to compete with a fully automated, AI-ready ops model. This is also the introduction to our 12 part video series Marketing Operations Full Funnel Mandate. 

Section 1: What Mature & Successful B2B Marketing and Sales Operations + AI Readiness Actually Requires

Before you can make the build vs. buy decision, you need a clear picture of what you’re actually deciding to build. Most leaders underestimate the scope and volume of all the skills, tasks and technical expertise required for mature and authentically competitive marketing and sales operations. That underestimation is where the gap starts.

This gap is ultimately driven by the changed expectations of the B2B buyer. Buyers now expect to be able to educate themselves digitally online across multiple channels. Once they demonstrate interest, they also expect marketing to continue to support their buyer journey with continued digital education. Increasingly they expect messages and content to be personalized with additional in person or virtual events to support their buying process and education around their specific use cases.  They also expect a seamless hand off from marketing to sales when they are ready to buy.  And more recently they also expect a seamless digital and personalized experience once they become a customer around customer service, adoption, training and personalized marketing if cross sell and upsell campaigns are leveraged.

How & Why Marketing Has Full Funnel Sales Responsibilities

(2 minutes) How and why did marketing become responsible for 70% of the GTM sales cycle

Why Marketing Leaders Are Now Automating Full Customer Lifecycle

(2 minutes) Why will marketing ops need to take on additional responsibilities for the entire customer lifecycle

As a result of these new expectations the B2B buyer has permanently changed the job descriptions and requirements for marketing and sales operations. According to Gartner, 67% of B2B buyers now prefer a rep-free buying experience. This is a number that has increased every time Gartner has measured it and once again it has jumped six percentage points in the past year.  Buyers now only spend only 17% of their total purchasing time in direct contact with potential vendors, meaning 83% of their journey happens before your sales team is ever involved. And according to 6sense, 81% of buyers have already picked a winner before they talk to a sales rep.  AI is predicted to increase this even more.

What this means operationally is that marketing now owns the first and most critical phase of revenue development. Not just lead generation, but sustained digital education, lead nurturing, lead scoring, and the handoff to sales at precisely the right moment. Meanwhile, the average B2B purchase involves 6 to 10 decision makers, each doing their own independent research across multiple channels and content formats.

What’s Now Required for Success

The scope, consistent effort, tech expertise, talent and skills it now takes to support this reality includes:

  • Great Content
  • Personalized Content
  • Marketing Automation Platform Management (Marketing Cloud Account Engagement, Marketing Cloud NEXT, Pardot, Hubspot in some cases for GTM)
  • Salesforce configuration and administration (Sales Cloud)
  • Sales Enablement skills
  • Data integrations
  • Third party SaaS integrations
  • Campaign strategy
  • Campaign execution,
  • Lead Nurturing Workflow Development
  • Lead Scoring
  • Lead Grading 
  • Dashboards
  • Attribution and ROI Measurement
  • Marketing-to-Sales Hand-off Design and Processes
  • Training and Adoption Support
  • Database Management and Data Governance
  • AI data infrastructure (with increasing urgency building and maintaining the data infrastructure required to deploy AI in a trustworthy way)
  • Generative AI
  • Salesforce Native AI
  • Additional Agentic AI

In addition to the list above you also have the increasing native capabilities that you can leverage with Salesforce’s three major platform release cycles per year. Technology is moving fast. And this increased Salesforce firepower can provide a tremendous competitive advantage. But capturing this value (that can set you apart) demands constant attention and expertise just to keep pace to ensure you get all the firepower you have paid for.

This is support is no longer just a role. It requires a team of interconnected disciplines that can collaborate effectively.  Sales and marketing alignment becomes critical because your teams, strategy and tech stack now needs to function as a coordinated system that focus on creating seamless customer experiences.

The decision to build this coordinated system with all the requsite skills sets completely in-house is now also a decision to staff, manage, train, retain and carry the fixed expense of that entire team (or parts of that team) on a sustained basis.

What Type of Support Is Required for Successful Automation and Sales Enablement

(5 minutes) Learn why 70% of companies struggle to find the technical expertise and professional experience needed to maximize their automation and CRM investments and what a fully supported team looks like in practice.

Section 2: The True Cost of Building In-House

Most in-house build decisions are made with incomplete math. The salary line is visible. The recruiting fee is visible. Everything else including the ramp time, the turnover cycle, the burnout risk, the opportunity cost of the months between hiring and productivity is harder to see, which means it rarely makes it into the ROI calculation. Here is an itemized list the more complete calculations. 

The budget pressure is real and documented.

Marketing budgets have fallen from 11.2% of company revenue in 2018 to 7.7% in 2025, representing a 30% reduction in proportional spend. And at the same time output expectations have grown significantly. 64% of CMOs now report they don’t have the budget to hit their stated strategic goals. This is the environment in which you’re trying to build and retain a specialized team. The additional cost and competition for proven marketing automation specialists, Salesforce admins, and data ops professionals was already difficult and is now even more difficult in this do more with less environment.  And the market knows it.

Your team is already stretched and lack of support technical capability and capacity increasingly compounds those challenges.

65.3% of marketers report feeling overwhelmed in 2026, up from 58% just a year earlier. 55.1% have experienced emotional exhaustion in the past twelve months. 83.5% of marketers say they are expected to produce more content without a proportional increase in resources. This isn’t a morale problem it’s a structural one. When the professional responsible for creating great strategies, creating great content and increasingly taking over more and more of the early stage sales functions are already at capacity are also being asked to take on responsibility for implementing, optimizing and managing technology then adding technical complexity doesn’t solve the problem. It accelerates it creating multiple challenges for your existing team.

 What Challenges Do Most Teams Face Without Proven Marketing Automation, Sales Enablement, Data and AI Support

(5:00 minutes) Get an honest look at the tactical barriers that keep most teams from realizing the ROI they were promised, and why adding one more hire rarely solves the underlying structural problem.

The single point of failure problem.

In most mid-market organizations, one person, often a marketing manager or a Salesforce admin (or over time a series of marketing managers or admins) is the single point of accountability, success and failure for everything in the tech stack. When that person is out, on leave, or leaves the company, campaigns stop running, automations break, data quality degrades, and nothing in Salesforce gets updated correctly. This isn’t a personnel issue. It’s a structural fragility built into the lean in-house model at mid-market scale. It is not solved by hiring one more person. It is solved by building redundancy. This requires either a larger in-house team or a different model entirely.

The opportunity cost of ramp time.

Even a strong hire takes time to become productive in your specific Salesforce environment, with your specific data model, your specific campaign history, and your specific integrations. This is typically measured in months, not weeks. During that window, the execution gap you were trying to close is still open. Pipeline is not accelerated, insights are not produced, data hygiene is not addressed and AI deployments are delayed.

Section 3: What Happens When You Underinvest and The Strategic Consequences

The barriers described above are predominantly tactical. They are manageable in isolation. But when they persist, or when the technical execution gap goes unfilled and/or processes are not transitioned methodically, and or frontline end users are not trained the challenges compound and the strategic problems are much harder to solve.

When marketing teams are not fully supported by technically experienced marketing and sales operations professionals the consequences show up at specific points in the revenue funnel. Lead quality drops because lead scoring models are configured once and never tuned so sales gets a flood of low-quality contacts and loses trust in the system. The marketing-to-sales alignment you paid for is only theoretical because the automation connecting both teams isn’t properly configured, which means the handoff is manual, inconsistent, and frustrating for everyone. And your technology investment fragments into silos, Salesforce Sales Cloud is doing one thing, your Marketing Cloud Account Engagement (marketing automation platform) is doing another, with no unified view of the customer. This means every claim you make about marketing’s contribution to revenue is difficult to prove.  It also becomes impossible to extend these tools, automation and even AI deeper into the customer funnel because you lack the earlier stage foundations that you need to build toward later stage funnel success.  And for companies now considering Marketing Cloud Next and Agentforce for truly competitive automation and seamless customer experience  are even more at risk. 

 Strategic Complexities Leaders Create When They Underinvest in Automation, Sales Enablement, Data & AI Support

(3:30 minutes) Learn how underinvestment in marketing ops creates compounding strategic problems from lead quality failures and broken sales handoffs to data conditions that make AI untrustworthy.

Gartner research found that 74% of B2B buyer teams experience a disconnected “unhealthy conflict” during their purchasing decision process which can negatively impact who they short list and/or decide to purchase from. The right solution is to provide personalized communication/education and a a seamless, data-rich handoff from marketing to sales at the right moment in the buyer journey. This requires systems that are properly built and maintained with accurate information shared across both sales and marketing teams so they are working together to service the buyer. Organizations that can deliver that experience win business.  Organizations that can’t lose deals to competitors who can.

The Emerging AI Challenge

And then there is the AI problem, which is different from all the other challenges your team and organization faces.

Every major technology vendor, Salesforce included with Agentforce, (its current AI agent platform) and Marketing Cloud Next (its emerging solution)  for automating marketing, sales, account management and customer service workflows are now delivering AI capabilities directly into the tools you’re already paying for. The competitive advantage available from those tools is real. But the tools only work well when the data feeding them is clean, consistently mapped, and syncing correctly across your entire tech stack. Most mid-market companies’ data is not in that state. Field mapping inconsistencies, duplicate records, broken syncs between marketing automation and Salesforce, incomplete contact records become the norm, not the exception. And they are the precise conditions that make AI outputs unreliable.

The companies that will be positioned to deploy AI effectively in 2026 and 2027 are not the ones buying the newest tools. They are the ones that have maintained strong data governance as an ongoing practice because within the next 18 months everyone will have access to same publicly available data.  In the future the main competitive advantage sales and marketing will have will be driven by the quality and volume of their proprietary data. This means connected clean data and data governance is not a one-time project.  It is a systematic process.  For mid-market companies It is maintenance work that requires sustained attention, specialized expertise, and someone who is accountable for it every week/month.

How Can Leaders Quickly Plan for Success Using an Automation Maturity Model to Address 7 Critical Strategic Question

(3:40 minutes) We use Gabriel Sales’ proprietary automation maturity model to quickly benchmark where your organization stands across sales ops and automation technology, data, and AI readiness. We then provide a clear and prioritized roadmap to help you understand where and how to close the gaps.

Section 4: The 7 Business Advantages of Outsourcing Marketing Automation, Data Governance and Sales Operations

If the analysis in the sections above is familiar and/or if you recognize your organizations gaps in any of the discussion areas then the case for outsourcing deserves a serious look. Here is what outsourcing some or even specific area means for you in business terms.

1. Speed to Competency with No Ramp/Learning Curve

An outsourced marketing operations support team brings tested frameworks, proven campaign structures, and certified platform expertise to your organization on day one. There is no onboarding period during which they are learning how to connect and leverage your tools. There is no learning curve during which they are building confidence with the features and possibilities of your Salesforce systems. The execution gap closes immediately rather than over a period of months.

Companies using marketing automation to nurture leads see a 451% increase in qualified leads compared to organizations that aren’t doing this effectively. That number is not the ceiling. It is what you should expect when your marketing team’s great content, smart strategies and hard work is supported by a team that can leverage your tools properly to enable what well executed automation and now AI can deliver. Most companies have already paid for all the tools they need for success. But not they do not  execution expertise required for the level of performance they were promised. The distance between where you are and where properly executed automation takes you can start to be addressed in weeks rather than months (or even years) when the team doing the technical work, data management and managing the processes already knows how to get all the jobs done. 

2. A Team for the Cost of a Headcount

Outsourcing does not buy you one person’s skill set. It can buy you a blended team based on your gaps: certified Salesforce administrators, marketing automation specialists, data management & data governance practitioners, sales enablement specialists, campaign managers, and strategic oversight each contributing the specific expertise their discipline requires, working as a coordinated unit. In most cases, this blended team costs less than a single senior full-time hire when you account for salary, benefits, recruiting fees, management overhead, and ramp time.

This matters because the scope of modern marketing operations exceeds what any single hire can cover because the task requirement in any single area are part time. When you hire one person to do the work of several disciplines, they will inevitably go deep on the areas they’re strongest in and leave gaps in the others. Outsourcing eliminates the gaps.

3. Proven ROI From a Verified Playbook

The ROI case for well-executed marketing automation is not theoretical. Nucleus Research found a 14.5% increase in sales productivity when marketing automation is properly leveraged for lead scoring and sales enablement. Sales teams using buyer-centric enablement content achieve 28% higher win rates than those working without it. For every dollar invested in marketing automation, companies see an average return of $5.44. And automated email sequences generate 320% more revenue than non-automated campaigns.

These results are available to any organization with the right tools and the right execution capability.

 How Does Achieving Successful Automation + Sales Enablement Impact the Productivity and ROI of Your Sales and Marketing Operations

(7:00 minutes) Learn about the concrete business outcomes that well-executed automation and sales enablement delivers, and understand what changes operationally when your tech stack is fully supported..

4. Variable Cost Structure and Financial Flexibility

Every full-time employee is a fixed cost. Once hired, they represent a budget line that requires justification regardless of whether the workload demands on their full capacity in any given month. Outsourcing converts that fixed cost into a flexible monthly engagement. You scale up during a product launch, a major campaign push, or a Salesforce implementation. You scale back during a slower period. This flexibility is structurally unavailable when you’re managing in-house headcount, and it matters especially for organizations already operating under tightened budgets.

5. Continuity, Resilience, and No Knowledge Loss

One of the most underappreciated costs of the lean in-house model is the knowledge that walks out the door every time someone leaves. When the person who knows how your Salesforce instance is configured, how your lead scoring model works, and why certain automation rules and workflows were built the way they were decides to move on, that institutional knowledge is gone. The next person starts typically from scratch, often without even knowing what they don’t know.

An outsourced team maintains that continuity as a structural feature of the model. The knowledge lives in documented processes, in systems, and in a team, not in a single person’s head. There is no single point of failure, no campaign emergency when someone is out, and no organizational reset when someone leaves.

6. Cross-Industry Best Practices and Cross-Pollination Means You Start Ahead

A team that has executed hundreds of implementations across multiple industries and verticals brings an understanding and a library of what works and what doesn’t. They have seen the campaigns that didn’t convert and understand why. They have seen the lead scoring models that created sales friction and know how to calibrate them differently. They have seen the data governance failures that made AI deployments untrustworthy and built the frameworks and connected the required fields to prevent them.

Most organizations that are building their marketing operations in-house are starting from scratch and experimenting, iterating, and learning at their own cost. Outsourcing eliminates the trial-and-error phase.  Your strategists can implement and innovate faster. You get to start where a mature team’s portfolio already landed, not where they started.

7. AI and Data Readiness as an Ongoing Practice

This is the advantage that is least discussed and most consequential in 2026 and 2027.

The marketing automation market reached $6.65 billion in 2024 and is projected to more than double to $15.58 billion by 2030, driven in large part by AI integration directly into the platforms you’re already using. The competitive pressure to deploy AI in your marketing and sales operations is real, and it is accelerating.

But the bottleneck is not the AI tools themselves. The bottleneck is the data foundation beneath them. AI outputs are only as trustworthy as the data flowing into the systems generating them. Duplicate records, broken field mappings, inconsistent data entry, unresolved sync errors between your marketing automation platform & Salesforce are just some of the conditions that make AI unreliable. Most of these issues are maintenance problems. They regenerate continuously without a governance framework and data hygiene managers and/or automation specialists  in place to prevent them.

After a one time clean up an outsourced marketing automation specialist and/or marketing operations teams maintains that data foundation as a continuous practice as part of their ongoing operational discipline. For organizations being asked by leadership to get AI-ready, this is often the fastest and most reliable path. 

Section 5: A Decision Framework: When Outsourcing Is the Right Call and When You Should Build In House

Outsourcing is likely the stronger strategic decision when:

  • You are just getting started implementing Salesforce and/or Marketing Cloud Account Engagement. Especially if you are migrating from an existing platform like Hubspot where and outsourced partner can help you seamlessly migrate what was already working and build the new enhancement faster with best practices.
  • You have Salesforce and marketing automation in place but aren’t seeing consistent, measurable ROI from the investment. The tools are there but the execution infrastructure to run them effectively isn’t. Or the person running them is stretched too thin to run them well.
  • You are ready to migrate from Marketing Cloud Account Engagement (Pardot) to Marketing Cloud Next to leverage Salesforce’s native AI solutions.
  • Your team’s workload has expanded to the point where strategic and creative work is being consistently crowded out by operational and technical tasks. If your best marketing people are spending significant time troubleshooting integrations, managing data quality issues, or trying to figure out why an automation isn’t firing correctly, you have a structural problem that adding one more hire won’t solve.
  • You need technical depth across multiple disciplines, Salesforce administration, marketing automation management, data governance, sales enablement configuration and AI readiness where it doesn’t make practical or business sense to hire full-time resources to plug specific gaps.
  • You’re under pressure to scale marketing output without proportionally scaling headcount.
  • You’re being asked to deploy AI and recognize that your data isn’t ready for it. The path from “our data is a mess” to “our AI is producing trustworthy outputs” runs directly through the data governance and system configuration work that outsourced operations teams implement and manage as a core competency.
  • You are concerned with compliance surrounding HIPPA and GDPR.

 

Building in-house may make more sense when:

  • You’re at enterprise scale where a fully-staffed internal marketing operations team is the industry standard, your budget supports it, and the competitive complexity of your organization genuinely requires deep institutional knowledge that an outside team would struggle to replicate.
  • Your marketing operations work involves highly proprietary processes, sensitive data environments, or regulatory requirements that make external access to your systems genuinely difficult to manage.
  • You have the time, budget, and infrastructure to recruit, train, and retain senior talent across multiple specialized disciplines simultaneously and your leadership has realistic expectations about how long that build takes.
  • If you have an industry leading brand or a compelling mission you should be able to attract certified ops talent that would have been inaccessible five years ago.
  • If you have a company where marketing ops and or sales ops capability is also you core product offering or you are a company where go-to-market sophistication is the product differentiator you should consider building in house.

Section 6: The Questions Worth Answering Before You Decide

If you’re at an inflection point where you need more capability and capacity and have decided that mature automation, sales enablement and a strong AI foundation are all critical here are the questions that will give you the clearest picture of which direction to go and what you should keep in house vs. outsource to a proven resource.  It is possible to run hybrids based on your needs, goals, specific gaps and situation.

 

  • What is our current fully-loaded cost to maintain marketing operations in-house including salary, benefits, recruiting fees, management time, and the cost of turnover when it happens?

  • Who is currently managing and executing the tasks required to meet and exceed or goals? Is this their core competency?  Are we distracting them from higher priority and/or higher revenue producing tasks?  Is this causing a retention issue for top talent?

  • What specific technical expertise are we missing right now, and what would it realistically cost to hire for it? Do we even understand what expertise we need?

  • How long would a new hire take to be meaningfully productive in our specific Salesforce environment? And what is the cost, in pipeline/insights/AI readiness, of that ramp period?

  • Are we actively using the Salesforce and marketing automation features we’re already paying for? If the answer is no, what’s preventing adoption? Is adding headcount the right solution to that problem? Is it training? Or is it a different kind of support?

  • Is our data clean enough and connected enough for AI deployment in the next 12 to 18 months? If we ran an honest audit of our Salesforce and marketing automation data quality today, would the results support the AI roadmap we’re communicating to leadership? How much pressure would data readiness relieve for our team?

  • What would our existing team accomplish if they weren’t carrying the operational and technical execution load? If we could give our strategists and creative people back the hours they’re spending on tech management, troubleshooting, feature education, what would they do with that time. What would that time and focus be worth?

    Section 7: Recommended Next Steps if the Math and Potential ROI Points Toward Outsourcing

    If this analysis points toward outsourcing and the question shifts from if to who, when and what Gabriel Sales can help.

    We can quickly help get you started answering the “who” and “what” with our free technology health check that is followed by our risk-free audits and tactical road maps.

    We make getting started easy because we know most leaders are already overwhelmed.  And we are confident that we can quickly build trust with measurable results.  Because after 15 plus years of helping companies build automated sales enablement and marketing engines we understand that the difference between for most organizations moving from mediocre to successful results is working with a partner that can provide the required certified technical expertise, sales and marketing operations experience and the proven track record collaborating with teams with support customized to align with your specific situation.

    We are not a generalist digital agency that also does Salesforce. We are not a solo consultant who becomes your single point of failure by a different name. We are fully managed team with certified expertise across the full stack and a proven methodology for getting organizations from where they are to where they need to be.  We love what we do and always lead with full transparency because when you win we win and that’s the easiest way to sell.

    See how we’ve done this for companies like yours →

    Learn what makes Gabriel Sales different →  

    More about us and FAQs →  

     Why Gabriel Sales Is the Right Solution for Companies Serious About Marketing Automation and Sales Enablement Success

    (8 minutes)  Learn how Gabriel Sales’ blended team model delivers certified expertise across the full stack from day one to make the complex simple, all with minimal ramp time, risk, or fixed cost of building in-house.

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    How Leaders Plan for Success Using an Automation Maturity Model (Educational Video)

    How Leaders Plan for Success Using an Automation Maturity Model (Educational Video)

    by | Apr 27, 2026

     This video explains how leaders plan for success using an Automation Maturity Model.to navigate the complexity of using modern sales and marketing technology, data and AI. 8th video in our Full Funnel Mandate Series. 

    (Watch Time – 3:40)

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    Summary

    This video explains how leaders plan for success using an Automation Maturity Model.to navigate the complexity of using modern sales and marketing technology, data and AI.  

    The framework helps leaders understand five stages of competitive maturity by aligning sales and marketing with- technology and tactical optimizations, data enablement and AI readiness, and human capital management. By applying this systematic approach, leaders can benchmark their current capabilities, create ROI roadmaps, and address seven key strategic questions to effectively deploy automation, sales enablement, data, and AI technologies.

    Transcript

    How can leaders quickly plan for success using an automation maturity model to address seven critical strategic questions?

    Navigating the Marketing and Sales Automation Inflection Point

    Marketing leaders are at an inflection point where there is both so much opportunity and so much risk to achieve the ROI possible at every stage of the customer funnel. But most leaders didn’t get into sales and marketing to manage tech stacks, data, and AI. So when they take a step back and examine all the integrated technologies, tactics, data, and applications required to automate functions across the entire sales funnel, crafting a strategy, and investment plan to reach mature automation, productive sales enablement, and AI data readiness can seem overwhelming unless they apply a proven and systematic framework like Gabriel Sales automation and sales enablement maturity matrix to help them plan for both short term ROI while also building the foundation for sustained results.

    A Framework for Competitive Maturity and Aligned Success

    Because this type of framework helps both marketing and sales leaders understand, collaborate, and address the five stages of competitive maturity and all three of the critical pillars required for aligned success, including technology use in tactical optimizations for automation, sales enablement, and CRM tools, data enablement, including hygiene, governance, and AI readiness, insights, analysis, and dashboards, and management of human capital, including training, sponsorship, culture, and existing resource allocation. With this type of planning and innovation tool, sales and marketing leaders can benchmark their capabilities, capacity, and maturity level and craft an ROI road map with both short term targets and long term objectives.

    7 Key Strategic and Operational Questions

    Because with targets and a road map in place, marketing leaders can work with sales to quickly address seven key strategic and operational questions to cost effectively and successfully use automation, sales enablement, data, and AI technologies for exceptional sales and customer experiences. All anchored in achieving more with less by asking:

    How can we cost effectively and systematically increase my team’s automation and CRM capability and capacity?

    How do we reduce my organization’s wasted time, ongoing frustration, and struggles with the consistent use, optimization, and daily management of technology?

    How can we deploy and refocus my team’s talents, energy, and expertise on our top priorities of strategy, analysis, innovations, content production, and improved execution instead of distracting them with repetitive tasks, data management, and technology troubleshooting?

    How do we Improve our organization’s ability to maximize the performance and ROI of specific campaigns and measure their impact to improve marketing budgets and performance?

    How do we align and bridge marketing and sales to drive greater technology adoption, collaboration, and sales productivity?

    How can we leverage existing data to make smarter decisions and enhance AI driven

    How do we lay the foundation for more mature and advanced automation, preparing the organization to both strategically and tactically manage the full customer life cycle?

     

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    The Support Required for Automation and Sales Enablement Success (Educational Video)

    The Support Required for Automation and Sales Enablement Success (Educational Video)

    by | Apr 24, 2026

    This video (Part 5 in our Full Funnel Mandate Video Series) discusses the marketing support required for Automation and Sales Ennoblement success.

    (Watch Time – 5:30)

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    Summary

    What are the different types of support required for Automation and Sales Enablement Success for both short term results and long term foundational success?

    This video (Part 5 in our Full Funnel Mandate Video Series) discusses the critical need for specialized marketing automation and sales enablement support for B2B organizations. It explains why organizations struggle to find the marketing support required for Automation and Sales Enablement success. We detail the capability and capacity technical expertise needed to maximize their CRM and automation technology investments for great sales and customer experiences.

    Transcript

    What Support Does Your Marketing Team Need for Automation and Sales Enablement?

    Building, reaching, and sustaining successful automation and sales enablement to create great sales and customer experiences can seem overwhelming because it requires integrating multiple moving parts in the right order at the right time, including optimizing rapidly evolving core technologies and adding the appropriate AI, leveraging experienced marketing and sales professionals for great strategies, content, and campaigns, marketing and sales ops expertise for consistent execution, data governance and data management across multiple systems, and a culture committed to marketing and sales alignment.

    Where Do Most Teams Stand Today?

    So where do most teams actually stand today, and what’s holding them back? We’ll discuss what most teams already have in place for success, why finding automation and sales enablement support is so tricky, and what proven and tactical automation and sales enablement support looks like, how providing the right support helps your team achieve more with less, and finally, why not providing support overwhelms your team to both diminish and impact their results.

    What Most Teams Already Have in Place

    Let’s start by looking at how most teams are currently situated to achieve mature marketing automation and sales enablement. Today, most marketing teams have the internal talent or the existing agency partners they need to manage brands, craft multiple strategies, buy and run media campaigns, create great content, execute events, run social, pay per click, and SEO, and write great copy. So organizations already have the creative and strategic talent they need to fuel both go to market campaigns and automated customer campaigns.

    The Critical Skills Gap

    But because of the rapid adoption and evolution of CRM and automation technologies, one of the top challenges for most organizations is finding proven marketing automation and sales ops talent that know how to fully maximize your tech stack investments. Seventy percent of B2B companies and executives currently struggle to find this critical skill set because the technology itself is so new. So the market hasn’t had enough time to develop the volume of talent and experience required.

    Because transitioning from struggling with tech to effective automation support for your team requires blending multiple skills, including both CRM and automation technology expertise with best practice marketing and sales process knowledge and frontline hands on technology workflow experience.

    How the Right Support Drives Exponential Results

    And when you provide your existing team with this full support, they can produce exponential results, and you actually achieve more with less. They can focus on their core competencies of strategy, creative, and campaign performance as your automation support team reduces all the friction of daily and consistent execution across both sets of funnels. And this support knowledge base is even more critical if you are serious about building mature automation because automation expertise allows your team to maximize the more advanced tech stack features now available, including real time dashboards, reporting, and attributions for smarter decisions, predictive lead scoring, and sales enablement features for increased sales productivity, and all the data governance and unified data tools, which are now required for more effective AI deployments, personalization, and advanced automations to transform today’s capabilities into long term advantages.

    Deploying AI Confidently Within Salesforce

    So now this level of support is even more important for organizations that want to confidently and safely deploy AI solutions directly within Salesforce for mature automation and sales enablement. Because when it comes to AI, your CRM is only as powerful as what flows into it. This support will ensure a strong data foundation. They help your team with the daily management of clean, connected, and enriched data across your tech stack, so your AI outputs are accurate today and your automated workflows are trustworthy as you scale.

    Reducing Costs and Building Custom Solutions

    This support also reduces cost by consolidating SaaS subscriptions and replacing third party tools with new AI capabilities built natively inside and on top of Salesforce. This specialized support also helps you build custom solutions that were previously cost prohibitive in Salesforce, giving your managers and leadership team the ability to automate business and data processes without expensive outside platforms.

    Positioning Your Team for the Future with Agentic AI

    And with that data foundation and this level of support in place, you’re positioned to deploy Agentic AI for advanced automations, real time alerts, and hyper personalization, driving competitive sales advantages and genuinely exceptional customer experiences across your funnels.

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    What Do B2B Buyers Need and Expect from Marketing and Sales Ops (Educational Video)

    What Do B2B Buyers Need and Expect from Marketing and Sales Ops (Educational Video)

    by | Apr 22, 2026

    In part 3 of our video series “Marketing Ops New Full Funnel Support Mandate” discuss what B2B Buyers need and expect from marketing and sales ops to support and automate their buyers journey and their customer journey. We also discuss the need for a seamless hand offs to sales, customer service and account managers when Buyers are ready.

    (Watch Time – 4:45)

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    Summary

    What do B2B Buyers need and expect from Marketing and Sales Ops? Today’s B2B buyers want marketing teams to lead their buying process. When they are ready to buy they expect a seamless hand off to sales and post-sale they also expect digital education and even more personalized content during their adoption and customer journey. Organizations that embrace full-funnel automation are seeing stronger engagement, loyalty, retention and increased revenue as a result.

    Transcript

    What Do B2B Buyers Now Need and Expect?

    So what do buyers now need and expect from your marketing and sales operations for an automated buyer’s journey and connected customer experience?

    Top of Funnel: Building Awareness and Trust

    The digital first buyer’s needs and expectations start at the very top of the funnel. To build awareness and trust, B2B buyers now expect multiple types and formats of content delivered in up to 5 channels. Most buyers now require between 10 to 21 or more touches before they’ll convert as an inbound lead.

    The Long Buyer’s Journey

    Now most inbound leads are not ready to immediately meet with sales. Most buyers now start their journey at least 3 to 12 months before they’re ready to buy. 54% won’t transact for over a year. 80% want to avoid your sales team until they’ve completed their research and are ready to shortlist. And 40% would prefer to avoid sales completely if possible

    Middle of Funnel: Nurturing and Education

    All these desires dramatically impact the middle of the funnel. Because according to Merkel, buyers now expect marketers and not sales reps to manage the first 65% of the buyer’s journey with digital education and lead nurturing campaigns. And according to Gartner, buyers now expect educational content and campaigns to support their purchase decision at every stage of the funnel, including awareness, consideration, and preference. And moving forward, buyers are also increasingly requiring that these campaigns and content are personalized for both campaign engagement and serious consideration of your solution, which dramatically increases your marketing team’s workload. Because today, the average B2B purchase decision includes 6 to 10 decision makers and influencers.

    Bottom of Funnel: The Marketing-to-Sales Handoff

    Next, at the bottom of the sales funnel, buyers now expect a seamless handoff from marketing to sales when they’re ready to buy, expecting sales to be enabled with prior engagement history and additional third party data. They don’t want it to feel overly invasive, but according to a survey of buyers, 74% of leads that have opted into GDPR and cookies expect sales reps to immediately add value enabled with a basic understanding of their company’s fundamentals in prior content, events, and email engagement. And marketing teams that provide their sales team these tools increase their win rates by 28%.

    Post-Sale: Automation and the Customer Experience

    And after these increased wins, buyers that become customers now expect automation and continued personalized digital engagement because B2B customers now state that a disconnected customer experience after the sale is now their number one frustration.

    The same leading marketers that built mature sales automation are now extending automation to build digital customer funnels across multiple processes to meet these new expectations with profitable results. The 25% of companies that automate onboarding and training experience a 92% increase in adoption and an 88% increase in loyalty. Marketing teams that use it for continued education and event marketing with personalization see a 65% increase in advocacy. And the 20% of mature companies that are doing both are experiencing a 4x increase in retention. And finally, mature teams that are also deploying their tech systems for personalized cross sell and upsell see an increase in campaign engagement and a 60% increase in purchases per customer.

    Summary: Marketing’s Expanding Role

    So to summarize, marketing’s function and role is now permanently extended and much more complex because of this combination of B2B buyers and now customers’ digital first habits and desires for frictionless automated experiences with a seamless handoff to sales and customer service. Combined with the multiple financial rewards experienced when marketing supports full funnel automation is why Forrester’s senior analysts believe that for sales and marketing organizations to survive and thrive, B2B marketing leaders are now required to continue to quickly transform their organizations from lead generation and brand awareness with increased capability and capacity to support sales with continuous digital engagement across the entire sales funnel and customer life cycle, executing multiple processes, and increased use and optimization of automation technologies.

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