Agentic GTM, explained: What changes when sales agents run the workflow

Agentic GTM has evolved from a niche jargon term to a named category in a very short time. For revenue leaders, it poses a problem.

The term now shapes budgets, roadmaps, and vendor pitches. Yet, it's so new that few can accurately say what separates an agent from the artificial intelligence features already built into the workflow. This matters because buyers have already become more interested in and reliant on these tools.

Many businesses now report using generative AI somewhere in the purchasing process, with an increasing number naming AI engines as providing more information than vendor websites or sales reps. As more buyers research and decide to use AI, the go-to-market motion needs to meet them there.

Apollo has assembled the following guide using data from Forrester, Deloitte, McFadyen Digital, Salesforce, McKinsey, Reuters, and more to define agentic go-to-market and differentiate it from other AI features. The guide also highlights what real adoption looks like and outlines where agents are already producing revenue.

Defining the term: Agentic AI vs. plain AI features

Agentic AI is a phrase that eludes a single definition, so the following four categories have been outlined to clarify any misunderstandings.

1. What it is NOT

First and foremost, agentic AI is not the same as the AI features many teams are already running. Auto-drafted emails, lead scoring, CRM logging, and chatbot replies are solutions that speed up a single task while humans still drive the work. They are useful, but not agentic. According to a report by Gartner, the gap between the label of agentic AI and true capability is merely rebranding, a practice known as "agent washing."

Agent washing refers to calling AI assistants, chatbots, and robotic automation "agents," despite the fact that they aren't.

2. What it IS

Agentic AI is an autonomous system that perceives its environment, reasons towards an overarching goal, and executes multistep work across tools without step-by-step guidance from a human being. Forrester describes GTM agents as software that simply learns how to reason, act, and collaborate on its own, just like a seasoned business professional. The distinction between what it is and isn't comes down to full autonomy, plus execution.

3. The GTM distinction

From a GTM standpoint, the gap between true agentic AI and fake agents matters. General chatbots and copilots will answer questions, but GTM agents will actually own outcomes. A true AI sales agent won't just produce a potential lead when you ask, but it will research the account, draft outreach, update the CRM, schedule a meeting, and only follow up with a human when pre-determined judgment is required. Procurement agents on the buy side do the mirror image of this.

4. Work out of the app, into the model

The deepest shift in the market is architectural. For around two decades, software value lived within whichever interface a human interacted with. For example, a rep would log into the CRM and do all the work. Agentic GTM agents move that work away from the app itself and into the model. The agent becomes the operator, in a way, and the human just sets the goals and guardrails for it.

The adoption reality check

Only a few vendors are seemingly using true agentic AI at this point. Deloitte published hard numbers.

In a February 2026 study of 1,060 B2B suppliers and buyers in the U.S., Deloitte found that 45% of suppliers use AI in sales, but only 24% have actually deployed the true agentic and autonomous kind. Buyers are slightly further ahead with 61% using it to some degree in purchasing and 38% utilizing the agentic type. Even fewer in both groups actually utilize full agents.

Executives constantly overestimate how far their organizations have come with AI, simply due to how new the technology is. Every integration of it, no matter how minor, feels groundbreaking.

When people state they use AI, they are typically referring to assistive tools rather than automatized workflows. Deloitte found, however, that digitally mature suppliers were five times more likely to use AI extensively and to use AI generatively at all. These organizations also exceeded annual sales growth targets by 110% more than their counterparts.

The gap in adoption is where performance separation is starting to open up. The reason some lag behind, though, isn't necessarily their fault. Deloitte cites budget pressure, specifically related to large-scale enterprise resource planning (ERP) modernization and limited IT capacity, as the main practical issue. AI agents require clean and connected data, as well as access to numerous tools. The technology isn't cheap to implement, and many businesses simply don't have the capital for a major overhaul.

Where it works: Revenue evidence from early movers

For those businesses that do have the budget and are capable of integrating agentic AI into their workflows, the results are hard to argue with. In Salesforce's 2026 State of Sales research, 83% of sales teams using AI reported revenue growth in the past year against 66% of teams that don't. This is a 17-point gap that is only set to compound as agentic workflows mature.

McKinsey's November 2025 analysis titled Agents for Growtheven put a number on the ceiling: Effective and scaled AI deployment can lift productivity by 3%-5% annually and growth by 10% or more. McKinsey also notes that agentic AI could even unlock $2.6 trillion to $4.4 trillion in annual value, with as much as 20% of that productivity lift being concentrated in marketing and sales.

The catch, though, is that nearly 8 in 10 organizations report no significant bottom-line gains from AI so far. The cause is cited as being due to fragmented pilot launches, weak data to pull from, and thin governance. This means that investing in the technology now will produce marginal gains for the near-term, but potentially major gains in the long run.

Success will ultimately depend on the experience customers have with agentic AI. Buyers who get fast, accurate, and relevant help will convert and buy more. This is true whether or not it's a human or an AI agent behind the screen. It's why digitally mature suppliers also exceed their target goals by the largest margins. The revenue isn't coming from the AI agent itself, but rather the better buying experience that the agent makes possible at scale.

The trajectory: What the next 24 months look like

The trajectory of agentic AI in the near term is fascinating. Gartner projects that over 40% of existing agentic AI projects will be killed by the end of 2027 due to rising costs and poor implementations.

Despite this dire forecast, they are projecting that 33% of enterprise software applications will still include agentic AI by 2028, up from less than 1% in 2024, which is estimated to drive more than $450 billion in revenue.

The same forecast sees at least 15% of day-to-day work decisions being made autonomously by agents in 2028 as well. This is up from essentially zero in 2024.

On the buying side of things, Gartner's headline projection is bolder yet. They estimate that 90% of B2B buying could be handled by AI agents by 2028, potentially moving trillions of dollars through agent-to-agent exchanges.

This isn't a seller-only story either. Forrester named zero-click buying as the top B2B shift for 2026. Buyers increasingly get what they need inside AI answer engines and never have to click through to a vendor, making the entire purchasing experience smooth. This results in a discipline shift from search engine optimization to answer engine optimization.

From an architecture standpoint, all of these benefits reward the plumbing rather than the polish. Agents can only act on structured and connected data when they have governed tool access. The best way to implement this is to develop human-AI teams that share data products where agents are treated as managed talent.

Another prediction from Forrester outlines that an estimated $10 billion in enterprise value will be erased through bad outputs with AI and the fallout that follows, and the last thing your business wants is negative press. The best way to avoid this is to only pursue agents where there is a clear and measurable outcome, rather than buying into hype.

Three actions for revenue leaders now

There are three moves that will separate leaders from experimenters of agentic AI in the coming years.

First, treat AI as a GTM architecture decision, rather than a technology choice. Map out the workflow before evaluating vendors. The question shouldn't be focused on which agent to purchase, but rather where in your business model a system should be making decisions and what it needs to do so safely.

Second, all businesses need to fix their foundation before scaling. Ensure that all data is both cleaned and connected, so that agents can be given the right tools to work with. Each agent should also be treated like managed talent, with a defined objective and constant oversight. Start with the cases that will produce a clear return on investment, then scale from there.

Finally, move more into the adoption window. The jump from under 1% of enterprise software utilizing AI in 2024 to a projected 33% in 2028 is the competitive window, but it's already halfway shut in 2026. Focus on implementing the answer engines that buyers currently trust, perfect the buying experience, and develop one high-value workflow rather than waiting for an entire finished platform. The best organizations will compound small and well-governed wins now to set the stage for when agentic agents become the norm.

This story was produced by Apollo and reviewed and distributed by Stacker.

Copyright 2026 Stacker Media, LLC

This story was originally published July 7, 2026 at 8:00 AM.