Picture a familiar enablement problem: last quarter’s pricing deck is still sitting in a shared folder. One seller finds it and sends the wrong figure to a prospect. Bad day.
Now connect an AI agent to that folder. The same deck can shape call briefs, follow-up emails, deal rooms, and answers across dozens of deals before anyone spots the pattern.
That is the shift revenue enablement teams need to plan for. Reps are no longer the only users of their content. AI agents use the same competitive intelligence, pricing, messaging, and process guidance to complete work on a rep’s behalf.
The quality of that work depends less on how impressive the model sounds than on the knowledge it can access. If the source is stale, contradictory, or unapproved, the agent can carry the problem into every workflow it touches.
Gartner predicts that AI agents will outnumber human sellers ten to one by 2028, yet fewer than 40% of sellers will report a productivity improvement. In a separate forecast, Gartner said more than 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls.
Adding more agents is not enough. Revenue teams need a governed source of GTM knowledge that people and agents can use with confidence.
Here is what that foundation should do.
1. Govern the knowledge, not just the file
A content portal can tell you where a deck lives. It may not tell an agent whether the pricing on slide 14 is still valid.
Useful GTM knowledge needs context around it: an owner, a source, an approval state, appropriate permissions, and a review date. That context lets the system distinguish an approved claim from an old draft that happens to match the search.
The unit of governance matters, too. If the same positioning statement appears in a battlecard, onboarding course, coaching prompt, and buyer-facing page, maintaining four copies creates four opportunities for drift. A modular source lets the team update the claim once and reuse it wherever it belongs.
This is the difference between storing content and running knowledge as infrastructure. Reps can see where an answer came from. Agents can retrieve the right version. Enablement can find stale material before it reaches a buyer.
2. Use AI to reduce the maintenance burden
Every pricing change, product release, and competitive move creates a trail of updates. Manual maintenance rarely keeps pace because enablement teams have to find every affected asset before they can fix it.
AI can help with that work. It can identify duplicate or conflicting material, draft updates, classify content, and surface items that need review. What it should not do is quietly replace human ownership. The team still decides what is true, what is approved, and who can use it.
The practical test is what happens after the first draft. When a source changes, can the platform show which assets and workflows depend on it? Can an owner review the change before it spreads? Once approved, does the correction reach both the rep experience and the agents using that knowledge?
3. Apply brand controls while content is being created
Agents are producing more of the material buyers see: emails, executive summaries, decks, one-pagers, and deal-room pages. A brand review at the end of every workflow will not scale with that volume.
Approved messaging, templates, design rules, and reusable components need to guide the work as it is created. That does not eliminate human review for high-stakes material. It gives routine work a safe starting point and makes review the exception instead of the only control.
Ask a rep and an agent to create the same buyer asset from the same approved knowledge. Both versions should sound like your company, use the right claims, and follow the same visual standards.
4. Carry one source of context from coaching to buyer execution
A rep should not hear one message from a coaching tool, receive a different answer from an assistant, and then send a third version to the buyer.
Coaching, rep guidance, agent-generated content, and buyer experiences need to draw from the same governed source. Otherwise, every handoff becomes another copy someone has to reconcile later.
During an evaluation, take one approved message and follow it through the system. You should be able to trace it from the source to a coaching recommendation, a rep answer, an agent-generated follow-up, and a buyer-facing asset. If the trail disappears at any point, the team has lost control of that version.
5. Connect governed knowledge to agents through MCP
Model Context Protocol (MCP) is an open standard for connecting AI applications to external systems. For enablement teams, it offers a practical way to make GTM knowledge available in the AI tools their company chooses.
MCP is the connection, not the governance model. A server still needs to enforce identity and permissions, return the right source context, and limit actions to what that user or agent is allowed to do. Simply giving an agent another search endpoint or content export adds material without telling it what to trust.
Test the connection live. Ask the same question through the rep experience and a connected agent. Then correct a stale source and ask again. Change a permission and repeat the test. Both users should receive the updated answer, while the restricted content stays restricted.
A practical evaluation sequence
The five capabilities are easier to assess as one connected workflow than as five separate feature demos:
- Choose a real competitive or pricing claim and identify its owner, source, approval status, and review date.
- Ask the same question in the rep experience and through a connected AI agent.
- Update the source, approve the change, and confirm that both answers change without rebuilding every asset by hand.
- Restrict access and verify that the rep and agent both respect the new permission.
- Create a buyer-facing asset and check its facts, message, design, and source trail.
A polished answer in a staged demo is easy. This sequence shows whether the platform can keep that answer reliable after your business changes.
Spekit: AI-first revenue enablement
For teams enabling both reps and their AI agents, Spekit® is an AI-first revenue enablement platform that unifies AI-powered content creation, content management, learning, governance, coaching, digital sales rooms, and analytics. AI Sidekick, the rep’s AI sales coach, delivers Enablement in the Flow of Work® through approved content, contextual coaching, deal intelligence, and next-best actions inside tools reps already use, including Salesforce, Gong, Slack, Gmail, Claude and every other web app.
Spekit's MCP grounds any LLM or your own AI agent in the same accurate, governed knowledge engine that powers Spekit, so every email, message, and piece of content it produces reflects real, approved information.
Spekit launched GTM Knowledge Engine 2.0 in June 2026 to give revenue teams that governed foundation. It organizes GTM knowledge into modular, reusable units with ownership, permissions, governance, and source context built in.
AI Content Builder helps teams create and maintain battlecards, playbooks, decks, and deal-specific assets from approved knowledge. Brand Studio applies company standards during creation. Spekit MCP connects that governed source to supported tools including ChatGPT, Claude, Copilot, Glean, Gemini, and custom agents.
Reps still get answers and guidance where they work. Their agents can draw from the same source instead of a separate set of files. When enablement corrects the source, the update can reach both users without creating another maintenance job.
Best for: High-growth, mid-market, and enterprise B2B revenue teams that want governed GTM knowledge for people and agents in the flow of work, grounded in a governed GTM Knowledge Engine.
At your next renewal or platform evaluation, do not settle for a content search and an AI-generated summary. Ask the provider to show what the answer is based on, who controls it, where it is used, and what happens when it changes. That is the test of whether the platform is ready for both users.
