FutureScape 2027: Three Leadership Decisions That Are Still Open. Here’s How to Use the Time.

If your AI budget grew tomorrow, where would you invest first?

Most leaders answer with a platform or a vendor. The IDC FutureScape 2027 webinar argued that the better answer is in people and governance, and that the decision stays open only for a limited time. IDC’s Rick Villars described the open window as the area where leaders “can’t solve everything this year, but making decisions and commitments now will allow you to be ready to really capitalize on this AI first transition in 2029.”

This is the second of three posts on the windows that frame IDC FutureScape 2027: The Great Rewrite. The launch blog introduced the Top 10 predictions, and the closing window post covered the cost, model, and software-factory decisions that are nearly made. Here we turn to leadership. The target sets the pace: 60% of enterprises will run a majority of their business processes AI-first by 2029, up from 13% in 2026. IDC defines AI-first as agents becoming the default starting point for how work gets done, with humans assisting and providing oversight, which is why the governance and skills decisions below carry so much weight. Webinar viewers can revisit the replay.

The talent bill: what does skipping the people investment cost?

Villars started with a cost that never appears on a hardware invoice. When leaders feel they are not getting value from their AI investment, he said, “It’s much more about the people, about the talent, the decisions that your people make about what models to use.”

The FutureScape prediction puts a number on it. Enterprises that have not invested in AI talent development and governance by 2029 will see 50% lower revenue growth than peers who do. The price tag arrives before the deadline: IDC predicts that organizations delaying AI talent investment until after 2027 will face a talent acquisition premium of two to three times, because demand outstrips supply across industries.

The same report adds a measured comparison. In IDC’s Worldwide Future of Work Survey (May 2026, n = 1,146), organizations with no return on AI reported increased revenue at half the rate of those earning the highest returns, 18.5% versus 37.0%. This is a survey comparison between groups, and the 50% figure above remains a prediction. Enterprises with a mature AI center of excellence are four times more likely to grant AI autonomy on complex work, 63% versus 15%.

Jobs are shifting while this plays out. By 2029, G2000 enterprises will move 30% of entry- and midlevel execution roles toward supervising and orchestrating agents. IDC expects enterprises to reskill existing workers into those roles, because a shrinking and aging talent pool leaves too few new hires to backfill them.

Audit before you commit.

The IDC guidance for the first six months is that the CIO and CHRO charter an AI center of excellence with written decision rights, a named owner for compute and token spend, and audit standards built into the technology stack before agent deployment scales further. Between six and 18 months, the center of excellence writes a learning plan into every agent deployment, with a skills target and its own budget line. Longer term, finance and HR model the revenue and talent-retention risk against the claimed savings before any headcount cut tied to agent deployment is final.

Owners: the CIO and CHRO lead, and the CFO models the risk.

Measure: the share of agent deployments that carry a funded learning plan with a skills target, plus the modeled risk set against claimed savings for each headcount cut tied to agent deployment.

The role: what does a knowledge architect do?

IDC defines knowledge architecture as the work of setting the standards, practices, and exception rules that govern what an enterprise’s AI agents know. Daniel Saroff, group vice president of research and consulting at IDC, took the next question. An enterprise architect designs how systems fit together and sets the technology standards. A knowledge architect keeps that systems thinking and adds a different job: defining “what agents know,” which means the standards, practices, and exception rules agents work within. Those rules set what good looks like and which practices are mandatory, and they decide how edge cases resolve and where the boundaries sit.

Saroff framed the shift as a change in the core question. The enterprise architect asks how systems connect. The knowledge architect asks “what does good look like and what happens at the edge?” The output changes with the question, from reference diagrams to “governed knowledge foundations.”

IDC predicts that by 2030 the traditional enterprise architect role will have transformed into a knowledge architecture role that lets AI agents evolve 50% faster. Saroff’s reasoning was brief. “Agents learn from a well governed foundation,” he said. “And without one, each deployment … repeats the last mistake and is inefficient.”

The starting point is uneven. IDC’s 2026 AI MaturityScape Benchmark Survey of 1,900 organizations in 20 markets found that only 12.8% of enterprises have reached the two most advanced stages of AI maturity, while more than 61% remain in the two least mature.

In IDC’s view, the role asks for a mix of skills. Saroff described it as part architect, part editor, and part diplomat. Business fluency matters because the person has to know how work gets done and how the business ranks its priorities. Knowledge structuring turns what individuals know implicitly into explicit rules that guide agents. Regulatory literacy means working with the risk organization to see how rules apply to agents. And because the role carries little formal authority, persuasion and demonstrated value are part of the job description, a condition Saroff noted enterprise architects have always faced.

Inventory before you stand up the role.

IDC advises that within 12 to 18 months you name an owner and write down the standards, practices, and exception rules your production agents rely on, flagging every place none exist. The function itself follows in the 18 to 36 month horizon.

Owners: the CIO or CTO names the owner, enterprise architecture leads the inventory, and a risk partner reviews it.

Measure: the share of production agents whose standards and exception rules are written down.

The proof: why are CIOs pulling forward-deployed engineers back?

Villars asked Saroff whether the knowledge architect connects to forward-deployed engineering teams, the vendor-supplied engineers who embed inside a customer to build. Saroff answered with a conversation from two weeks earlier, when he met with 20 CIOs at a forum and asked how they use these teams.

Several told him they had used the teams and were now pulling them back. In Saroff’s telling, the CIOs still saw the value, since end users and the business both love them. The pressure came from expense, which he traced to a gap the knowledge architect addresses: “How do you create the guardrails and rules around the agents?” The same gap covers getting data to agents effectively and holding the deployed engineers to the enterprise’s standards.

Without that role, Saroff said, IDC sees forward-deployed teams “less likely to meet ROI goals, less successful in the delivery, and lower end user satisfaction.” That is an early signal from one forum, and the pilot below is how you test it against your own engagements.

Pilot before you scale.

Pair one forward-deployed engagement with a named knowledge owner, and compare its ROI against its stated goals and its end-user satisfaction with an engagement that has none. IDC also suggests putting a question to every AI platform and tooling vendor: how do their agents consume, and get governed by, your own knowledge standards? Hold the answer against a standing evaluation criterion.

Owners: the CIO and the business sponsor lead the pilot, and the CFO tracks return against the original goals.

Measure: ROI against stated goals and end-user satisfaction, per engagement.

Before the window narrows: what should tech leaders do first?

Each beat above carries a gated first move. In sequence:

Audit your AI talent and governance position before you commit budget. Charter the center of excellence, name the owner of compute and token spend, and put the funded learning plan into every agent deployment.


Inventory the knowledge your production agents rely on before you formalize the knowledge architecture role. Name an owner and flag the gaps.


Pilot a knowledge owner on one forward-deployed engagement before you scale the model. Measure return and satisfaction against a comparison engagement.

The Top 10 report frames the stakes in Villars’s words: “Organizations that delay governance, talent, and knowledge architecture investments through 2027 will find that the cost of catching up rises faster than any budget adjustment can close.”

What signals should you watch as the window stays open?

Talent pricing. If AI talent costs climb toward the two-to-three-times premium IDC expects after 2027, your window is shortening.


The maturity benchmark. Movement in the 12.8% of enterprises at advanced AI maturity, and in the more than 61% still foundationally stuck, shows whether the leader and laggard gap is widening.


Peer behavior. Watch whether other CIOs scale back forward-deployed teams or report that ROI goals fell short, and what they say the cause was.


Vendor answers. Track how platform and tooling vendors respond when asked how their agents follow your knowledge standards.

Review these quarterly. Bring your baseline for each measure to your IDC account team, and ask to be connected with the analyst who covers your area.

Where can you go deeper?

The research behind every prediction in this post is in the FutureScape reports at idc.com/futurescape. IDC Quanta brings those reports together with hundreds of other IDC analyst reports, so you can ask about the prediction that caught your attention and see the evidence behind it.

To hear the leadership discussion again, watch the webinar replay.