Your board approved the budget. The vendor delivered a compelling demo. The pilot launched with executive sponsorship and internal fanfare. And somewhere between that kickoff call and today, the results quietly stopped matching the narrative.

MIT Sloan's Davenport and Bean made a pointed prediction for 2026: agentic AI is heading into the Gartner Trough of Disillusionment, the same place generative AI landed just twelve months ago. You've watched this cycle before. The difference this time is the speed at which failed deployments compound, and the size of the budgets attached to them.

The window to course-correct is right now, before Q2 budget reviews lock in another round of scaling decisions on a shaky foundation.

Why Agents Fail Quietly

Agents don't crash visibly. They produce plausible-looking outputs that are subtly wrong, at a velocity that outpaces human review, and by the time an error surfaces, it has been replicated across hundreds of transactions.

Publicis Sapient's 2026 Guide to Next, based on research with over 500 industry leaders, captured this precisely: organizations are failing at AI not because their models are flawed, but because the data feeding them is inconsistent, fragmented, and ungoverned, what the report calls "decision debt," where confidence outpaces capability and assumptions scale before systems are ready. Not a technology problem. A systems readiness problem dressed up as an adoption success story.

Three Root Causes That Keep Showing Up

Data isn’t agent-ready: Agents operating on poorly governed data don't reason better than humans , they make mistakes at scale. As Guy Elliott, Publicis Sapient's Industry Lead for EMEA and APAC, put it: "Confidence without measurement is belief, not certainty."
Automated workflows are not understood: There's a meaningful difference between a workflow that exists and one mapped with enough precision for an agent to navigate reliably. Agents require explicit decision logic, not tribal knowledge that disappears when someone leaves the team.
No Clear Success Metrics: Most organizations have no clear definition of success for their agent deployments. Without it, teams default to measuring activity, how many tasks ran, how many tickets closed, instead of impact. The metrics that actually matter are decision quality, cycle time compression, and human capacity freed for higher-order judgment. If you're not tracking those, you're not managing a deployment, you're counting outputs and hoping for the best.

What Good Looks Like

The organizations pulling ahead aren't the ones who deployed first. A major bank with IT service management didn't start with agents in complex workflows but started with a narrow knowledge-search application to prove data readiness and build trust before expanding autonomy. The enterprise context, data, workflow logic, governance boundaries, was built before the agent, not alongside it.

Before You Scale, Check This First

If you have active agent investments or budget allocated for 2026, run against these questions before committing to scale. A "no" here is far cheaper than a failed rollout at enterprise scale.

On your data: Is the data your agent reasons over governed, labeled, and accessible without manual intervention? If a human would struggle to find and trust it, an agent will too — just faster and at higher volume.
On your workflows: Have the workflows being automated been explicitly mapped; not assumed? If your documentation lives in a PowerPoint from three years ago, you don't have workflow documentation.
On your people: Do employees working alongside agents understand what the agent can and cannot do? Human-in-the-loop isn't a compliance checkbox, it's what keeps agent errors from becoming business incidents.
On your metrics: Are you measuring decision quality and business outcomes, or just task volume? Without a baseline, you're not managing a deployment, you're managing a narrative about one.
On accountability: Is there a named owner at the business unit level responsible for agent outputs? When something goes wrong at scale, "the model did it" is not an acceptable answer to your board.

If you can't answer yes to most of these, you don't have an AI problem. You have a systems problem that AI is about to make visible expensively.

Strategic Move to Make By Q2 ‘26

It's a reason to be precise about where you are before you scale. The enterprises that emerge ahead of their competitors will be the ones who used this window, Q1 and early Q2 2026, to audit their foundations rather than accelerate past them.

The competitive gap between AI leaders and laggards will widen significantly in the second half of this year. The leaders won't be defined by who deployed the most agents but by whose agents actually held up. The models are ready enough. The question is whether your organization is.