US businesses are scaling AI quickly, but the harder work starts after deployment. As adoption, efficiency gains, and data readiness rise, the next challenge is closing the post-adoption gap: making AI fit the business, keeping costs predictable as usage scales, and maintaining control as AI takes on more responsibility.
We’ve just completed the second wave of Infor’s Enterprise AI Adoption Index, our global survey of more than 2,100 business decision-makers across seven countries. For the 550 US business decision-makers in wave 2, the direction is clear: AI adoption is accelerating.
The findings below reflect respondents’ reported experiences and perceptions of AI adoption in the market and should not be interpreted as outcomes achieved through the use of Infor products.
Since the April wave of the index:
- Full-scale AI deployment rose from 21% to 34%.
- Reported efficiency gains increased from 28% to 35%.
- 74% now say their data is mature and well-governed enough to support reliable AI, up 12 points since April.
Those numbers point to a clear shift in the conversation. For US businesses, the challenge moves from simply adopting AI, to making it work reliably at scale.
That is the post-adoption gap US businesses now need to close.
The real challenges start after deployment.
Confidence in managing AI implementation internally has risen nine points since April.
At the same time, US respondents estimate that 49% of AI-generated insights and workflows still require expert review or adjustment.
Businesses may be growing more confident in deploying AI, but that doesn’t mean humans can step away fully, or even know when they need to be involved and when they can fully hand off decisions to AI systems.
Nearly half of AI-generated insights and workflows in the US still require expert review or adjustment.
- Rick Rider, SVP of AI Innovation
AI that works has to understand the business it’s built for. Many organizations find that general-purpose AI solutions may not fully address their industry-specific needs.
The first challenge is fit.
68% of US respondents say off-the-shelf AI does not adequately address challenges specific to their business or industry.
As US deployment expands, that becomes harder to ignore. A generic system may be technically capable while still lacking the context to understand how a specific factory, distributor, retailer, or other enterprise operates.
The result can be more customization, more workarounds, and more human intervention. That’s why industry context needs to be part of the foundation, not layered on after the fact.
As AI gains authority, governance has to keep pace.
As AI gains authority, keeping control becomes more important:
- 54% of leaders globally are mostly or very comfortable with autonomous agents fully executing critical business processes without human input at every step.
- 49% prefer AI to act as an equal partner or lead in high-stakes decisions such as financial forecasting and workforce planning.
Comfort with greater AI autonomy is rising, too: 15% of US respondents are now very comfortable with autonomous agents handling many processes, up from 10% in April.
Together, the findings show that businesses are giving AI more authority even as human oversight remains essential.
That growing authority is colliding with unresolved governance concerns: 33% of leaders globally cite data security, sovereignty & privacy, or compliance among the greatest barriers to advancing their AI strategy.
If an agent makes a high-stakes decision, organizations need to be able to trace what happened, understand the “why,” and know where human review or approval belongs.
Governance has to be built into the system. If it’s added after the problem surfaces, it’s already too late.
Why it matters: US businesses still face a substantial review burden, demand for predictable costs, and persistent security and compliance concerns
The US findings illustrate true momentum:
- Deployment is up.
- Efficiency gains are up.
- Confidence in data readiness is up.
But those gains do not close the post-adoption gap on their own.
The lesson is that progress in one area doesn’t remove the work required in another. Stronger data readiness doesn’t eliminate the need for governance, and higher efficiency gains don’t eliminate the need for human review.
The next phase of AI adoption is about turning that progress into lasting value.
What US businesses should do next.
For leaders looking to close the post-adoption gap, three questions stand out:
The goal is a truly agentic enterprise, where AI agents and people work as one coordinated system under human oversight.
How Infor helps close the post-adoption gap and moves you closer to being an agentic enterprise
Infor Velocity Suite is designed to help businesses close the post-adoption gap.
Velocity Suite brings industry-specific AI directly into existing operational processes. Industry context is built in from the ground up, while connected applications, data, workflows, and agents are designed to help reduce the fragmentation that can create added work and cost.
Industry AI Agents operate within defined workflows while actions remain explainable, traceable, and auditable, helping reduce manual oversight without removing human accountability.
For US businesses scaling quickly, closing the gap means turning adoption into durable value without adding unnecessary complexity or losing control. The real test is whether AI becomes easier to rely on as it becomes a more routine part of how work gets done.
To see how we’re helping US businesses close the gap and adopt market-specific solutions, visit our customer stories at infor.com/customer-stories, or get in touch with our US team.
Find out how the US compares globally. Download the full Infor Enterprise AI Adoption Index for the complete findings across seven markets.