Scaling AI requires investment in workforce readiness
This thought leadership for emerging enterprise ($1.1 billion to $5 billion) organizations is part of a three-article feature examining the intricacies of AI adoption for organizations in different revenue bands, using data from our AI Impact Survey. The other articles explore AI for midmarket ($100 million to $1 billion) and large (more than $5 billion) organizations.
Executive summary
Emerging enterprises have built a strong foundation for AI adoption through governance, operational integration and board oversight. However, workforce readiness is emerging as the key barrier to scaling AI. As organizations embed AI into daily operations, demand for process, data, security and change management skills is outpacing talent development. By redefining roles, training employees within real workflows and strengthening management capabilities, emerging enterprise organizations can close workforce gaps and turn early AI success into sustained competitive advantage.
Emerging enterprises have moved faster on AI than many larger or smaller peers. They are more likely to have strong board oversight and deeper operational integration. As AI enters day-to-day operations, workforce capability is becoming the pressure point.
In Grant Thornton’s 2026 AI Impact Survey, 46% of operations leaders at emerging enterprises (firms with $1.1 billion to $5 billion in annual revenue) identified workforce skill gaps as a primary obstacle to AI initiatives. That compares with 29% of midmarket ($100 million to $1 billion) leaders.
The workforce data on AI adoption tells a consistent story for emerging enterprise firms:
| $100 million to $1 billion | $1.1 billion to $5 billion | More than $5 billion | |
|---|---|---|---|
| Primary AI obstacle: Workforce skill gaps-x | 29% | 46% | N/A |
| Biggest barriers preventing AI from scaling: Talent or upskilling gaps | 35% | 39% | 30% |
| Need external talent and upskilling support to scale enterprise AI | 23% | 23% | 13% |
x: Denotes data collected from operations function leaders only; the more than $5 billion range was statistically insignificant for this question
Emerging enterprises are the most likely segment to view workforce readiness, not technology, as the barrier to scaling AI.
Demand for process, data, security and change management skills is rising faster than many organizations can develop them. Emerging enterprises are more likely than larger organizations to see talent and upskilling gaps as barriers to scaling AI. They are also just as likely as smaller midmarket firms to need external support to build those capabilities.
Put simply, the workforce doesn't yet have the depth to match the organization’s ambition.
At the same time, emerging enterprises often lack the capital and brand recognition to compete for talent at the same scale as the largest organizations. As a result, scaling AI cannot depend on hiring alone. The firms making progress are redesigning how work gets done as AI moves into day-to-day operations. They are defining where AI fits, building governance early and training people where the work actually happens.
1:19 | Transcript (PDF - 217.93KB)
AI is redefining the work your people do
AI has changed what all organizations need from their workforce. At emerging enterprises, this is even more acute because AI adoption is already moving into operational workflows.
Before AI, most human work went toward building, maintaining and delivering goods and services. Relatively little time was dedicated to scoping which business problems to solve and how.
Now that AI and agents handle more routine and repeatable tasks, people can spend more time scoping problems and determining how to apply AI to solve them. That raises demand for people who understand both operations and how AI can be applied within them.
“Invest in people who understand how operations and the front office work in your industry,” said Sumeet Mahajan, Grant Thornton AI & Data Partner. “Then train them to define what AI means for the value chain in which they sit. Once AI moves into day-to-day work, those teams become critical to scaling it consistently across the business.” This approach is key to competitive differentiation.
Emerging enterprises are working through this shift under more intense competitive pressure than their counterparts:
| $100 million to $1 billion | $1.1 billion to $5 billion | More than $5 billion | |
|---|---|---|---|
| Competitor moves are driving AI adoption | 34% | 43% | 37% |
That competitive pressure, combined with workforce challenges, creates conditions for reactive AI adoption. Even under that pressure, emerging enterprises are outperforming many peers on AI governance and operational readiness.
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Governance is already producing returns
At emerging enterprises, governance-led AI adoption is already starting to pay off:
| $100 million to $1 billion | $1.1 billion to $5 billion | More than $5 billion | |
|---|---|---|---|
| Boards are involved in AI strategy development oversight | 52% | 60% | 46% |
| Extremely prepared to handle AI-enhanced privacy and security challenges | 29% | 63% | 48% |
At 60%, emerging enterprise boards are more likely than those at larger or smaller organizations to be involved in AI strategy development oversight. Additionally, 63% of emerging enterprise leaders say they are extremely prepared to handle AI-enhanced privacy and security challenges.
Board engagement and a strong security and privacy foundation give emerging enterprise organizations the confidence and capability to scale AI.
That governance maturity is translating into operational progress. Emerging enterprises are the most likely revenue group to say AI is fully integrated into operations and their core applications are mostly AI-ready.
That integration is producing measurable outcomes that their larger and smaller counterparts aren’t matching — emerging enterprise organizations are the most likely revenue group to say AI is accelerating innovation and driving new revenue.
| $100 million to $1 billion | $1.1 billion to $5 billion | More than $5 billion | |
|---|---|---|---|
| AI is fully integrated in enterprise operations | 8% | 22% | 19% |
| More than 50% of core applications are AI-ready | 29% | 61% | 42% |
| Experiencing AI-enabled innovation acceleration | 28% | 36% | 29% |
| Experiencing AI-enabled revenue growth | 30% | 48% | 43% |
What workforce readiness looks like operationally
Over the past year, many of the organizations working to scale AI have encountered the same operational questions: how to distribute AI responsibility across teams, where managers need more oversight and how to embed AI into day-to-day work without creating fragmentation.
“AI needs to become a capability you have instead of just a technology you use,” said Grant Thornton Transformation Partner Jennifer Morelli. “To make AI a capability, leadership must fundamentally rethink how teams, managers and processes operate, embedding AI into the core of how work gets done rather than layering it on top of existing ways of working.”
The next phase is building the organizational capability to scale AI consistently across the business. That means closing the gap between AI ambition and workforce readiness. Here’s how you can do it:
- Define repeatable AI responsibilities across the organization: Don’t concentrate AI knowledge within a small group of leaders. Build roles and responsibilities that distribute AI capability across functions.
- Give teams time to find where AI solves problems: Before training people on tools, give them space to connect the dots between AI and their day-to-day work.
- Train people using real operational work: Generic AI training creates awareness. Training tied to actual workflows creates capability. Show people how to apply AI tools to their individual jobs.
- Use partners to bridge capability gaps: Advisory and outsourcing partners can help establish operational standards, support implementation and fill capability gaps while internal teams mature.
- Prepare your managers to lead AI-enabled work: Managers are becoming the link between AI strategy and day-to-day execution. They need to guide changing workflows, oversee how AI is used and keep teams aligned as responsibilities shift.
- Use AI as a recruitment advantage: The best candidates want to use AI tools to produce better quality work more efficiently. Show them how they can do it at your organization.
Emerging enterprises have already done the hard work of building governance-led AI adoption. The data shows it's paying off.
Workforce readiness is the next dimension of the AI proof gap. Closing it positions organizations to turn early AI momentum into sustained operational advantage.
For more information on how organizations of all sizes can turn practical AI into measurable business results, visit the AI services page.
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