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Enterprise AI value depends on more than buying capable technology: employees need the skills and latitude to use it, while hiring and workforce plans must support the company’s AI strategy. Fortune’s October 6, 2026 account of an AIQ Summit points to large differences in upskilling and AI-talent planning between companies identified as AI “Pacesetters” and others. Those comparisons are suggestive, not proof that people practices alone cause better financial returns.
What the reported numbers say about AI readiness
Fortune reports figures from ServiceNow’s Enterprise AI Maturity Index comparing companies it calls AI “Pacesetters” with companies outside that top group. The differences reported are substantial:
| Practice | AI Pacesetters | Companies outside the top group |
|---|---|---|
| Invest in employee AI upskilling | 57% | 4% |
| Have plans to attract, hire, and retain AI talent | 68% | 10% |
These percentages are attributed to ServiceNow’s index as reported by Fortune journalist Jeremy Kahn on October 6, 2026. Fortune’s account does not state the index’s sample size, field dates, or full methodology. The figures describe differences between groups; they do not establish that training or recruitment caused a particular level of AI adoption, productivity, or financial return.
Why technology does not create value on its own
A capable AI system can still fail to change how work gets done if employees do not know how to use it, have no time or support to practice, or cannot adapt their processes. At the summit, Amy Webb argued that insufficient training helps explain why some companies struggle to realize enterprise-wide returns, describing a pattern of organizational “learned helplessness.” That is Webb’s interpretation, not a causal finding demonstrated by the index figures.
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Webb’s broader point was that flexibility can be built. “It’s not about being AI native,” she said. “It’s about being flexible, and it is entirely possible for any company to be more flexible, but they have to put together the mechanisms to effectuate that.” In practice, this means creating pathways for employees to learn, try AI in relevant work, and revise habits and workflows when the technology changes what is possible.
What people-centered AI readiness involves
Training tied to actual work
Upskilling is more useful when it helps employees apply AI to tasks they perform, rather than treating training as a one-time introduction to a tool. Workers need opportunities to practice, ask questions, and understand where human judgment remains necessary. Training investment is one of the index differences Fortune reports, but the article does not specify what programs the companies used or how their effectiveness was assessed.
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Hiring for adaptability and problem-solving
Danielle Gonzalez, chief people officer at Palo Alto Networks, said the company uses “observable interviews” and hackathons to see candidates solve problems. She described valuing agency, rapid learning, and the ability to reconsider assumptions: “We’re looking for agency, and we’re looking for those people who can not only learn something at a fast rate…but also unlearn what they thought to be true, so that they can make space to move forward.” These are examples of hiring approaches, not evidence that any single interview format guarantees better AI outcomes.
HR plans that match the AI strategy
Fortune says Pacesetters also stand out for long-term HR strategies that support AI strategy and comprehensive assessments of organizational AI skills. The article gives no percentages for those practices. Their relevance is practical: leaders need to understand what skills the organization has, what capabilities are missing, and how roles and development plans may need to change as AI becomes part of work.
Shared responsibility for learning
Drew Holler, chief human resources officer at Lennar, described a balance between employer support and employee initiative: “We’re going to give you all the tools, but it’s your responsibility to upskill yourself as well,” he said. “We’re going to give you trainings, but you, as an individual, have to upskill.” The employer’s role is to make useful tools and training available; employees also need to engage with them and build relevant skills.
How to assess whether an organization is ready
The summit report suggests several questions leaders can ask. They are useful indicators to examine, not a standardized scorecard or a guarantee of return:
- Does the company invest in employee AI upskilling, and can staff apply that learning to real workflows?
- Are there plans to attract, hire, and retain AI talent that reflect the organization’s needs?
- Does HR have a long-term strategy aligned with the company’s AI strategy?
- Does the organization assess its workforce’s AI skills and identify gaps?
- Do hiring and development practices look for adaptability, agency, and practical problem-solving?
- Are risk and security leaders involved in AI strategy in a way that addresses real risks without reflexively rejecting new use cases?
These questions connect the technology decision to the organizational conditions needed for adoption. They do not replace evaluating the AI system itself: reliability, security, fit for the task, and cost still matter. The point is that technical capability and workforce readiness are complementary, not competing, parts of realizing value.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence does—and does not—show
Fortune’s report of the ServiceNow index offers descriptive comparisons between Pacesetters and other companies, alongside speakers’ perspectives on training, adaptability, hiring, and HR planning. It does not provide a quantified analysis of financial returns, independently rank companies across all these practices, or demonstrate that one workforce intervention causes stronger results. The most supportable conclusion is narrower: companies Fortune identifies as Pacesetters report more emphasis on upskilling and AI-talent plans, while summit speakers argue that people and organizational flexibility are important to putting AI to work.
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Fortune’s October 6, 2026 report is the source for the index figures and summit remarks.
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