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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThe widest gap between AI “pacesetters” and other organizations in Fortune’s October 2, 2026 report was their ability to attract, hire, and retain AI talent: 68% of pacesetters reported doing so, compared with 10% of other organizations. Ongoing AI upskilling showed a similarly sharp divide—57% versus 4%. The figures come from the ServiceNow Enterprise AI Maturity Index as reported by Fortune; they show an association, not proof that training alone creates better AI results.
What is the widest gap between AI leaders and laggards?
In Fortune’s report on comments by ServiceNow director of futures Diana David at the Fortune AIQ Summit, the largest single gap in the cited talent practices was attracting, hiring, and retaining AI talent: 68% of “pacesetters” versus 10% of other organizations. The gap in ongoing AI upskilling was also pronounced: 57% versus 4%. David summarized the talent difference as: “Talent investment. That was the biggest gap,” Fortune reported.
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| Practice or measure | Pacesetters | Other organizations |
|---|---|---|
| Attract, hire, and retain AI talent | 68% | 10% |
| Invest in ongoing AI upskilling | 57% | 4% |
| Have a path to unified data | 64% | 14% |
| Have a clear, strong AI vision | 57% | 21% |
These are findings attributed to the ServiceNow Enterprise AI Maturity Index and reported by Fortune, not a controlled comparison showing that any one practice caused an organization to become a pacesetter. The source material does not establish the index’s full sample design or the precise definition of “pacesetter,” so the percentages should not be treated as universal rates for all employers.
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Some organizations report investing in ongoing upskilling, but the cited figures suggest it is far from routine: 57% of pacesetters versus 4% of other organizations said they invest in it. At the same time, Fortune reported that 42% of employees say they are not receiving enough AI training, while 59% of organizations lack long-term HR plans for AI. Those workforce-readiness figures are attributed to the 2026 index in Fortune’s coverage and ServiceNow’s 2026 index announcement.
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Tool access and workforce readiness are different things. A generic introduction to an AI tool may help employees try it, but role-specific instruction is more directly connected to the work they must perform: what tasks are appropriate, how to check outputs, when to escalate, and how AI changes handoffs or accountability. David’s recommendation, as reported by Fortune, is to make training specific to AI and job functions. That is practical guidance from the discussion, not evidence from a controlled training intervention.
What does AI maturity mean for business?
In the discussion, maturity is broader than adopting a model or making a chatbot available. The reported differences span talent, workforce development, leadership direction, unified data, and the ability to put AI into operating workflows. David described the challenge as “It’s really operational discipline, right?”
One example is the use of autonomous, multistep workflows that are not checked by a human at every step. Fortune reported that 9% of organizations overall used them, including 36% of pacesetters and 2% of other organizations. This is a narrow measure of autonomous workflow use, not a measure of all AI adoption. Greater autonomy can change speed and workload, but it also raises the importance of deciding which work is suitable, assigning responsibility, and setting appropriate review and escalation controls.
Fortune also reported an average ROI of 160% for pacesetters from the index. The available report does not provide the ROI definition or full calculation, so that figure should not be read as an independently verified return or as directly comparable across companies.
How should leaders redesign work for AI?
David’s guidance, as described by Fortune, points toward treating AI as an operating-model question rather than simply adding software to existing tasks. She said, “I think it starts with leadership,” and advised aligning the organization around a vision, improving data foundations, choosing workflows, assigning ownership, and iterating. She cautioned against imposing one top-down redesign or putting AI on top of a broken process.
- Start with a business outcome. Identify the delay, cost, quality issue, or customer problem the workflow should address before choosing an AI capability.
- Map the end-to-end process. Find bottlenecks, handoffs, duplicate work, and failure points. Decide whether the process needs redesign before automation.
- Check data readiness. Establish whether the information needed is accessible, usable, and governed well enough for the intended workflow. The index figures show a gap in organizations reporting a path to unified data, but do not define a universal readiness threshold.
- Assign an owner and measures. Name who is accountable for the workflow and how the business will assess the result. A deployment without ownership or outcome measures can make activity look like progress without showing whether the work improved.
- Train for the role. Explain how AI fits specific tasks, how employees should verify results, and what to do when outputs are uncertain or unsuitable.
- Set review and escalation points. Decide where a person must approve, inspect, or take over, especially before moving from assisted work to multistep autonomous workflows.
- Iterate from a bounded use case. Observe how the redesigned workflow performs, address failures, and expand only when the process and its controls are working.
Which AI approach should an organization choose?
The reported findings do not rank interventions or prove which one will deliver the best result. They do suggest useful choices for leaders to examine before expanding AI use.
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| Decision | More deliberate approach | Common limitation to watch |
|---|---|---|
| Employee preparation | Training tailored to job functions and the tasks employees will perform with AI | Generic tool access does not establish that staff can use or check AI appropriately |
| Process change | Redesign the end-to-end workflow where needed, rather than layering AI onto a broken process | Automating existing friction can preserve or amplify it |
| Information foundations | Address data access, consistency, and governance relevant to the use case | AI performance depends on having suitable information and clear controls for its use |
| Accountability | Assign ownership and define business outcome measures | Deployment counts alone do not show whether business value was achieved |
| Human involvement | Set review and escalation requirements to match the workflow’s risks | Removing human checks at every step is not automatically appropriate for every process |
How Fortune AIQ differs from the ServiceNow maturity index
Fortune AIQ is a company ranking, not another name for the ServiceNow Enterprise AI Maturity Index. Fortune’s 2026 AIQ page describes a 75-company ranking drawing on ServiceNow’s maturity framework and ETR evaluation. Its methodology says 167 respondents were polled from July 10 to August 11, 2026; Fortune and ETR owned survey design, collection, and calculations, and ServiceNow was excluded as sponsor. See Fortune’s 2026 AIQ methodology.
The distinction matters: the 68%, 57%, and other leader-versus-other figures in the summit story are findings attributed to the Enterprise AI Maturity Index. They are not, by themselves, the scoring rules for the Fortune AIQ list. Fortune’s inaugural 2025 edition was AIQ 50, while the 2026 edition is AIQ 75. The 2025 list was described as a Fortune 500 ranking informed by the ServiceNow framework, an ETR survey of technology leaders, and prior-year ETR data; its top five were Alphabet, Visa, JPMorgan Chase, NVIDIA, and Mastercard. ServiceNow’s announcement of the 2025 list describes that inaugural edition.
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