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AI upskilling is more likely to help when employees can connect it to real work, practice with suitable tools and support, and have time to learn. Assigning a video or presentation without those conditions may create the appearance of training without building practical judgment.
Why AI upskilling often misses the mark
In the September 12, 2025 episode of What IT Leaders Want, CIO hosts Keith Shaw and Matt Egan discuss how to upskill employees for an AI-enabled workplace. In a segment presented by Computerworld’s Valerie Potter, Jeff Foster, Redgate Software’s director of technology and innovation, describes why passive, one-off instruction can fall short: people need to understand the problem a technology addresses and get a chance to apply what they learn.
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The hosts raise a practical concern: when employees are left to experiment without company guidance, time, tools, or resources, they may turn to public AI tools and enter company information. That is a risk the hosts flag, not a quantified assessment of how often exposure occurs. Clear direction and approved ways to practice can help leaders address the concern without discouraging learning.
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What the survey figures do—and don’t—show
Pluralsight’s March 6, 2025 announcement reports results from a survey of 600 technology decision-makers. These figures describe respondents’ answers, not every company’s experience:
- 75% said their company had experienced delays or pauses in at least one AI project because of a lack of employee AI expertise.
- 35% said half or fewer of their employees had well-developed AI skills.
- 38% said half or fewer of their departments had incorporated AI skills into training programs and day-to-day use.
The figures point to a gap between AI ambitions and skills development among the surveyed decision-makers. They do not establish that any particular training approach will close that gap. The episode also mentions Microsoft and LinkedIn findings, but the exact underlying source for those figures is not established here, so they are not repeated as verified statistics.
How leaders can make learning part of the work
Start with the problem employees need to solve
Before teaching a tool, explain the work problem it is meant to address. Ask learners to identify the task, constraint, or customer need first, then consider whether AI or another technology is an appropriate response. This approach gives employees a way to evaluate a tool’s usefulness instead of treating adoption as the goal in itself.
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Foster describes a “10% time” practice at Redgate: Friday afternoons are set aside for learning and development. Activities can include lightning talks, trying a technology, finding a new way to solve a customer issue, or building a small application. This is an example from his account, not a guarantee that the same schedule will suit every organization.
Leaders can adapt the principle by making learning time explicit and pairing it with approved tools, clear data-handling guidance, and access to someone who can answer questions. Without time and resources, a training assignment may leave people to experiment on their own.
Use bounded experiments to build practical skill
A small, realistic project offers practice without requiring a high-stakes deployment. Foster gives examples such as a toy application or a Slack bot that orders team lunch. The point is not that these projects teach every AI skill; it is that employees can try ideas, encounter limitations, and discuss what they learn in a bounded setting.
For an AI exercise, leaders might define a low-risk task, specify which data may be used, and ask participants to explain where the tool helped and where human review remained necessary. The exercise should match the organization’s policies and the work employees are expected to do.
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Foster says Redgate looks for people with a thirst for learning. He describes asking candidates about the last book they read, a technology they have tried, and what excites them. These are examples of questions from his account, not a validated hiring test. For current employees, leaders can similarly make room for exploration and ask what skills people want to develop.
Build judgment, not just familiarity
Make technical reasoning visible
Foster says Redgate asks people making software changes to record why they are making a change, which options they considered, and why they chose one. He calls these records architecture decision records (ADRs). They let colleagues revisit the reasoning behind a decision, not just its outcome. Foster said Redgate’s library contained almost 500 decisions when he spoke; that is his account at the time of the episode, not a current independently verified count.
The same principle can inform AI work: document the problem, the options considered, relevant constraints, and the reason for choosing a particular approach. A record of reasoning helps teams review decisions as tools, requirements, or circumstances change.
Give people varied problems to work through
Foster describes the “expert beginner” as someone who has seen only a small part of a field, settles on rigid rules, and mistakes confidence for competence. His example concerns software used in customer environments with very different database counts, including an unusually large deployment. A rule that works in one familiar setting may not hold across the range.
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Leaders can help employees develop judgment by exposing them, with support, to cases where a familiar rule is not enough. Ask them to explain what assumptions changed, what evidence matters, and when they would seek help. The aim is not to make every learner an expert at once, but to help them recognize the limits of what they know.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical way to assess an upskilling plan
Before launching a learning initiative, leaders can check whether its design includes the conditions Foster’s examples emphasize:
- Work connection: Is the learning tied to a real problem employees are expected to handle?
- Time and resources: Do people have protected time, appropriate tools, and clear guidance?
- Practice and support: Can learners try the skill on a bounded task and discuss difficulties?
- Judgment: Does the plan teach people to evaluate context and exceptions, rather than only follow rules?
- Organizational memory: Can employees see and revisit the reasoning behind earlier technical decisions?
This is a way to examine a learning plan, not a tested ranking of training methods. The episode’s examples support a broader leadership principle: employees need opportunities to apply learning and develop judgment, not just access to training materials.
Source and scope
The discussion is from CIO’s 28-minute episode, published September 12, 2025. Foster’s Redgate examples are presented as his descriptions of company practices, not independently audited results or proof that they will work in every organization. The transcript available for the later part of the interview is incomplete, so no additional career-development advice is attributed to Foster beyond the material covered above.
Listen to the CIO episode and read its transcript. For the survey details, see Pluralsight’s March 6, 2025 announcement.
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