The adoption of artificial intelligence in companies won’t be decided solely by licenses, models, automation, or technology investment. Gartner warns that by 2027, half of the companies without a people-centric AI strategy will lose their top AI talent to competitors that prioritize real team training.
The warning comes amid a race to incorporate generative tools, agents, and automation into internal processes. Many organizations measure progress by the number of employees with access to AI tools or the hours supposedly saved. However, Gartner considers this approach insufficient and potentially overlooks a more serious issue: workers are not always prepared, do not always trust corporate tools, and do not always understand how their roles will change.
The mistake of measuring AI only by hours saved
The report is based on Gartner’s Global Labor Market Survey, conducted in the first quarter of 2026 with 12,004 employees and managers across 40 countries. The firm identifies a clear gap between access, usage, and transformation. In fact, 19% of respondents say they haven’t saved time with AI, despite many companies highlighting time savings as the main success metric.
This data challenges a widespread narrative. Having an AI tool available doesn’t necessarily mean the employee uses it correctly. Saving a few minutes on a task does not automatically equate to improved quality, redesigned processes, or increased value. Gartner emphasizes that companies should focus more on the depth and diversity of AI use rather than simple adoption metrics.
According to their data, employees proficient in AI across multiple use cases are twice as likely to be highly productive, 2.3 times more likely to produce high-quality work, and 3.2 times more likely to improve processes effectively. The difference is thus not in using AI once a week to summarize a text but in thoughtfully integrating it into various tasks, with supervision, context, and accumulated learning.
Gartner recommends creating a more realistic return index focused on the diversity and depth of AI use. It also suggests establishing a common repository of use cases to capture learnings, avoid duplication, and accelerate knowledge transfer within the organization.
The shadow of personal AI at work
One of the most sensitive points in the report is the use of personal tools. Gartner notes that 88% of employees with access to enterprise AI also use personal AI tools for work tasks. This behavior is not surprising. Many public tools are faster, more familiar, or more convenient than corporate solutions, prompting workers to turn to them for urgent tasks.
The problem is that this convenience introduces data risks. When an employee copies company information, internal documents, code, contracts, spreadsheets, or customer data into an unapproved tool, the company loses control over privacy, compliance, and security. The phenomenon of “shadow AI” resembles the old “shadow IT,” but with a key difference: AI can process, summarize, and reuse sensitive information much more easily.
Gartner also warns of talent risks. Hybrid users—those combining personal and corporate tools—are 1.7 times more likely to report significant time savings compared to those who only use company solutions. If internal tools are worse, slower, or too limited, top talent may feel held back by their own organization.
This points to shared responsibility between CIOs and CHROs. Technology should improve user experience, security, and integration of tools. HR must participate in governance, define decision rights, manage expectations, and mitigate risks related to people, culture, and organization.
AI isn’t reaching all employees equally
Gartner also detects an uneven distribution of benefits. While many companies offer enterprise AI, 73% of highly productive users are managers or executives. Individual employees—often responsible for many automatable tasks—receive less guidance, support, and onboarding.
This can create an internal divide. Leadership uses AI to prepare documents, analyze information, summarize meetings, or accelerate decisions, while operational teams remain unsure how to apply it in their daily work. If this happens, AI enhances management productivity but fails to truly transform the organization.
The solution isn’t just providing generic training. Managers should act as bridges between strategy and implementation. They understand their teams’ actual work, identify bottlenecks, repetitive tasks, and uncertainties blocking adoption. For AI to become routine, mid-level managers need training, concrete examples, and the ability to tailor tool usage to their area.
The other major barrier is psychological. Gartner notes that anxiety over job loss is hampering adoption and reducing productivity. Data shows that employees with a positive outlook on AI are 3.4 times more likely to be highly productive. It’s not enough to teach how to craft prompts; companies must explain how roles will change, what tasks will be automated, the skills that will become more important, and where human judgment remains essential.
This is crucial for retention. AI, data, automation, and technology professionals have many options today. If they perceive their company is improvising, poorly measuring, not protecting data, or lacking understanding of AI’s human impact, they might leave for organizations with a more mature strategy.
The Gartner survey of 197 C-level executives and senior managers in December 2025 indicates that readiness remains limited: only 27% report having a comprehensive AI strategy, and just 20% believe their workforce is truly prepared. The gap between ambitions and internal capacity is clear.
For companies, the message is uncomfortable but valuable. AI isn’t just implemented by buying tools. It requires redesigning processes, training with real cases, building trust, safeguarding data, and motivating employees to use corporate solutions. Reducing this to access, licenses, and time savings might lead to a paradox: abundant AI with little real impact and top talent looking elsewhere.
Frequently Asked Questions
What does Gartner predict about AI and talent?
Gartner forecasts that by 2027, half of the companies without a people-centric AI strategy will lose their top AI talent to competitors more focused on actual team training.
Why isn’t measuring hours saved enough?
Because time savings alone do not reflect quality, productivity, or process improvements. Gartner recommends assessing the depth and variety of AI applications within the organization.
What is shadow AI?
It’s the use of personal or unapproved AI tools for work tasks. While it can boost individual productivity, it also increases risks related to privacy, security, and compliance.
What should companies do to retain AI talent?
Enhance internal tools, provide training with real use cases, establish governance, communicate role evolutions, and create an environment where employees trust AI and know how to work with it.
via: Gartner