The conversation around artificial intelligence has fundamentally shifted from what's technologically possible to what's practically achievable in real organizations, and that shift hinges entirely on people. While companies continue pouring billions into AI tools and infrastructure, a growing gap has emerged between technology investment and actual business impact. The companies winning the AI race aren't those buying the fanciest models; they're the ones redesigning how work gets done and investing in their workforce with the same intensity they're investing in the technology itself.

Why Worker Confidence in AI Is Falling Even as Investment Rises?

There's a paradox unfolding in enterprise AI adoption. Employers are investing heavily in artificial intelligence capabilities, yet worker confidence in using these tools is declining. This confidence gap represents the real bottleneck in AI transformation, according to workforce experts preparing to present research at VivaTech 2026. The challenge isn't technical; it's human. Organizations need to close the gap between what they're buying and what their people actually feel equipped to use.

The evidence is becoming clearer. Among technology leaders surveyed across 12 countries, 54% of Chief Information Officers (CIOs) are already realizing positive returns on their AI investments, according to the Experis CIO Outlook 2026 research based on responses from 1,930 technology leaders. That's a meaningful milestone, but it also means nearly half of CIOs aren't yet seeing measurable business value from their AI spending. The difference between the winners and the rest often comes down to workforce strategy.

How Are Leading Companies Turning AI Ambition Into Business Impact?

Organizations that are successfully moving beyond AI experimentation share a common approach: they're treating workforce transformation as a strategic priority equal to technology deployment. This means redesigning work processes, developing new skills systematically, and creating clear career pathways for employees to grow alongside emerging technologies.

One concrete example of this shift is the emergence of a new role called the Forward Deployed Engineer (FDE). This position combines technical knowledge of large language models (LLMs) and specialized smaller models with deep business understanding to translate AI capabilities into measurable return on investment (ROI) and drive client adoption. The FDE role is among the fastest-growing positions in the AI ecosystem, reflecting how companies are rethinking what technical talent actually needs to know to create business value.

LTM, a business creativity partner to major enterprises, recently launched AI 1000, a structured workforce transformation initiative designed to develop more than 1,000 AI-certified engineers, including Forward Deployed Engineers. The program uses a four-stage model to identify high-potential engineers, enable them through curated learning journeys focused on AI-native skills, deploy them into real AI programs, and govern their performance while feeding insights back into the system. This creates a continuous cycle of capability building and measurable business impact.

"The role of the technology engineer is evolving rapidly. AI 1000 is built with the purpose of enhancing workforce productivity in creating tangible business outcomes. Through the AI 1000 Center of Excellence, we are building structured pathways to develop the combination of technical excellence and domain expertise to enable this purpose and prepare our talent for the future," said Venu Lambu, CEO and Managing Director of LTM.

Venu Lambu, CEO and Managing Director, LTM

LTM has already built significant momentum in AI workforce development. The company reports over 6.5 million learning hours completed, nearly 84% learning penetration across its workforce, more than 15,000 external AI certifications earned, and more than 24,000 AI-trained associates. The AI 1000 program formalizes these efforts into a structured, evidence-based approach with defined role pathways, measurable milestones, and a governed deployment framework. Critically, success is measured not by the number of employees trained, but by the business outcomes those engineers actually deliver.

Steps to Build Workforce Readiness for Enterprise AI Adoption

• Identify High-Potential Talent: Use data-driven assessment tools like AI Readiness Index scores to identify which employees have the foundational skills and learning capacity to develop into AI-capable roles, rather than assuming all technical staff can transition equally.
• Design Curated Learning Journeys: Create targeted training programs focused on AI-native skills and validate capabilities through practical hackathons and real-world use cases, ensuring learning translates directly to job performance.
• Deploy Into Real Programs With Governance: Move trained engineers into actual AI projects with clear performance tracking, feedback loops, and career frameworks that reward business impact, not just technical certifications.
• Establish Continuous Feedback Cycles: Build systems that capture insights from deployed engineers and feed those learnings back into training programs, creating a virtuous cycle of improvement and adaptation.

What Does the Shift From AI Tools to AI Workforce Strategy Mean for Enterprises?

The broader implication is significant. As AI capabilities become increasingly commoditized, the competitive advantage shifts to organizations that can effectively integrate these tools into their operations through skilled, confident, and well-supported workforces. This represents a fundamental reorientation of enterprise AI strategy from a technology-first approach to a people-first approach.

ManpowerGroup, the global workforce solutions leader, is returning to VivaTech for its 10th consecutive year with a focus on this exact transition. The company's theme for the conference is "Human First, Digital Always: Redesigning Work for the Age of AI," emphasizing that organizations must move beyond experimentation and unlock the full value of AI by redesigning work, developing skills, and creating pathways for people to grow alongside emerging technologies.

"The conversation around AI has shifted from what's possible to what's practical. While employers are investing in AI, worker confidence in using it is falling. That's the gap we need to close. The hardest part of AI adoption is the people side of the change. The companies getting ahead right now are the ones investing in their workforce with the same intensity they're investing in the tools," said Becky Frankiewicz, President and Chief Strategy Officer of ManpowerGroup.

Becky Frankiewicz, President and Chief Strategy Officer, ManpowerGroup

This workforce-first perspective is gaining traction across industries. Manufacturing, for example, is confronting a historic labor crunch as aging workforces retire, and AI is emerging as a bridge for knowledge transfer to a new generation. The shift from traditional automation to autonomous AI agents is spawning entirely new career categories while eliminating old ones, making workforce strategy not just a human resources concern but a core business imperative.

The data supports this shift in priorities. Among the key challenges enterprises face, the ability to reskill workers quickly enough to keep pace with AI-driven change ranks among the most critical, particularly in industries facing the greatest workforce pressures. Organizations that treat this as a strategic priority, with dedicated centers of excellence, structured learning pathways, and clear governance frameworks, are the ones seeing measurable returns on their AI investments.

For enterprises still primarily focused on acquiring AI tools and platforms, the message is clear: technology is necessary but not sufficient. The real competitive advantage in the AI era belongs to organizations that invest equally in their people, redesign work to leverage both human and artificial intelligence effectively, and create career pathways that help employees grow alongside the technology. That's where the business impact actually happens.