Infosys explains the gap between AI adoption and AI-first work in today’s workplaces

By now, most employees have interacted with AI at work—sometimes knowingly, often not. It drafts emails, routes service tickets, suggests next steps. Yet work still feels fragmented, reactive, and overloaded.

That contradiction defines the AI moment in workplaces today. Adoption is high. Confidence is not.

As organisations head into 2026, the difference between using AI and becoming AI-first is no longer semantic. It is structural—and increasingly visible in outcomes.

“An AI-first workplace embeds AI into end-to-end workflows transforming systems, processes, and experiences, not just improving user productivity,” says Shishank Gupta, Senior Vice President and Global Practice Head, Digital Workplace Ecosystem at Infosys. “It takes a long-term view, investing beyond immediate ROI to enable sustainable impact.”

Most organisations, he argues, are still layering AI on top of existing ways of working—without rethinking how work actually flows.

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From AI tools to AI-first work

Despite years of pilots and experimentation, leadership confidence remains thin. Gupta points to a telling statistic: only 2% of leaders feel confident across all key dimensions of AI readiness.

The gap, he says, stems from treating AI as a technology programme rather than an operating-model shift. Tools are deployed. Work remains unchanged. Employees adapt around the system instead of the system adapting to them.

That gap becomes costly as AI moves from optional to expected.

The next phase of AI adoption is already reshaping everyday workflows.

“Autonomous agents and context-aware systems fundamentally shift workflows from remaining reactive to more intelligent, proactive systems,” Gupta says. Instead of employees chasing approvals, fixing errors, or navigating multiple applications, work begins to anticipate intent.

The outcomes go beyond efficiency:

• Self-healing IT environments

• Automated approvals

• Personalised, context-aware support

In one example Gupta cites, a marketing agency using AI-driven project management recorded 30% faster turnaround times, freeing teams to focus on strategic and creative work rather than routine execution.

The design principle matters. “Organizations should deploy agentic frameworks that act as experience layers, understanding intent and contextualizing requests before routing to specialized agents.” In this model, AI becomes the connective tissue of work—not another interface employees must manage.

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Why leadership models—not tools—become the bottleneck

Technology, Gupta suggests, is rarely the limiting factor. Leadership models are.

Embedding AI meaningfully requires new operating structures, clear Responsible AI guardrails, and an AI-first culture that goes beyond experimentation. That includes redefining roles, investing in readiness, and building trust.

At Infosys, this has meant formalising AI capability through clear skill tiers:

• AI Aware professionals build baseline literacy

• AI Builders redesign processes

• AI Masters develop advanced solutions involving agentic AI and small language models

The objective is depth, not breadth.

Gupta cautions leaders against spreading AI too thin. “Leaders should identify high impact areas where major pieces of work can be AI enabled as against multiple smaller work packets spread across multiple workflows.” Fewer, high-quality use cases make impact visible—and adoption sustainable.

Most organisations do not fail at starting with AI. They fail at moving beyond pilots.

“Organizations often falter when scaling from pilots to AI-first execution due to fragmented data, weak governance, and cultural resistance,” Gupta says. Pilots are often selected for ease of implementation rather than business value.

Culture plays a decisive role. Research cited by Infosys shows 83% of leaders believe psychological safety directly impacts AI success, yet fewer than half rate their organisations highly on it. Without a safe-to-fail environment, experimentation becomes cautious, learning slows, and pilots remain isolated successes.

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What leaders must get right in 2026

As AI shifts from experimentation to expectation, Gupta’s guidance is grounded rather than futuristic.

“Leaders must embed AI responsibly by integrating it into workflows, strengthening governance, and focusing on outcomes rather than experimentation for its own sake.”

Equally important is how AI is positioned. “Position AI as an augmentator, not a replacement through ‘Human + AI’ models that enhance productivity and engagement.” That framing shapes trust, adoption, and long-term impact.

The real work ahead lies in:

• Redesigning roles

• Creating fluid skill pathways

• Investing in upskilling at scale

As 2026 unfolds, one lesson is becoming clear: AI-first is not about how many tools an organisation deploys. It is about whether work itself has been redesigned to use them well.