The emergence of generative artificial intelligence and large language models (LLMs) has sparked significant curiosity and experimentation throughout the business world. Fortune 500 companies are actively testing AI co-pilots, developing content assistance tools, and reevaluating their customer service and operational strategies.
According to McKinsey, AI's long-term potential is estimated at $4.4 trillion in enhanced productivity growth derived from corporate use cases. Furthermore, the rapid advancement of AI technologies, such as large language models (LLMs), has established AI as an essential business capability. Additionally, IDC projects that global expenditures on AI will exceed $632 billion by 2028, underscoring the strategic imperative for enterprises to incorporate AI into their operations.
The potential of these technologies is undeniable. However, potential does not always translate into impact.
At Wipro, we collaborate closely with global enterprises that are ambitious and pragmatic about AI. Over the past few months, we have conducted a series of CIO roundtables across various geographies with panels of senior technology executives who, while appreciative of AI's potential in their business, invariably pivot to stories of failure during deployments and express a desire to focus on their organization’s readiness to manage the AI science experiment that must be deployed at scale.
We consistently hear that the main challenges are not model selection, infrastructure, or cost—they are human. The real work lies in aligning people, processes, and behaviors to effectively and responsibly utilize this new class of tools.
Here’s our perspective on that challenge and what we’ve learned from helping large organizations navigate the transition.
1. Start with Change, Not Code
Generative AI represents a new way of working. It doesn’t just automate processes; it truly enhances collaboration. Rather than simply speeding up tasks, it revolutionizes decision-making and reshapes how we gather knowledge, making everything feel smoother and more connected.
Many organizations approach AI adoption like any other software rollout: choose a platform, do a pilot, and try to scale what works. However, with generative AI, pilots often show technical promise while failing to stick. Why? Because people don’t know what to trust, how to use it, or what it means for their role. And then they reject it or, worse, sabotage it.
What we’re seeing work:
- Start with user behavior and workflows, rather than the technical model.
- Identify areas with cognitive load, repetition, or bottlenecks that can be improved, not eliminated.
- Co-design solutions with the teams who will utilize them daily.
When employees feel involved and prepared, rather than cornered or caught off guard, they are much more inclined to embrace and maintain new tools.
2. Redefine Roles and Expectations
AI’s large language models (LLMs) enhance human capabilities in ways we are still discovering.
Here are a few industry-specific use cases where AI is augmenting human capabilities:
- Manufacturing: Integrating cloud-based AI with legacy systems requires collaboration with teams with different skills. Siloed teams and lack of communication and understanding between teams can create a major roadblock in integration followed by adoption. This is especially relevant in manufacturing where IT/OT convergence is already an issue and upskilling the existing workforce to interact with and maintain AI-powered automation systems in the cloud is critical.
- Finance: AI can enhance fraud detection and risk management by analyzing vast amounts of data in real-time. A unified cloud strategy ensures that data from various sources is accessible and integrated, enabling more accurate and timely insights.
- Healthcare: AI-driven diagnostics and personalized treatment plans rely on comprehensive data from multiple sources. A unified cloud strategy facilitates the seamless integration of patient data, improving the accuracy and effectiveness of AI applications.
But this augmentation changes what’s expected of the human in the loop. Do they validate? Edit? Just oversee? Are they redundant?
We recommend:
- Redefine job responsibilities to reflect the shift from “doing” to “directing.”
- Offer clarity on what generative AI is and is not responsible for.
- Provide training in prompt design, judgment, and critical evaluation of AI output.
People must understand how to use the tools and how their role fits into a hybrid human-AI workflow.
3. Create Guardrails Without Creating Fear
Concerns about hallucination, bias, security, and compliance are valid and essential; however, innovation stalls if governance becomes a barrier to experimentation.
What’s working:
- Establish clear usage guidelines early, specifying which data can be used and how AI can assist.
- Utilize tiered risk frameworks: internal tools may have lower risk thresholds than customer-facing applications.
- Encourage “sandboxed” environments where employees can safely test and learn.
Governance should enable confidence, not introduce paralysis.
4. Rethink Skill Development as a Cultural Strategy
A major misconception regarding AI adoption is the belief that upskilling involves transforming everyone into data scientists or prompt engineers. Achieving success requires a broader cultural shift towards curiosity, experimentation, and collaboration across functions.
We’ve seen impact when companies:
- Introduce generative AI literacy programs at all levels—from executives to frontline teams.
- Frame upskilling as capability enhancement rather than threat mitigation.
- Celebrate small wins and peer-to-peer learning instead of relying solely on formal training.
Generative AI adoption represents a change management journey, and learning must occur in context, not in isolation.
5. Anchor Innovation to Outcomes, Not Novelty
Many enterprises feel overwhelmed by the rapid pace of AI advancements. It’s tempting to pursue what’s new; however, sustained adoption results from addressing real problems, not merely showcasing demos.
We advise clients to:
- Prioritize use cases connected to significant, measurable outcomes, such as cost reduction, cycle time improvements, and customer satisfaction.
- Assess impact based on speed, volume, and human enablement. Are individuals making better decisions? Are they feeling less overwhelmed?
- Create space for experimentation—but move swiftly to operational integration where value is demonstrated.
Change lasts when it’s connected to things that matter.
6. Think in Terms of Operating Model, Not Just Tools
Embedding generative AI in an enterprise is less about deploying an assistant and more about transforming business operations. This means aligning AI initiatives with broader digital, data, and workforce strategies.
Some key shifts to consider:
- Is your organizational structure designed to support AI-as-a-service across business units?
- Do you have roles (like AI product managers or content curators) that didn’t exist three years ago?
- Are incentives aligned to reward safe, creative AI adoption?
Even the most advanced AI tools will fall short without proper structural support.
Final Thoughts: It’s a People Problem (in a Good Way)
We think generative AI can accelerate productivity, creativity, and insight across the enterprise—but only when it’s human-centered. The technology is advancing rapidly. The challenge—and the opportunity—is helping people evolve with it.
For CxOs, the priority is no longer whether generative AI can be used. It’s about introducing it thoughtfully, supporting its operation, and embedding it sustainably.
Technology isn’t the hard part. Change is. But with the right strategy, change is where the value lives.
