HCLTech’s generative AI deployments are no longer stuck in pilot mode. This observation was made by executive vice president and CTO & global head (AI & Cloud Native Labs) Alan Flower, who recently said several clients had already moved into full production, delivering what he called a “genuine value stream transformation” as part of their Horizon 3 journey. 

The Indian IT giant was named a ‘Horizon 3 Innovator’ in HFS Research’s 2022 Cloud Native Transformation report. This top-tier recognition highlights HCLTech’s disruptive innovation in cloud-native technologies, strong market impact, and future-ready strategies. This helps reinforce India’s position as a global tech leader, signalling that Indian IT firms are now driving advanced, transformative innovation, not just offering support services.

In the HFS Horizon framework, Horizon 3 refers to companies that are not just refining existing services (Horizon 1) or creating adjacent innovations (Horizon 2), but disrupting the industry with next-gen, transformative capabilities.

This is the highest recognition in the HFS Horizons model, reserved for companies driving radical, future-ready innovation.

In a virtual one-on-one with David Cushman, executive research leader at HFS Research, Flower elaborated how HCLTech has deployed an AI clinical advisor for a major US-based healthcare provider. 

He explained that AI sits alongside doctors, giving them instant access to the latest medical research and recommending the most suitable treatment plans—freeing up nearly three minutes per 20-minute consultation. 

“While that may seem minor, the time savings translate into at least $50 million annually for the client, given the high cost of clinical labour,” he stated, adding that beyond cost the AI advisor also eases clinician burnout and boosts patient confidence, enabling faster, more accurate diagnoses informed by real-time research.

GenAI Awareness

Speaking from HCLTech’s AI lab in London, Flower said that the past two years marked a period of heavy experimentation, especially with horizontal use cases like chatbots. He informed that the phase is now over, sharing that the company has just kicked off its 500th GenAI project—an indication that enterprises are no longer testing the waters but are diving in with full confidence.

“There’s now a clear recognition among our clients that it’s time to move beyond pilots,” Flower said. “We’re seeing business leaders step in, not just tech teams, and they’re asking how to re-engineer their operations around AI. Many are aiming to become AI-native enterprises.”

Flower emphasised that clients now have clarity on use cases and are laser-focused on measuring and proving ROI before scaling solutions. He said the priority today is to find the most obvious, high-impact value stream opportunities and deliver measurable results.

Unexpected Challenges

As organisations worldwide move from GenAI pilots to full-scale production, Flower highlighted a fresh wave of challenges—ones that go beyond technology and touch deeper aspects of enterprise operations and mindset.

He pointed out that while most companies have already addressed expected hurdles like hallucinations in AI models, unexpected challenges are now surfacing as businesses move to scale. Among the most notable, he explained, is a misalignment between initial cloud expectations and operational realities.

“Many clients begin their AI journey with hyperscalers, assuming everything will run in the cloud,” Flower said. “But that’s often not the case. AI deployments are turning out to be hybrid in nature, with solutions spanning edge devices, AI PCs, on-prem data centres, and cloud environments. The challenge is, clients realise this too late, often right before production.”

Another concern is financial feasibility. Despite a surge of high-impact AI use cases, not all of them are economically viable at scale. Flower shared that many clients are now prioritising FinOps—financial operations for AI—by seeking model fine-tuning, cost efficiency, and localised deployment options. 

“Clients want smaller, cheaper models that can even run on a laptop,” he said, pointing to the growing demand for optimisation as AI moves from labs to business lines.

The most novel challenge, however, is what Flower calls the “HR problem of agentic AI”. With digital agents becoming integrated into workflows, enterprises are now grappling with questions about accountability, performance, and even job metrics. “Companies are reengineering their business around agentic AI, and we’re seeing a shift toward hybrid workforces that include digital agents,” Flower said. 

“Now clients ask: Who owns these digital employees—IT or HR? How do we measure and reward their performance? What if a human says, ‘I missed my targets because of the agents you assigned to me’?”

According to Flower, this third challenge is more about organisational change management than the technology itself. HCLTech CEO C Vijaykumar had earlier emphasised that generative AI will remain a core focus for enterprises across all industries.

The company’s four flagship AI offerings—AI Force, AI Foundry, AI Labs, and AI Engineering—have all seen substantial adoption and scaling during FY25. Notably, AI Labs alone has delivered 500 GenAI engagements for 400 clients.

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