How is AI transforming the future of enterprise innovation? EXPLAINED

Artificial intelligence is moving beyond pilot projects and proofs-of-concept into the very fabric of enterprise strategy, reshaping how businesses innovate, compete and create value. Industry leaders stated that the technology is no longer just a productivity tool; it has become a strategic capability driving industry-specific transformation, richer customer experiences and smarter business process orchestration.

It also transforms enterprise innovation by shifting from simple task automation to autonomous, goal-driven systems that reason, plan, and execute complex workflows. Companies leverage generative models, multi-agent frameworks, and predictive analytics to reshape research and development, streamline operations, and hyper-personalise customer engagement.

Enterprises' investment in AI

According to Praveen Ojha, Chief Technologist at EPAM Systems India Pvt. Ltd., enterprises are concentrating AI investment where it delivers the strongest competitive edge. He noted that AI now blends reasoning, prediction and content generation with enterprise data, enabling companies to redesign business models and boost workforce productivity. Sectors like healthcare and financial services are already seeing tangible applications, from clinical research and pharmacovigilance to content generation and regulatory mapping, while multimodal and agentic AI increasingly power more intuitive, automated workflows.

Praveen Ojha said, "Enterprises are focusing AI investment on a few key areas where it creates the most competitive differentiation: industry-specific transformation, customer experience and business process orchestration. AI is combining reasoning, prediction and content generation with enterprise data to help organisations redesign business models, personalise customer experiences, augment workforce productivity through intelligent automation and create new sources of value through AI-enabled products and services. In healthcare and life sciences, AI is modernising clinical research through AI-powered evidence generation, data hub development, MDM (Medical Decision Making) modernisation and pharmacovigilance."

"In financial services, it's being applied to content generation, MCC (Merchant Category Code) validation and BIAN (Banking Industry Architecture Network) mapping. Alongside these use cases, multimodal AI is enabling richer, more intuitive customer experiences by combining text, voice, image, video and sensor data, while agentic AI is increasingly orchestrating workflows, supporting decisions and executing complex business processes with human oversight. As these capabilities scale, data centre operations must evolve too. Supporting multimodal and agentic AI at scale requires intelligent infrastructure that can dynamically allocate compute and orchestrate workloads with minimal manual intervention," he added.

Ojha emphasised that true competitive advantage doesn't come from foundation models alone, but from combining them with proprietary data and deep domain expertise. Many organisations, he said, are now deploying "digital workers", AI agents managing end-to-end functional responsibilities under human oversight. As these systems scale, he added, supporting infrastructure must also evolve to dynamically allocate compute and orchestrate workloads with minimal manual intervention, ensuring enterprises can convert AI capability into measurable business outcomes.

"Capturing this value requires rethinking enterprise innovation. Many are increasingly adopting digital workers - AI agents that handle end-to-end responsibilities within a function, with humans providing oversight. Competitive advantage doesn’t come from access to foundation models alone. Instead, it comes from combining those models with proprietary data, institutional knowledge and deep domain expertise. Beyond efficiency gains, organisations have started looking at AI enabling new products, better customer experiences and new sources of business value. Ultimately, leadership in this next phase will belong to organisations that can turn AI into real outcomes through engineering capabilities and multidisciplinary teams that can deploy AI at production scale," Ojha said.

AI into workflows and innovation cycles

Echoing this shift, Mahesh Sharma, SVP of Data & AI Engineering at Bread Financial, noted that the AI conversation has moved from experimentation to full-scale transformation. Organisations, he explained, are embedding AI into everyday workflows and innovation cycles rather than treating it as a standalone tool. Sharma outlined three defining shifts: reaching critical mass in AI adoption to unlock real impact, using AI to shorten the gap between idea and execution, and empowering agentic AI to plan and execute complex, long-running tasks, freeing humans to focus on strategic judgment and creativity.

"The conversation around AI is shifting from experimentation to transformation. Forward-looking organisations no longer view AI as a standalone tool; they are reimagining how work gets done by embedding AI into workflows, customer experiences and innovation cycles to unlock new forms of value creation. The future will be shaped by three important shifts. First, organisations must rethink how they use AI to solve business problems. Access alone does not create impact. Value emerges when adoption reaches critical mass, and AI is incorporated into daily work.
Second, AI is dramatically shortening the distance between idea and execution. Organisations can hypothesise faster, validate assumptions with greater confidence, and accelerate the journey from concept to implementation. As a result, teams can innovate with greater speed and precision. Third, Agentic AI will fundamentally reshape how work is performed. Agents will increasingly plan, coordinate and execute complex, long-running activities based on desired business outcomes. This shift will enable new operating models where humans focus on strategic judgment, creativity and oversight, while AI handles orchestration and execution at scale," Sharma said.

"Organisations must be deliberate in how they pursue this transformation. Leaders must define the problem they want to solve, identify where AI can create meaningful value, and establish clear accountability for outcomes. The organisations that create lasting value will be those that view AI not merely as a productivity tool, but as a strategic capability that equips people to solve complex problems, make better-informed decisions, and convert ideas into sustainable business outcomes," Sharma added.

Reacting to the use of AI in enterprises, Dinesh Parikh, Vice President - Product Management, Newgen Software, said, "For years, enterprises viewed AI primarily as a tool for generating insights. Today, we're in a far more consequential phase where AI is an active participant in how work gets done. The conversation is no longer about intelligence in isolation; it's about intelligence in motion. The most innovative organisations are embedding AI directly into the fabric of their operations, enabling systems to understand and orchestrate actions across people, processes, and technology. This shift is redefining enterprise agility, helping businesses move from reactive decision-making to continuous, adaptive execution, where AI can drive multi-step intelligence and actions while keeping humans in the loop for oversight, judgment, and accountability. What's particularly exciting is the emergence of AI as a force multiplier for human potential. Rather than replacing expertise, it amplifies it, freeing employees from routine work and enabling them to focus on creativity, strategy, and problem-solving. The enterprises that lead the next decade won't be those with the most AI pilots. They'll be the ones that successfully orchestrate AI, automation, data, and human ingenuity into a connected system of innovation, transforming intelligence from a capability into a competitive advantage."