The debate over enterprise AI has shifted from whether businesses should adopt it to how they should govern it, where it delivers the greatest business value, and how to deploy it without compromising trust. This was the central focus during AI Appreciation Day, marked annually on July 16, as companies transition from testing AI to putting it into daily operations.
Leaders in technology now argue that the next phase of AI will not be determined by the size of foundation models. Instead, it will depend on creating secure, domain-aware, and human-centric systems tailored to specific business needs. This change shows that AI’s effectiveness relies more on proper management than on sheer power.
Governance replaces hype as the top concern
Dan Mountstephen, General Manager and Senior Vice President for Asia Pacific and Japan at Okta, directly addressed the challenge: “Instead, the focus has shifted to how AI should be governed, where it delivers the greatest business value, and how organisations can deploy it without compromising trust.” He emphasized that no single AI platform, model, or cloud provider will dominate enterprise use. The real difficulty lies in establishing consistent governance across diverse systems.
Identity management is becoming essential for this governance. While human users remain central, AI agents, APIs, and service accounts now require the same oversight—visibility, lifecycle management, and access controls—to function safely. This change highlights a key principle: AI systems, like employees, must have structured permissions and accountability.
Trustworthy AI demands engineering rigor
Chandan Govindarajulu, Executive Vice President at Virtusa, reinforced this idea: “Responsible AI must become an integral part of the engineering process rather than an afterthought.” His focus on transparency, rigorous testing, and continuous human oversight reflects a firm stance: trustworthy AI is not optional—it is necessary for widespread adoption.
The tension between AI’s independence and oversight is most apparent in workflows where AI acts as a partner rather than a tool. Venugopal Ganganna, Co-Founder and CIO of LS Digital, noted that companies are adopting agentic AI, systems that do more than assist; they actively manage tasks, from customer interactions to investment decisions. However, success depends on design: workflows must ensure humans and AI work together, not replace each other.
This approach matches a broader industry trend. Unlike the early days of AI, when speed of adoption was the main goal, today’s focus is on measurable results, time saved, errors reduced, and improved decisions. The question is no longer whether AI works, but whether it works within the business, not just alongside it.
Small businesses prioritize human-AI collaboration
For small businesses, the urgency is even greater. Brijesh Agarwal, CEO of Busy Infotech, said AI should support human judgment, not replace it. He added that its value should ultimately be measured by time saved, errors prevented, and better decisions.
Security and infrastructure now shape the discussion as much as governance. Cybercriminals are increasingly using AI to automate attacks, from phishing to malware creation. Subir Sangal, CEO of Eagle Information Systems, warned that the future of AI will not be decided by model size, but by who builds systems that understand the business they serve and keep it secure.
This understanding extends to where AI operates. Private deployments are growing as businesses prioritize control over data, models, and institutional knowledge. Praveer Kochhar, Co-Founder of KOGO AI, noted that trust depends on maintaining auditability, something public platforms often cannot guarantee. The shift reflects a simple fact: enterprises will not surrender control over their most sensitive operations.
Infrastructure and control define AI’s future
Badri Gomatam, Group CTO at STL, connected this to infrastructure: “The future of AI will be built on resilient, scalable and sustainable networks.” Without the right foundation, even the most advanced models risk becoming liabilities.
Tushar Agnihotri, CEO of Route Mobile, highlighted how AI has moved beyond being a technical tool to becoming a strategic priority at the board level. “Artificial intelligence is no longer a technology conversation; it has become a boardroom imperative,” he said. Companies that integrate AI into customer interactions and operations will gain a competitive advantage; but only if governance, transparency, and trust are built in from the beginning.
Agentic AI requires new workflow designs
Venugopal Ganganna of LS Digital described how companies are moving from treating AI as a productivity aid to deploying it as an active partner in complex processes. Agentic AI, systems that manage tasks like customer journeys or investment optimization, demands a rethinking of how humans and machines collaborate. “The organisations that succeed will be those that design workflows where people and AI work together to create better outcomes,” he said, adding that AI-native businesses will require strong data foundations, connected technology platforms, and responsible governance.
Cybersecurity as a non-negotiable requirement
Dr. Sanjay Katkar of Quick Heal Technologies framed AI’s security challenges as both a technical and national concern. “Responsible, transparent and well-governed AI is not just an innovation imperative, but a national cybersecurity imperative,” he stated. The rise of AI-powered phishing, deepfake scams, and automated malware has forced businesses to treat AI adoption as inseparable from risk management.
