Artificial intelligence is steadily moving from pilot projects to enterprise-wide deployment, with organizations increasingly focusing on measurable outcomes and governance. Hao Yang, Vice President of AI at Splunk, recently shared insights on how enterprises are navigating the next phase of AI adoption, highlighting both the opportunities and challenges that lie ahead.
Yang noted that companies across sectors are moving beyond experimental AI projects and are beginning to operationalize models in real-world environments. This transition, however, is not without its complexities. While enthusiasm for AI remains strong, many organizations are learning that the path to value requires careful planning, robust infrastructure, and strong governance frameworks.
A key theme shaping enterprise adoption is trust. With businesses now relying on AI to make recommendations, analyze large datasets, and automate decisions, transparency around how these systems function has become non-negotiable. Yang emphasized that leaders are increasingly prioritizing explainability to ensure stakeholders understand why AI models arrive at specific outcomes. This transparency, he suggested, is essential for building long-term confidence in enterprise-scale deployments.
Another important shift is the growing integration of AI into security and observability workflows. Splunk has been at the forefront of this evolution, focusing on how AI can detect anomalies, mitigate risks, and provide predictive insights in critical enterprise environments. According to Yang, AI’s ability to identify potential system failures before they escalate is becoming one of the most valuable applications, particularly in industries where downtime or breaches carry significant financial and reputational consequences.
While enterprises are investing heavily in AI, the talent gap remains a pressing concern. Organizations are finding it difficult to hire and retain professionals who can build, deploy, and manage AI systems at scale. Yang pointed out that the demand for AI expertise extends beyond technical roles, as business leaders and operations managers must also understand how to interpret and apply AI-driven insights effectively.
The future of AI adoption, Yang believes, will depend heavily on how well enterprises balance innovation with regulation. With countries across the world introducing AI governance frameworks, businesses in India and beyond must adapt quickly to evolving compliance standards. Rather than viewing regulation as a limitation, Yang suggested enterprises treat it as an enabler of responsible adoption that builds consumer and stakeholder trust.
Enterprises are also increasingly focused on ROI. Beyond proof-of-concept initiatives, businesses want to see AI deliver tangible benefits such as improved efficiency, cost savings, and new revenue streams. Yang highlighted that measurement and monitoring tools are critical for ensuring that AI deployments not only work as intended but also generate lasting business value.
One area showing rapid momentum is predictive analytics. Enterprises are using AI models to forecast customer behavior, supply chain needs, and market demand with greater accuracy. This capability allows organizations to shift from reactive to proactive decision-making, giving them a competitive edge in fast-changing industries.
At the same time, Yang cautioned that cultural adoption is as important as technological readiness. Successful enterprise AI requires leadership buy-in, cross-functional collaboration, and a culture that embraces experimentation. Without these elements, even the most advanced AI systems risk underperforming or being sidelined.
Globally, AI adoption is accelerating. Gartner forecasts that by 2026, over 80% of enterprises will have integrated generative AI into their operations in some capacity. India, with its vast digital ecosystem and expanding base of skilled professionals, is expected to play a major role in shaping how enterprises deploy and scale these technologies.
Looking ahead, Yang is optimistic about AI’s trajectory in the enterprise sector. He believes the coming years will see not just incremental improvements, but transformative changes in how organizations operate. From streamlining workflows to enabling real-time decision-making, AI is poised to become embedded in the very fabric of enterprise strategy.
For now, the focus remains on building trustworthy, scalable, and results-driven AI systems. Enterprises that can align technology with governance, culture, and business objectives will be best positioned to lead in the next phase of AI adoption.