Artificial intelligence is reshaping the way enterprises operate, moving beyond predictive analytics to systems capable of autonomous decision-making and intelligent execution. Even with increased investment in AI infrastructure, many companies struggle to make pilot initiatives drive enterprise-wide change because of disjointed data, isolated systems, poor governance, and a lack of measurable business goals.
In the latest episode of the Analytics Insight Podcast, Ashish Chandra, a global AI thought leader, shares his perspective on how organizations can transition from AI experimentation to enterprise-scale execution. He talks about reasons why most AI projects don’t succeed in generating value, the need for good-quality data and governance, ways that companies should stay away from ‘AI Theatre,’ the skills needed to be ready for AI, and what’s ahead for AI in the business world during the next ten years. Here are the excerpts:
In my opinion, the major problem is that firms perceive AI as a technological effort rather than a business transformation. Trying out new things is not difficult, but transforming the entire business involves data, governance, ownership, operating models, and business impacts. At present, most firms possess hundreds of proofs of concept but only a few production-ready AI products. Those organizations that succeed will be those that use AI as part of their everyday business activities. Only through this can the value of AI be realized.
I believe AI should become part of the enterprise architecture rather than sitting on top of existing systems as another application. Organizations need to create an AI fabric that connects enterprise data, business processes, knowledge layers, memory systems, human expertise, and decision intelligence. Instead of deploying hundreds of disconnected AI assistants, businesses should establish a unified intelligence layer that continuously learns from operations. The future will be intelligence-centric rather than application-centric. Just as cloud computing transformed enterprise infrastructure over the past decade, AI will become the underlying intelligence powering every critical business function.
As I have said before, data and context should be considered the bedrock of enterprise AI. Bad data leads to bad intelligence, no matter how intelligent the model is. Enterprises should invest in data foundations, metadata management, semantics, security, compliance, explainability, and governance. Equally critical is developing infrastructure purpose-built for AI workloads rather than relying on existing enterprise infrastructure. In the years ahead, enterprises won’t have any more AI; they’re going to have more trustworthy AI. Trust will be the key differentiator as companies rely on AI systems that can produce transparent and explainable results.
I think the assessment of the success of an AI solution has to be much wider than a conventional financial ROI approach. There are four areas where I measure the AI value. First, it is a financial one, involving such metrics as increased revenue, optimized costs, higher margins, and automation benefits. The second one is operational and involves faster cycle times, increased efficiency, and lower error rates. Third, it is the area of intelligence that relates to parameters such as improved decision-making quality, higher accuracy of forecasting and predictions, and better reusability of knowledge. Finally, there is a strategic aspect that includes innovation, customer experience, competitiveness, and agility.
This is going to be a time when companies will go beyond systems of record and into systems of reasoning. Not only will businesses stop digitizing themselves, but they will also start thinking, predicting, optimizing, and adapting continuously. There will be self-governing business capabilities, AI-driven business models, enterprise memory, agentic workforces, self-optimizing supply chains, digital twins, and executive decision intelligence all in real-time in this new age. Human beings will have to play a greater role in judgment, governance, creativity, and strategy. Companies that embrace the intelligent transformation will not only be more efficient but will also create the future of competitive advantage.
Listen to the full conversation on the Analytics Insight Podcast for deeper insights into building enterprise AI that delivers measurable business impact.