Tag: AI Pilots

  • Why 80% of AI Pilots Fail: Ashish Chandra on Moving from Experiments to Enterprise Impact

    Why 80% of AI Pilots Fail: Ashish Chandra on Moving from Experiments to Enterprise Impact

    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:

    Why do Many Organizations Fail to Scale AI Beyond Pilot Projects?

    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.

    How Can Enterprises Build AI as Core Business Capability Instead of Another Technology Layer?

    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.

    Why are Data Quality and Governance Critical to Successful Enterprise AI?

    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.

    How Should Organizations Measure Return on Investment from AI Initiatives?

    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.

    What does Future of Enterprise AI Look Like Over Next Decade?

    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.

  • From Pilot to Profit: How to Make AI Commercially Viable at Scale

    From Pilot to Profit: How to Make AI Commercially Viable at Scale

    Over the past few years, enterprises have become proficient at launching AI pilots. However, the real challenge—and the key to lasting value—lies in scaling those pilots into reliable, commercially viable capabilities. Technical success in a test environment tells us very little about an initiative’s readiness for production. To bridge that gap, organizations must focus on five critical areas: business case alignment, cost transparency, governance, ownership, and workforce upskilling.

    1. Anchor AI Initiatives in Business Metrics

    One of the most common mistakes is delaying the business case until after a pilot shows promise. Instead, establish the economic logic at the outset. Every use case should tie directly to a metric the organization already values—such as turnaround time, conversion rate, productivity, service quality, or risk reduction. Early-stage experimentation will involve uncertainty, but there should always be a clear hypothesis about where value will come from and what must be true for the investment to make sense at scale. Additionally, prioritize business functions where end-users have a higher propensity to adopt AI, increasing the likelihood of successful scaling.

    2. Understand the Full Cost of Production

    During a pilot, costs like model access or platform fees are visible. But production deployments bring a much broader cost base: data cleaning and maintenance, system integration, security testing, ongoing monitoring, human review, and workflow changes. These costs are manageable when anticipated early. The right question is not simply how much the technology costs, but whether the value created continues to justify the total cost of operation as usage expands. Avoid scaling first and asking about economics later.

    3. Establish Clear Governance and Leadership Direction

    Governance, risk, and compliance concerns are major scaling barriers—50% of respondents in a recent Deloitte study cited them as a primary obstacle. Organizations often swing between rushing ahead without adequate controls or delaying useful initiatives until every data and governance issue is solved. Leadership must assess readiness in the context of each use case. The level of oversight should reflect the consequences of error. Security, compliance, evaluation, and human-in-the-loop processes should be designed in from the start, not bolted on after a successful pilot is waiting for approval.

    4. Define Clear Ownership Across Teams

    AI initiatives commonly begin in innovation, technology, or transformation teams—appropriate during exploration. But difficulty arises when ownership must move beyond that group. Once AI becomes part of a business process, the benefiting function should be accountable for outcomes. At the same time, technology teams own architecture and reliability, risk teams need visibility into controls, and finance must understand the economics. Successful scaling happens when these groups are involved early enough to make decisions together, rather than being brought in sequentially as approval gates.

    5. Invest in Contextual AI Upskilling

    A major barrier to AI adoption and scaling is the lack of a common understanding of AI among employees. Generic online courses often fail to connect to an individual’s day-to-day work in their specific industry, domain, and organization. Enterprises should invest in customized, curated learning and development programs that help employees appreciate how AI applies to their roles. This leads to better adoption and more effective scaling of AI use cases.

    Concluding Note

    As enterprise AI programs mature, success will be defined not by the volume of initiatives but by the value they deliver. Organizations must become more disciplined about prioritizing use cases that demonstrate clear business impact, operational readiness, and sustainable economics. The companies that get this right will not necessarily have the longest list of AI initiatives—but they will consistently turn pilots into trusted, scalable solutions. Ultimately, the true measure of AI maturity is the ability to create lasting business value long after the initial excitement has faded.

    Authored by Raghu A, Partner, Deloitte India

  • AWS Co-Founder Matt Domo on Why Enterprise AI Pilots Fail and How to Scale

    AWS Co-Founder Matt Domo on Why Enterprise AI Pilots Fail and How to Scale

    According to a recent Forrester analysis, only 10-15% of artificial intelligence pilots successfully transition into long-term production. That means most enterprise AI investments stall before they ever deliver real impact. Matt Domo, a co-founder of Amazon Web Services (AWS) and creator of foundational Microsoft enterprise technologies, has a clear explanation: the problem is rarely the technology itself, but rather how organizations are structured around it.

    Now advising Fortune 500 companies, government agencies, and universities, Domo shared his insights with AI Magazine on moving from experimentation to enterprise-scale execution.

    Why Most AI Initiatives Fail at the Executive Level

    Domo identifies three root causes for AI failure: unclear ownership of outcomes, misaligned incentives across teams, and operating models that were not built for AI-driven decision-making. “Leaders fund pilots, but they don’t redesign how work happens. They treat them as technology projects instead of using them to change how the business operates,” he said. Without organizational alignment, AI simply gets layered onto existing processes, which looks like progress but yields no measurable results.

    Scaling AI from Pilot to Enterprise-Wide Deployment

    Scaling does not come from running more pilots; it comes from standardization. “Companies that succeed define a repeatable path from pilot to production, assign clear ownership of outcomes, and integrate AI into core workflows instead of layering it on top,” Domo explained. He warns that when every team starts from scratch, you end up with scattered experiments, not scale.

    ROI Metrics That Convince Boards

    To secure board-level investment, Domo advises focusing on metrics directly tied to financial results and strategic goals. “Boards aren’t convinced by activity. They’re convinced by measurable impact tied to the P&L,” he said. The metrics that matter include cost reduction, revenue lift, and faster decision cycles. Vague reporting like usage or engagement does not work; clear attribution of what changed, by how much, and how it ties to financial outcomes is essential.

    Avoiding AI-Washing and Misaligned Projects

    Organizations avoid AI-washing by starting with the desired outcome, not the tool. “Define the business result first, assign clear ownership of that outcome, and only then determine where AI actually improves the workflow,” Domo advised. Misalignment often happens when teams are incentivized to launch initiatives rather than deliver results. The fix is to tie every AI effort to a measurable objective and hold a single owner accountable.

    Speeding Up AI-Driven Decision-Making

    Speed in AI adoption comes from clearer ownership and fewer handoffs. In large organizations, decisions slow down due to fragmented accountability and excessive alignment steps. Domo recommends defining who owns the outcome, standardizing the inputs those decisions rely on, and reducing the number of required approvals. “AI can surface better insights, but unless the organization is structured to act on them quickly, those insights sit in dashboards,” he noted. Speed comes from aligning decision rights with the people closest to the outcome.