By Anand Mahurkar, CEO & Founder of Findability Sciences
Despite growing enterprise investment in AI, many projects never move beyond the pilot stage. What do you see as the biggest barriers, and how can organizations overcome them?
The pilot-to-production bottleneck isn’t fundamentally a technical hurdle; it’s a structural design issue. Companies typically build pilots simply to demonstrate that a model works rather than verifying whether it can survive real-world P&L conditions—two distinct scenarios requiring completely different success metrics. Gartner projects that at least 30% of generative AI initiatives will be abandoned post-proof-of-concept by the end of 2025 due to inadequate data quality, unclear business value, or rising expenses. Meanwhile, MIT’s Project NANDA revealed that 95% of businesses deploying generative AI recorded zero measurable returns. This does not indicate model failure; rather, it reflects organizations that failed to define “success” prior to development.
The remedy is straightforward in theory yet demanding in practice: select use cases tied directly to a P&L owner from day one, map out the return before writing any code, and treat data readiness as a mandatory prerequisite rather than an afterthought. At Findability, we refuse to approve a pilot unless there is a specific, named business metric it must influence and an accountable individual assigned to drive it. Adhering to this principle eliminates most of the reasons projects quietly stall in pilot purgatory.
How is Findability Sciences helping organizations across industries such as agriculture, manufacturing, retail, and financial services turn AI investments into tangible business outcomes?
Success relies on treating artificial intelligence as an operational backbone for the company instead of treating it as an occasional lab experiment. A prime illustration is our AI Factory for Sugar, developed alongside Grupo Pantaleon, as well as our initiatives with the Agricultural Development Trust in Baramati, where Stoma Sense and Stoma Insight transition mills and farms from reactive habits to predictive strategies. Yield forecasting, crop stress detection, and harvest scheduling are all anchored to metrics the CFO already monitors, avoiding redundant dashboards that go ignored.
This methodology translates seamlessly outside of farming. Within manufacturing, we utilize identical architecture for predictive maintenance and quality drift identification on assembly lines, catching defects ahead of costly stoppages. In retail and quick-service restaurants, Serva Insight delivers store-level operational intelligence compact enough for single-unit operators to implement without lengthy corporate debates, bypassing the need for an internal data science department. Financial services applications focus instead on risk scoring and fraud pattern detection, maintaining the core discipline: ground the deployment where data already resides, tie it to a concrete figure, and scale strictly after that metric moves.
The overarching theme is not simply to deploy more AI, but to apply it exclusively to the select business decisions that genuinely impact revenue, expenses, or risk, holding off on everything else until those core elements succeed.
As Generative AI dominates the conversation, do you think predictive and prescriptive AI are being overlooked? Where do these technologies continue to offer a competitive advantage?
Yes, and it represents an error we will eventually regret. Generative AI captures attention because it is the most demonstrable technology currently available, allowing users to watch content generated in real-time, which understandably captivates boardrooms. Yet, drafting text is rarely where enterprises win or lose financially. Predictive and prescriptive AI—forecasting demand, streamlining supply chains, or identifying failing equipment beforehand—operate quietly, lack visual flair, and align much more closely with balance sheets.
Consider any domain where high-stakes choices are made repeatedly under conditions of uncertainty, such as sugar mills determining harvest windows, manufacturers planning maintenance schedules, banks pricing risk, or retailers managing seasonal inventory. Executed thousands of times annually, a mere 2 to 3 percent boost in accuracy yields compounding financial returns that chatbot interactions cannot match. While Generative AI improves how you communicate decisions, predictive and prescriptive intelligence help you make better decisions initially. Enterprises treating these approaches as mutually exclusive prioritize style over substance. Those pulling ahead utilize both deliberately to address distinct challenges rather than taking sides.
Many organizations are still unsure where to begin their AI journey. What practical advice would you give business leaders looking to move from experimentation to enterprise-wide adoption?
Struggling organizations are typically hindered not by a lack of ambition, but by managing a dozen disjointed pilots instead of a unified program. S&P Global reports that the proportion of enterprises abandoning the majority of their AI projects surged from 17% in 2024 to 42% in 2025—a clear symptom of governance failure rather than technical shortcomings.
The practical solutions are unglamorous yet effective. First, consolidate efforts by selecting three to five use cases with the most direct paths to revenue or savings, and possess the discipline to cancel the rest; maintaining twenty simultaneous pilots strains budgets and divides leadership focus so that nothing actually launches. Second, establish a centralized data foundation once rather than forcing every pilot to rebuild data pipelines independently. This mirrors our ICUPP framework for clients: Infrastructure, Collect, Unify, Process, and Present. It functions as a structured sequence rather than an à la carte menu. Once infrastructure and data unification are solved, subsequent AI deployment accelerates while decreasing in cost, sparing new pilots from repeating past integration taxes. Third, assign a single senior leader to oversee AI outcomes—someone who answers to a quantifiable metric rather than a roadmap, operating free from committee delays. Fourth, subject every AI undertaking to the exact standards applied to any capital expenditure: defined returns, strict timelines, and clear accountability. Enterprise scaling is not simply a larger pilot; it demands a distinct operational discipline resembling a manufacturing floor more than a research facility.
For businesses applying that execution discipline to customer operations, Parloa provides a platform for building, testing, deploying, and monitoring AI agents across voice, chat, email, messaging, and API channels. Its lifecycle approach keeps agent performance, context, governance, and continuous improvement connected, giving teams a more structured path from a conversational-AI experiment to a measurable operating capability.
If you could give one piece of advice to CEOs planning their AI strategy for the next five years, what would it be?
Prevent AI from becoming just another administrative line item. Allow it to function as a mirror that reveals your company’s true operations with unprecedented clarity. Every organization intuitively understands which decisions dictate its future—what to cultivate, what to repair, and what to discontinue—yet historically lacked the instruments to act on that insight swiftly. That dynamic has changed.
In five years, the executives most proud of their accomplishments will not be those who adopted the newest model fastest, but those who leveraged this period to foster honesty, decisiveness, and proximity to their customers. That constitutes the true value proposition: efficiency not for its own sake, but the opportunity to operate an enterprise free from friction and guesswork.
Therefore, my advice takes the form of an ongoing question to revisit annually: if we viewed our enterprise with absolute clarity, what changes would we make, and do we possess the courage to execute them? Build your AI strategy around answering that honestly. All else—the tools, vendors, and roadmaps—remains secondary detail in service of that objective.
