AI. Everyone Has It. Why Are So Many Businesses Still Losing? | 202X Vision

Host: Vishwendra Verma, Founder, GrowthSutra

Expert Panelists:

Preeti Das — Board Member and Entrepreneur | Former CEO, Birlasoft and Spice Telecom
Deepak Bhootra — Industrial Engineer | Commercial Operations and Enterprise Pricing Expert

What’s Really Changing

Q. Let’s begin with an icebreaker question. Both of you have spent decades helping organizations navigate business transformations and drive commercial growth from distinct operational vantage points. If AI has made core intelligence dramatically abundant, what has actually become more scarce and valuable in the enterprise ecosystem?

Preeti Das : When basic intelligence becomes cheap and readily accessible, it turns into table stakes. The real strategic divide shifts entirely to the contextualization of that intelligence. There are distinct capabilities that AI cannot easily replicate today, and these are exactly where human value creates a premium.

The first is judgment—the capacity to ask the right questions to the system, analyze the array of choices it generates, and make the final critical decision on what to execute. The second is organizational context. Every company possesses a deeply unique ecosystem built over years of interaction with their specific customer segments, partners, and user groups. That institutional footprint is not replicable from one firm to another.

This reminds me of a concept highlighted by Satya Nadella regarding a reverse information paradox. In the legacy environment, knowledge traditionally flowed from the seller to the buyer. Today, AI has completely inverted that dynamic. The seller is becoming exponentially richer in automated know-how, while the buyer's baseline understanding is getting averaged out. To protect their unique corporate alpha, buying organizations must consciously build a trusted governance boundary around their judgment, context, and institutional learning so that proprietary strategic advantage stays within their walls.

Deepak Bhootra: To explain this dynamically, let me lean on my background as an industrial engineer. When you analyze processing lines or machinery, everything is governed by throughput. The ultimate processing speed of any automated chain is strictly limited by the slowest machine in the loop. You cannot exceed that threshold without structural failure.

When AI enters the enterprise, it acts as a massive generator of information overload. Corporate teams look at the sheer quantity of output and get incredibly excited, falsely assuming that high volume translates to high quality. The human brain isn’t even capable of absorbing that level of data pace, meaning organizations aren't actually gaining intelligence—they are suffering from acute cognitive overload.

The individuals and enterprises that win are not the ones generating the most automated text; they are the ones who process specific insights at the right pace, within the correct context, and apply real commercial judgment to their advantage. AI might standardize baseline capability across the board, but it will not change the underlying reality of high performance. We often talk about the classic Pareto principle where 20% of the team delivers 80% of the number. AI won’t break that ratio; it will simply alter the scale of human density required to hit it. Instead of needing 50 people to find those top performers, an optimized organization will find them with a lean team of ten.

The Loss of Competitive Alpha

Vishwendra : This brings us to a significant adoption gap observed in the market. While technology expands, recent enterprise studies show that many functional teams are failing to leverage AI to the strategic depths that industry leaders originally anticipated. We frequently hear tech visionaries like Alex Karp, CEO of Palantir, warn that if an enterprise adopts AI casually without a deep underlying thought process, they risk completely losing their "competitive alpha" because they are effectively feeding their proprietary IP back into foundational models.

Preeti, from your experience working with enterprise transformations and boards, where do you see organizations structurally strengthening their alpha, and where are they inadvertently weakening it through poor execution?

Preeti Das : I have observed both dynamics play out vividly across the corporate landscape. The first common failure point occurs at the very top of leadership. When you approach the chairman or CEO of a traditional organization to showcase an advanced AI framework that could fundamentally reshape their industry, their knee-jerk reaction is almost always: "Please take this to my CDO or my CTO." That response reveals a fundamental misunderstanding. They are treating AI as an isolated IT software implementation rather than what it actually is—a total business transformation. Unless the core leadership actively sits at the design table and drives the required change management, the implementation will stall.

I recall an instance with a large insurance firm that approached AI purely as a board-level tick-mark exercise. They had allocated a specific budget, committed to a timeline on paper, and executed the deployment without training the operational staff on how to leverage the output. It was deemed a complete failure simply because no one understood the practical value, and it completely eroded their timeline alpha.

Conversely, I am currently working with a young reinsurance organization—less than five years old—where the founders and the board view AI as their primary innovative edge. They possessed the operational patience to allow complex training models to mature. Today, that systematic investment is structurally transforming their data alignment, module configurations, and risk analysis daily. Younger organizations are naturally agile; they don't suffer from the massive corporate inertia that turns legacy legacy enterprises into the slow-moving "elephants in the room."

Deepak Bhootra : The "elephant in the room" dynamic is incredibly real. Corporate teams consistently sit in executive meetings where a flawed strategy is presented, yet everyone goes along with the flow because challenging the data feels uncomfortable. When you look closely at how sales and business units interact with AI, a highly problematic behavior emerges: people genuinely believe that prompting is equivalent to thinking.

Staff members sit down with a generative system and boast that spending two hours with a tool allowed them to churn out a 17-slide deck or a dense proposal. That is a dangerous confusion between performative productivity and actual commercial performance. Historically, if you had a complex business problem to solve, the rule of thumb was to spend 50 minutes deeply analyzing the problem and 10 minutes executing the solution. AI has completely inverted that ratio. Teams spend 50 minutes executing automated iterations and barely 10 minutes thinking about the core commercial outcome.

My research shows that a staggering 80% of professionals using AI accept the outsourced information as absolute, final judgment and pass it up the chain completely unquestioned. If a digital tool is doing all your analytical heavy lifting and your professionals are adding zero contextual reasoning, it becomes a circular death squad—you are effectively shooting your own commercial validity in the face. We are using highly subsidized technological products whose true long-term operational costs are completely hidden from us. Legacy giants don't win because they retrieve data faster; they win because they interpret information far better than their competition and align their stakeholders around that unique insight.

Re-Engineering Sourcing & Commercial Playbooks

Vishwendra : This structural shift directly challenges our traditional B2B commercial playbooks. For decades, legacy sales methodologies—from SPIN and Challenger to MEDDIC and value selling—were engineered under the fundamental assumption that the seller inherently knows more about the solution domain than the buyer. Today, that assumption is dead. Buyers routinely leverage advanced tools to scrape vendor footprints, evaluate functional metrics, analyze public data, and review technical capabilities long before the first formal introduction takes place.

Deepak, if the customer is already heavily pre-informed, what are enterprise teams actually selling in the modern era, and how must our discovery frameworks adapt?

Deepak Bhootra : The core frameworks themselves aren't going to vanish, but the way we apply them must completely evolve. The novice professional treats a sales playbook like a rigid script, whereas a seasoned commercial leader uses it strictly as a thinking framework. The danger with AI is that it turns strategic frameworks into simple, unthinking compliance checklists, causing professionals to second-guess their own gut instinct and seasoned judgment.

Decades ago, walking into a client meeting meant your value was tied directly to your product knowledge. Today, everyone has access to features. The customer has already used automated tools to parse analyst reports and run comparison grids. They don't want a standard, generic one-hour pitch deck; they want a crisp, hyper-focused 15-minute discussion targeting the precise operational gaps their internal research has highlighted.

Therefore, discovery can no longer focus purely on identifying standard corporate pain. It must focus heavily on organizational alignment. The modern enterprise deal-maker must map out exactly how consensus breaks down inside the buyer's organization. AI is bleeding traditional departmental boundaries—you will frequently receive complex technical questions from procurement and intense financial structures from IT. The future belongs to the professional who treats AI not as a crutch to replace their mind, but as augmented intelligence to sharpen their own commercial reasoning. Think of AI as high-performance sports shoes. The professional who slows down to put the shoes on securely will always outrun the crowd that rushes onto a rocky field barefoot just to simulate rapid motion.

Audience Questions

Q. We received an excellent comment from Sujoy Ghosh during the live stream. He notes that many enterprise buyers utilize completely flawed gauges—such as demanding immediate, short-term ROI metrics—to evaluate futuristic tech adoptions. Consequently, organizations either paralyze themselves or end up automating broken legacy workflows while completely forgetting their core business purpose. How should leadership re-frame this evaluation matrix?

Deepak Bhootra : Sujoy’s point highlights a classic corporate trap. It is exactly like purchasing an expensive gym membership without conducting a single honest analysis of your physical capacity or long-term operational endurance. You get incredibly excited looking at the glossy promotional materials, but the actual process baseline remains entirely unaddressed.

If your core underlying process is fundamentally broken, layering AI on top of it will simply multiply your operational inefficiencies at scale. If AI is a massive force multiplier, leadership must ask: Where is HR in this entire discussion? Why is this consistently treated as a software problem rather than a critical human capital evolution? For fifty years, corporate headers have claimed that human capital is the ultimate asset, yet our corporate accounting models relegate human worth to generic "goodwill" or hidden operational expenses. Leaders need to slow down, analyze their process integrity, and integrate human capital capabilities directly into the transformation blueprint.

Preeti Das : I completely echo that sentiment. When you look at traditional advisory frameworks from firms like McKinsey or Bain, a balanced enterprise strategy rests firmly on four distinct pillars: Strategy, Operational Excellence, Commercial Excellence, and People Excellence.

Pillars like strategy and operational optimization are structurally straightforward to define on paper. Commercial excellence requires deep execution, but the single toughest pillar to master has always been people excellence. Right now, HR is completely missing from the enterprise AI conversation. Every CXO in the boardroom needs to be tightly aligned around what the technology is expected to deliver, and it cannot be measured through lazy metrics like immediate bottom-line reduction.

Q. Following up on that, is there a practical way to structurally preserve high-agency human capabilities when automated tools can handle tasks like drafting client emails, auto-syncing CRM records, and summarizing meeting minutes? What is the one thing a high-agency leader must never delegate?

Preeti Das : The absolute baseline rule is simple: You can delegate tasks to AI, but you can never delegate accountability. Accountability for any commercial decision must rest entirely with a live human being.

Unfortunately, we are seeing professionals treat AI as an accountability shield, presenting automated reports to executives with the defense of, "Well, the AI system generated these options." AI can dramatically accelerate your operational response time, but human judgment is the only mechanism that can compress the actual decision time safely.

Let me share a concrete operational example that occurred recently in the reinsurance domain. A technical team deployed an automated system to read complex incoming proposal slips, extract the parameters, and log them directly into the core system—a straightforward task. However, the team failed to apply human business context to the prompt design.

When a partner firm transmitted a single email containing three separate, bundled commercial opportunities, the automated system suffered a logic breakdown and gobbled the entire data payload into a single corrupted entry. A high-agency human professional understands that partners bundle opportunities to save time. Because the technology team worked in total isolation from human operational context, a major blind spot went completely unnoticed for two months.

Deepak Bhootra: To build on Preeti’s point regarding accountability, I always advise professionals to apply a simple reputational test to their work. My mother used to tell me that whenever you make a significant decision in life, imagine a simple image: If your mother were to read a headline over her morning coffee exposing that you executed a deeply flawed or unethical decision, how quickly would she choke on that coffee?

If you are outsourcing your strategic output entirely to an automated tool without rigorous human validation, you are exposing your enterprise to massive reputational harm and immediate erosion of trust. As an audience member rightly commented, buyers can instantly spot a machine-powered, generic sales pitch, and most high-level executives reject them immediately. Speed might get you considered, but deep trust and human accountability are the only levers that get you chosen.

Q. That brings us to our final question regarding commercial memory. Can an organization truly preserve its strategic judgment at scale in the AI era, or are we simply building faster digital archives for static documents?

Deepak Bhootra: Historically, organizations have confused a cluttered CRM repository or a massive offsite document archive with actual institutional knowledge. Today, companies rush to build vector databases, assuming that scanning hundreds of legacy proposals solves the problem. It doesn’t. The actual strategic reasoning applied by a seasoned executive often remains an absolute black box to the rest of the organization.

We frequently witness scenarios where a legacy leader retires, and the company proudly brings in a young, dynamic successor who is deeply fluent in digital tools. Yet, in the very first high-stakes client meeting, that successor completely fails to interpret the real sentiment in the room or explain the historical nuances behind the account's pricing structure. Why? Because the tribal knowledge accumulated over a decade vanished with the departing leader.

Commercial wisdom is pattern recognition acquired over time—it is the intuitive understanding of subtle operational anomalies long before they ever register on a digital dashboard. Think of it like a sticky doorknob at home. The database tells you the door is locked, but generations of family experience tell you that if you simply lift the key slightly and rotate it at a specific angle, the mechanism clears instantly. AI can optimize data retrieval speed down to milliseconds, but it cannot synthesize human wisdom. Enterprises must actively focus on capturing the commercial reasoning behind the data, rather than just archiving the documents.

Key Takeaways

Shift from Pure Intelligence to Deep Judgment: Automated intelligence is a generic commodity. Sustainable competitive advantage belongs to leaders who apply human contextual judgment to filter out the digital noise.
Prioritize High Agency over Performative Productivity: Churning out volume metrics like multi-slide decks in record time is performative. High-agency professionals stand out by taking absolute ownership of the commercial outcome.
Transform Sales from Information Delivery to Sense-Making: Pre-informed buyers do not need standard product feature education. Modern commercial teams win by driving internal stakeholder alignment and resolving operational complexity for the client.
Balance Response Speed with Strategic Trust: High-speed automation is excellent for initial opportunity qualification. However, complex enterprise commitments require leaders to slow down, validate core process assumptions, and build deep human trust.
Engage HR to Scale Human Capital Value: Business transformation cannot be siloed within IT. Organizations must actively involve human resources to bridge the digital divide, eliminate cognitive overload, and foster institutional learning.
Own Accountability and Protect Alpha: Never allow automated systems to serve as an operational shield for critical business choices. Delegate baseline tasks to your tools, but preserve human reasoning to keep your proprietary strategic edge intact.

Watch The Replay

This Q&A is based on GrowthSutra's "AI. Everyone Has It. Why Are So Many Businesses Still Losing? | 202X Vision" session.

The full video replay, complete with detailed case discussions, is available on GrowthSutra's official LinkedIn and YouTube channels.