Productivity, Projects, Products, and the reality of Productionalization

Today, 04th March 2026, is celebrated in India as the festival of colors. There is palpable excitement all around, lots of love and good wishes, mischiefs, and a healthy doze of intoxication as well — famous as Thandai.

Part 1 of this series of musings (you can read here Code, Clarity, Comprehension, and Caution) narrated the conversations between various leaders in the enterprise in the context of AI infusion into thier day-in-life.

This article and narration takes a collective view of narration between enterprise leaders concerning the changing (unspoken) expectations of clients, the way AI consulting and services firms have operated till now, their offerings, challenges and risks.

Towards the end, this article also puts in several provocative questions to the current CXO roles, organization structure, and how enterprises need to prepare to run in the future.

Caution: It is going to be a long read, there are new characters, interesting conversations, and provocations at every turn. So, grab your thandai and give it a patient read.

When client expectations outpaced delivery capabilities!

Morgan, the company’s prolific Rain Maker sat across from Sumit, the Chief Technology Officer, in a corner office.

“Sumit, we need to talk about a shift in our sales conversations,” Morgan began. “Clients are saying they want productivity improvements, but what they really mean are specific business outcomes.”

Sumit nodded. “So, they’re not just looking for efficiency?”

“Exactly. A retail client asked for productivity across their supply chain but meant a 3x faster time to value within six months. They expect business value delivered, not just Data Modernization,” Morgan explained. This aligns with McKinsey’s 2025 findings, which show that 71 percent of organizations prioritize outcome measurement over activity metrics (McKinsey & Company, 2025).

Sumit leaned forward. “So, they’re using project language but expecting product dynamics.”

“Yes! They talk about fixed project plans but want flexibility to change requirements. They ask for quality deliverables and expect ongoing partnerships without explicitly mentioning managed services. They want continuous value delivery,” Morgan added, referencing Forrester’s 2025 research that found 68 percent of enterprise clients expect continuous deployment even in project-based services (Forrester, 2025).

Morgan continued, He heard from Prashant, the Business Lead for HLS vertical — “A healthcare client said they wanted an AI-powered clinical decision support system. They expected us to adapt as their business evolved, not just implement a unified data model on a modern data platform.”

“And how do we contract for this?” Sumit asked.

Morgan replied, “We need outcome-based contracts. We commit to specific results and engage continuously until those outcomes are achieved. Forrester reports that 73 percent of enterprise buyers prefer outcome-based partnerships over project engagements, even at a higher cost (Forrester, 2025).”

Sumit paused, absorbing the implications. “But that requires a complete restructuring of our delivery model.”

“Yes, and if we don’t adapt, we’ll fall behind. Gartner’s 2025 research found that 40 percent of enterprise projects fail because solutions don’t match business needs, often due to changing requirements during development (Gartner, 2025). Clients expect us to bridge the gap between what they say and what they truly need,” Morgan concluded.

The Unspoken Expectations

As Morgan and Sumit’s conversation ended, the fundamental shifts became clear:

As 2026 unfolded, the business landscape felt like a race where every company was sprinting toward the AI finish line, yet few had defined what victory looked like. The air was thick with urgency, and the stakes were higher than ever. Clients who once requested innovative solutions now demanded immediate results. They wanted products that could seamlessly integrate AI capabilities, not just promises of what was possible. The new mantra was clear: deliver now, or risk losing the competitive edge. Yet beneath the surface, a more fundamental question was brewing — one that would define not just what the company could sell, but what it actually was.

PRODUCTIVITY

Shub the CEO opened the bi-weekly ET call and asked Rachit, the frontline AI Leader to provide his perspectives.

Rachit started saying, “The conversation has shifted, Clients are no longer asking if we can do AI. They’re asking how quickly we can deliver a solution that works in their environment.”

Surya, the CXM Practice Lead, nodded in agreement. “They expect that solution to provide value from day one. No more pilot phases. They want productionalized capabilities that don’t just promise productivity, but deliver it immediately.”

Siva, the Marketing Lead, interrupted — a note of frustration in his voice. “This is exactly the problem I’m wrestling with. Are we selling consulting and services expertise, or are we selling accelerators and products? Because the messaging is completely different, and I don’t know which one we actually are.”

He pulled up a slide showing analyst coverage. “Gartner positions us as a ‘Systems Integrator with emerging AI capabilities.’ Forrester calls us a ‘Services-led AI Transformation firm.’ But our sales team is pitching us like we’re a product company — ‘Deploy our AI accelerators, transform your business in 90 days.’ Which narrative wins?”

Rashmi, the AI Product Engineering Practice Lead, interjected, looking thoughtful. “What we’re forgetting is that effort does not equal output, output does not equal outcome, and outcome does not equal value. We’re thinking in terms of activity and not in terms of client value. Clients don’t want a project completed; they want a solution that drives measurable business results.”

Aravind, the face of AI in the company, leaned back with a pragmatic expression. “Both are true and neither is complete. We are a services company with product-like capabilities. The issue is that clients don’t care about our internal taxonomy. They care about outcomes. And right now, we’re promising outcomes we can’t consistently deliver.”

Sri Harsha, the newly joined Business Lead for Europe and UK, provided an outside perspective. “In Europe, clients are skeptical of ‘AI as a service’ from traditional consultancies. They see it as consulting firms trying to extract margin by repackaging labor. But they trust product companies. So, your positioning problem isn’t marketing — it’s credibility.”

Bhaskar, the SCM Practice Lead, shifted uncomfortably. “What are you saying? That we should become a product company?”

“No,” Sri Harsha replied. “I’m saying you need to decide what you’re optimizing for. Right now, you’re optimizing for sales velocity. But the market is optimizing for delivery credibility. Those two things are in direct conflict.”

Rashmi added, “And that’s precisely the issue. We need to align our definitions of productivity with actual client outcomes. Before AI, we could measure productivity in hours worked or tasks completed. Now, we need to measure it in terms of value delivered. It’s a paradigm shift, and if we don’t recognize it, we’ll keep missing the mark.”

Praveen, a techno-functional enthusiast from the Bay Area, chimed in with impatience. “Listen, while we’re debating our narrative, startups are out here launching products every week that redefine industries! I’ve just come from a pitch event where companies are solving problems in days that we take months to even conceptualize. We’re way behind the curve!”

Ayappa, the people supply chain guy, spoke up — his voice carrying the weight of frustration. “While you’re all talking about productivity and outcomes, I’m sitting here unable to even forecast what we need! How am I supposed to plan recruitment when I don’t know what skills we’ll need in three months? The demand signals are all over the place. One week it’s ‘we need 50 AI engineers,’ the next week it’s ‘we need data governance specialists.’ And the recruitment engine? It’s not firing at all.”

He pulled up a dashboard showing recruitment metrics. “We have 340 open positions. Our average time-to-hire is 127 days. Industry average is 42 days (LinkedIn Talent Solutions, 2025). We’re 3x slower than the market. And with these exotic skill requirements everyone’s talking about, it’s only getting worse.”

Vaishali, the HR leader, looked visibly exhausted as she added, “And that’s just the supply side. On the employee side, we’re creating chaos. Employees don’t know what their career paths look like anymore. They don’t understand how their roles are changing with AI. Compensation is all over the place — some teams expect 100 percent premiums for AI skills, others are not. And benefits? We haven’t even figured out what benefits matter when half of our workforce is remote and distributed.”

She pulled up internal survey data. “Our employee engagement score dropped 18 percent in the last quarter. Exit interviews show that 47 percent of departing employees cited ‘unclear career trajectory in AI era’ as a reason (Society for Human Resource Management, 2025). We’re losing people because we can’t articulate what their future looks like.”

Priya, the yet-to-join Learning and Development lead, interjected with a fresh perspective. “This is exactly where I think we need to pivot our entire approach. Instead of trying to predict skills 18 months out, we need to embrace ‘Fluid Skills’ — the ability to learn, unlearn, and relearn continuously. Just-in-Time training is the mechanism, but Fluid Skills is the mindset.”

She pulled up research. “Deloitte’s 2025 Global Human Capital Trends report found that organizations that invested in ‘adaptive learning’ saw 34 percent higher employee retention and 28 percent higher productivity (Deloitte, 2025). The key is not predicting the future — it’s building the capacity to adapt to it.”

Rashmi, leaning back thoughtfully, offered a different lens. “What Priya’s saying reminds me of quantum physics principles. In quantum mechanics, there’s inherent uncertainty — you can’t simultaneously know both the position and momentum of a particle with perfect precision. But that uncertainty doesn’t mean chaos. There’s actually an ‘ordered chaos’ where patterns emerge from apparent randomness.”

She continued, “We’re trying to apply classical physics thinking — deterministic planning, predictable outcomes — to a quantum problem. We need to embrace the uncertainty while building structures that allow order to emerge from it. That’s what Fluid Skills enables.”

Aravind nodded slowly. “That’s… actually profound. But also slightly terrifying from an execution standpoint.”

Several people in the room rolled their eyes.

Yogesh, the Resource Management Lead, said bluntly, “With respect, we need to solve real problems the way we solve them — with data, process, and accountability. Not with quantum metaphors. Ayappa needs to know how many people to hire. Vaishali needs to know what to tell employees about their futures. We can’t just say ‘embrace uncertainty and order will emerge.’”

Rashmi smiled knowingly. “Fair point. But the quantum framework actually helps us operationalize this. In quantum systems, we use probability distributions and wave functions to describe possible states. We can do the same here — instead of hiring for specific roles, we hire for ‘skill clusters’ with defined probability distributions. Instead of fixed career paths, we define ‘career probability clouds’ where multiple futures are possible.”

Aravind and Priya jumped in, “Exactly! And that’s where Fluid Skills becomes operational. We identify core ‘anchor skills’ — things like problem-solving, learning agility, domain fundamentals — that remain stable. Then we layer on ‘fluid skills’ — skills that change based on project needs. We train people in anchor skills deeply, and fluid skills through JIT mechanisms.”

PROJECTS

Aman, the vertical lead for Retail, felt the pressure of the shifting client landscape. “Retail clients are demanding personalized experiences powered by AI — now,” he said, his tone reflecting urgency. “They want projects that can pivot quickly based on real-time data and consumer behavior. But here’s the problem: we’re committing to timelines we can’t meet.”

Majaz, the vertical lead for CPG, leaned back, arms crossed. “That’s a double-edged sword. We can’t throw resources at every new request. We need a strategy that balances client expectations with our delivery capabilities. But right now, sales is making promises that delivery can’t keep.”

Shashank the CRO interjected. “This is where we need to apply our thinking more critically. We’re falling into the trap of believing that simply completing a project means we’ve succeeded. But if that project doesn’t lead to a significant outcome for the client, then what have we really achieved? We need to focus on the end value we deliver, not just the work we complete.”

Praveen, cutting in with urgency, exclaimed, “Exactly! While we’re still debating feasibility and timelines, other companies are launching MVPs and iterating based on customer feedback in real-time! We need to adopt that mindset or risk being left behind.”

Arpan, the vertical lead for BFS, worried aloud. “I want to grow my vertical but if we commit to too many deliverables without the risk assessment, we’ll end up with more failures than successes. Our reputation is at stake. And in financial services, reputation is everything.”

Shub pointed to a report, “Gartner’s 2025 report on AI implementation found that 40 percent of enterprise AI projects fail to move beyond the pilot phase (Gartner, 2025). The primary reason? Misalignment between what was promised and what could be delivered. That’s us, right now.”

Sambhram, the delivery lead for Retail, added frustration to the mix. “And we’re not just failing on timeline. We’re failing on scope creep. Clients see what AI can do in demos, so they keep asking for more. We keep saying yes because sales told them yes. But our teams don’t have the skills or bandwidth.”

Pavan the AI transformation lead responded, “This is where we need to draw a clear line in the sand. We must communicate to clients that more features do not equal more value. A streamlined solution that meets their primary needs is far more valuable than a bloated one that misses the mark.”

Karthik, the Sales Excellence Lead, defended his position. “Sales is doing what sales does — identifying opportunity. But we need to give them a playbook for what we can actually deliver. Right now, they’re improvising, and it’s creating chaos.”

Praveen added, “While we’re focused on playbooks, startups are already defining new playbooks every day! We need to be agile and responsive to market changes, not stuck in bureaucratic processes.”

Vaishali interjected, “And from an HR perspective, this project chaos is killing our people. We have team members stretched across three, four, sometimes five projects simultaneously. They don’t know who their manager is. They don’t know what their performance expectations are. How am I supposed to manage their career development when they’re in constant flux?”

She pulled up data. “Research from the American Psychological Association shows that role ambiguity is one of the top drivers of burnout (American Psychological Association, 2025). We’re creating an environment where ambiguity is the default state.”

Priya suggested, “This is where project-based Fluid Skills training becomes critical. Instead of trying to assign people to stable roles, we need to help them navigate fluid project assignments. We need to provide JIT training that helps them quickly ramp up on new skills as they move between projects.”

PRODUCTS

Prashant, the vertical lead for HLS, raised a crucial point. “We need to redefine our service offerings. Instead of one-off projects, we should aim for comprehensive solutions that integrate AI across the client’s entire operation. But that requires us to ensure our delivery teams are aligned and capable.”

He pulled up a competitive analysis. “Our competitors are doing this. Accenture launched ‘Accenture AI’ as a unified product platform. Deloitte has ‘Deloitte AI Institute’ with standardized offerings. We’re still selling projects. That’s not scalable.”

Siva jumped in. “Every time we confuse effort with value, we risk our credibility. Before AI, we could afford to focus on processes. Now, we need to focus on outcomes. If we don’t change our definitions of success, we’ll lose the opportunity AI presents.” He continued, product companies have recurring revenue, predictable margins, and brand value. Services companies have project revenue, variable margins, and relationship value. We’re trying to be both, and it’s confusing everyone.”

Girish, the delivery lead for CPG, argued the practical reality. “But we don’t have a product platform yet. We have tools and frameworks, but not a cohesive product. Building one would take 18 months. Sales needs revenue now.”

“That’s the trap,” Sumit said bluntly. “You’re optimizing for short-term revenue at the cost of long-term positioning. In 18 months, your competitors will have products, and you’ll still be selling services. And services margins are being compressed by AI.”

Shub pulled up a research. “BCG’s 2025 research on workforce reskilling found that organizations using ‘structured reskilling programs’ achieved 67 percent success rates in transitioning people between roles, versus 23 percent for organizations that just hoped people would figure it out (Boston Consulting Group, 2025). The difference is intentional, systematic upskilling.”

Rekha the CHRO added, “And from a compensation perspective, we need to be clear about this transition. If we’re moving people from services to products, their comp structure changes. Their career paths change. We need to communicate this clearly and offer genuine opportunities for people to grow into these new roles.”

PRODUCTIONALIZATION

Arnab, the Data Engineering Practice Lead, brought the conversation to the operational reality. “None of this matters if we can’t actually productionalize AI. And right now, we’re struggling.”

He pulled up a dashboard showing project health metrics. “Of our 47 active AI projects, 31 are in ‘data preparation’ phase. That means they’re not even at the point where they can deploy AI. They’re still trying to get clean, labeled, governance-compliant data. That’s taking 3–4 months per project.”

Yogesh, the Resource Management Lead, added context. “And we’re stretched thin. We have 230 people trained in AI across the company. We have 340 projects that need AI expertise. That’s a 1.5x oversubscription ratio. People are burning out.”

Karthik, the Sales Excellence Lead, interjected with urgency. “But we have $85 million in pipeline for AI-related work. If we don’t resource it, we lose the revenue.”

“You’ll lose the revenue anyway if you can’t deliver,” Pratap said quietly. “Failed projects don’t close — they just extend and drain resources.”

Vaina the Delivery Excellence Lead interjected again, his voice firm. “We must recognize that productionalization isn’t just about completing tasks; it’s about ensuring we’re delivering the right outcomes. If we can’t do that, we’re just spinning our wheels.”

Sambhram added a ground-level perspective. “The problem is that we’re treating AI productionalization like any other project. We’re assigning a project manager, setting a timeline, and hoping for the best. But AI projects are fundamentally different. They require continuous iteration, model retraining, data quality monitoring. It’s not a one-time delivery — it’s an ongoing operation.”

He emphasized, “We cannot afford to view productionalization as merely a phase in a project. It’s a continuous cycle that demands our attention long after the initial launch. If we miss that, we’re failing our clients.”

Ayappa, still grappling with the complexity, said, “So productionalization requires a completely different operating model than what we have. Different skills, different organizational structure, different career paths. And I’m supposed to figure out how to staff this? I don’t even know what the roles are yet!”

Rashmi brought the quantum framework back. “This is the ‘ordered chaos’ I was talking about. We can’t predict exactly which skills we’ll need in six months. But we can define the probability distributions — the likely skill clusters. We can build organizational structures that allow people to move fluidly between these clusters. And we can create learning systems that enable that fluidity.”

Abhijay the HLS delivery lead, still skeptical, said, “But this requires investment. Training budgets, learning platforms, mentorship structures. Where does that money come from?” He looked at Pratap the CFO for a confirmation!

“From the productivity gains you’ll realize,” Pratap replied. “If you can reduce the people cost by hiring for potential and training internally for skills fitment, that’s a massive saving. If you can reduce project failure rates and overruns from 40 percent to 20 percent by having better-trained teams and tools, that’s enormous. The investment pays for itself.”

He pulled up analysis. “McKinsey’s 2025 research found that companies investing 1.5 percent of payroll in reskilling realized 3.5x return on that investment within 24 months (McKinsey & Company, 2025). This isn’t charity. This is business mathematics.”

THE MANAGED SERVICES AWAKENING

Aditya, the Managed Services Lead, had been silent throughout most of the conversation. Now, he leaned forward, his expression serious and thoughtful.

“I need to say something,” he began, his voice measured. “Everything we’ve been discussing — products, services, positioning, talent — it all misses a critical piece of the puzzle. And that piece is managed services.”

The room turned to look at him.

“Today, we don’t even position managed services as a strategic offering,” Aditya continued. “It’s an afterthought. A checkbox. ‘Oh, we also do managed services.’ But in an AI-infused world, managed services becomes the foundation for sustainable growth and client stickiness.”

He pulled up data from TSIA and recent market research. “The Technology Services Industry Association’s 2026 State of Managed Services report found that organizations moving to outcome-based managed services models saw 3.2x higher customer lifetime value and 47 percent lower churn (TSIA, 2026). But here’s the problem — most traditional consulting firms treat managed services as a low-margin commodity. We’re not positioned to win in this space.”

Sumit leaned forward, interested. “Go on.”

“Managed services in the AI era is fundamentally different from what we’ve done before,” Aditya explained. “Before, managed services meant ‘we’ll keep your systems running.’ It was about uptime, ticket resolution, incident management. The KPIs were: mean time to resolution, availability percentage, cost per ticket.”

He pulled up a slide showing the old model. “But that’s transactional. That’s not sticky. That’s not defensible against offshore competitors who can do it cheaper.”

Pratap interjected, “So what’s different now?”

“Everything,” Aditya said. “In an AI-infused world, managed services becomes about continuous value delivery. It’s about helping clients realize ongoing value from their AI systems. It’s about monitoring model performance, retraining models when data drifts, optimizing prompts, identifying new use cases, managing governance and compliance.”

He pulled up a framework. “Here’s what AI-infused managed services looks like:

1. Outcome-Based Service Levels — Instead of ‘we’ll resolve tickets in 4 hours,’ it’s ‘we’ll maintain model accuracy above 94 percent’ or ‘we’ll identify and implement three new AI-enabled use cases per quarter.’

2. End-to-End Service Offering — Not just maintaining the AI system, but integrating it with the client’s entire technology ecosystem. ERP systems, CRM systems, data warehouses, business intelligence platforms. The AI system only delivers value if it’s connected to the systems that matter.

3. Tools and Frameworks — We need proprietary tools for model monitoring, data quality assessment, prompt optimization, use case discovery. These tools become defensible IP that clients become dependent on.

4. Deep Domain Capabilities — We can’t be generic. We need to specialize. ‘Retail AI Operations,’ ‘CPG AI Operations,’ ‘Financial Services AI Operations.’ Deep domain knowledge combined with AI expertise.”

He continued, “And critically, we need strategic partnerships with ERP vendors. SAP, Oracle, Microsoft. These systems are the core of every enterprise. If we can integrate our AI capabilities directly into their platforms, we become indispensable.”

Sri Harsha nodded. “This is exactly what I’m seeing in Europe. The winners in AI services are not the ones selling one-off AI projects. They’re the ones building ongoing, integrated relationships where AI becomes embedded in the client’s operational fabric.”

Aditya added, “And the business model is completely different. Instead of project revenue that ends when the project closes, we have recurring revenue from managed services. Instead of margin compression from commoditization, we have defensible margins from proprietary tools and domain expertise.”

He pulled up market data. “A 2025 Forrester study found that organizations with ‘integrated managed services models’ achieved 2.8x higher gross margins on AI services compared to project-based models (Forrester, 2025). And they had 5.2x longer average customer tenure.”

Pratap asked, “But how does this fit into our current business model? We’re structured around projects, not operations.”

“That’s the challenge,” Aditya acknowledged. “We need to build a completely separate operating model for managed services. Different organizational structure. Different skill profiles. Different incentives. Different P&L.”

He outlined the structure. “We need:

1. AI Operations Centers — Regional hubs focused on 24/7 monitoring and optimization of client AI systems.
2. Domain Specialists — People who understand both AI and the specific industry vertical. A CPG AI operations specialist understands not just machine learning, but demand planning, supply chain optimization, pricing analytics.
3. Partnerships Team — Dedicated to building and maintaining relationships with ERP vendors, technology partners, integrators.
4. Tools and Platform Team — Building proprietary software for model monitoring, governance, use case discovery.”

Karthik asked, “But how do we sell this? Clients don’t think about ‘managed services for AI’ yet. They think about ‘AI projects.’”

“We need to educate the market,” Aditya said. “We need to show clients the value of ongoing AI operations. We need to position managed services not as a support function, but as a strategic capability that enables them to extract continuous value from their AI investments.”

He pulled up a case study framework. “We start with clients who have already deployed AI. We show them: ‘Your model accuracy is drifting. Your data quality is degrading. You’re missing new use cases. You need ongoing operations.’ We offer a managed services engagement. We show them value. We build the relationship.”

Rashmi added, “And it aligns with the outcome-based thinking we’ve been discussing. Instead of measuring success by ‘did we complete the project,’ we measure it by ‘are we delivering continuous value.’ That’s a fundamentally different mindset.”

THE POSITIONING PARADOX

Siva called an urgent meeting with Rashmi, Sri Harsha, and now Aditya to dig deeper into the market perception problem.

“I’ve been analyzing how analysts, clients, and employees perceive us,” Siva said, pulling up research. “And the picture is fragmented.”

He showed three different narratives:

Analyst View: “Traditional consulting firm with emerging AI capabilities, competing in the Systems Integration space.” (Gartner Magic Quadrant for IT Services, 2025)

Client View: “A services firm that talks about AI but delivers traditional consulting wrapped in AI language.”

Employee View: “A company trying to be a startup but run like a consulting firm. Leadership doesn’t know what we are.”

Morgan in his characteristic style leaned back and started saying “Nehi, Nehi, Nehi Boss”. “This is the core problem. Your positioning is incoherent. And incoherent positioning kills three things: brand value, talent attraction, and pricing power.”

He continued, “And it’s especially damaging in managed services. Clients don’t trust us to run their AI operations because we don’t have a clear story about who we are. They see us as a project shop, not an operations partner.”

Arnab added, “And this confusion leads to misalignment in our projects and productivity. If we can’t articulate who we are, we can’t deliver value. It’s essential to reframe our narrative around outcomes and value rather than just efforts and outputs.”

Siva asked the direct question. “So what should we be?”

“That’s not a marketing question,” Rakesh the Growth Officer said. “That’s a business strategy question. And it requires leadership to make a choice.”

Sri Harsha added, “In Europe, I’d recommend this: Position as a ‘Business Outcomes Company powered by AI.’ Not an AI company. Not a consulting firm. A company that uses AI to deliver measurable business outcomes. And critically, we deliver those outcomes through three integrated services: project delivery, product platforms, and managed operations.”

Pratap the CFO nodded. “That’s the key — integration. We’re not three separate businesses. We’re one integrated business model where:

• Projects deliver initial value and AI capabilities
• Products provide scalable, repeatable solutions
• Managed services deliver ongoing value optimization

Arnab jumped in to support the narration saying, “clients see us as a partner who helps them achieve outcomes, not a vendor who sells them services.”

Siva began taking notes. “So the positioning is: ‘Business Outcomes Company powered by AI’ with three integrated delivery models. That’s actually coherent.”

“But it’s only coherent if we can actually execute it,” Sambhram and Girish said almost at the same time. “And right now, we can’t. We need to:

1. Get serious about managed services as a strategic offering
2. Build the organizational capability to deliver it
3. Establish partnerships with ERP vendors
4. Develop proprietary tools and frameworks
5. Train people in domain-specific AI operations
6. Change how we measure success — from projects completed to outcomes delivered”

Abhijay emphasized, “And we need to start now. The market window is closing. In 18 months, the players who have built credible managed services practices will have defensible positions. The ones who haven’t will be commodity project shops.”

THE RISK CONVERSATION

Arpan, the vertical lead for BFS, raised a critical concern that had been building.

“We need to talk about risk,” he said. “Not just business risk. Regulatory risk. Reputational risk. Legal risk.”

He pulled up recent regulatory actions. “The SEC has started examining how financial services firms are using AI in advisory and decision-making. The EU AI Act is now in effect, with penalties up to 6 percent of global revenue for high-risk AI misuse (European Commission, 2024). And we’re out here promising AI solutions without clear governance frameworks.”

Prashant, the vertical lead for HLS, added a healthcare perspective. “In healthcare, the FDA is now requiring documentation of AI validation for diagnostic and treatment recommendation systems. If we deploy an AI system that makes an incorrect recommendation and a patient is harmed, we have liability exposure.”

Sri Harsha added international context. “In Europe, regulators are ahead of the market. They’re actively examining AI implementations. If you have clients in regulated industries in Europe, you need to ensure your AI systems are compliant before deployment, not after.”

Prashant continued, “And this is where our managed services model can actually provide a competitive advantage. By offering ongoing operational oversight, we can create a robust governance framework around client AI systems. We can help them navigate regulatory requirements, monitor compliance continuously, and ensure that their AI systems are operating within legal boundaries.”

Arpan nodded. “Exactly. If we establish ourselves as a trusted partner in compliance and governance, we not only mitigate risk for our clients but also solidify our position as indispensable.”

Manoj the CISOP added, “And we need to be transparent with clients about the risks. They need to understand that while AI can provide immense value, it also comes with significant responsibilities. Our role is to guide them through that landscape.”

Manoj concluded, “This is about positioning ourselves as not just a vendor, but a partner in their AI journey. We need to create a narrative where our managed services are framed as essential to their success — not just for operational efficiency, but for ensuring compliance, reducing risk, and driving sustained value.”

THE PATH FORWARD

As the meeting drew to a close, Shub summarized the strategic challenges ahead. “We’re at an inflection point. We can continue on the current path — selling ahead of our capabilities, stretching our teams, hoping things work out. Or we can make hard choices about who we are and what we can deliver.”

Shub laid out the options. “Option 1: Double down on services. Invest in delivery excellence, standardize our processes, build a strong delivery reputation. This means slower growth but sustainable. We become the ‘reliable AI services firm.’

Option 2: Pivot to products. Build a platform, invest in productionalization, compete on scale and repeatability. This means 18–24 months of low revenue while we build, but higher long-term margins. We become the ‘AI platform company.’

Option 3: Do both. Maintain a services business while building products on the side. This is the hardest path — it requires discipline, clear resource allocation, and honest prioritization. But it’s possible.”

Sumit added, “And we need to integrate managed services into whichever option we choose. It’s not just an add-on; it’s a critical component of our offering that can drive sustainable growth and deeper client relationships.”

Shashan summarized the European perspective. “You need to move fast. The window for traditional consulting firms to establish credibility in AI is closing. In 18 months, the market will have decided who the winners are. You need to be decisive now.”

Pavan the AI transformation lead concluded, “And we need to embrace the uncertainty and the ordered chaos that comes with it. If we can build an adaptable, responsive organization that prioritizes learning and continuous value delivery, we will not just survive; we will thrive. That’s the future we need to build together.”

The CXO Reckoning: When Algorithms Run the Enterprise, Who Leads the Enterprise?

A Critical View of Executive Leadership

Soum the so-called Chief Strategy Officer and Innovation Officer has been quietly listening to the entire conversation. He finally opened up and started with a provocative opening comment.

The traditional C-suite hierarchy — CEO at the top, CFO managing money, CTO managing technology, CHRO managing people — was designed for a world where humans made decisions and technology supported them. That world no longer exists.

There was a visible awkward silence in the room. Soum, in his usual style took a dramatic pause and wanted the message to sink in. Then he continued.

Today, algorithms are making enterprise decisions. Machine learning models optimize pricing, predict customer behavior, allocate resources, and detect fraud faster and more accurately than any human executive team ever could. Yet our organizational structures remain frozen in a pre-AI era, with executives still pretending they’re in control.

This is not a minor misalignment. This is an existential crisis for the C-suite.

The CEO is Becoming Obsolete (Or Should Be)

The Chief Executive Officer was designed to be the ultimate decision-maker. The person who set strategy, made calls, and drove organizational direction. But in an AI-driven enterprise, the CEO’s role has fundamentally changed — and most CEOs haven’t realized it yet.

Algorithms now set pricing strategy more effectively than any CFO. Machine learning models predict market trends with greater accuracy than any strategist. Predictive analytics identify customer churn before any sales leader sees it coming. The CEO is no longer the decision-maker. The CEO is now a decision-validator — someone who ensures that algorithmic decisions align with organizational values and stakeholder interests.

But here’s the problem: most CEOs aren’t trained for this role. They’re trained to make decisions, not to validate them. They’re trained to drive strategy, not to ensure that AI-driven strategy preserves human dignity and organizational culture.

The result? Soum looked at Shub and emphasized, CEOs are becoming figureheads in their own organizations, rubber-stamping algorithmic decisions they don’t fully understand, made by systems they can’t fully control.

The CFO is Drowning in Data They Can’t Interpret

Soum then moved on to narrate the situation for a good friend of his, the CFO. The Chief Financial Officer was the keeper of capital and the guardian of financial truth. But in an AI-driven world, the CFO is drowning in data.

Real-time financial analytics now generate thousands of insights every second. Predictive models forecast cash flow with granular precision. Automated systems detect anomalies and flag risks instantly. The CFO has access to more financial information than ever before — but less time to interpret it, less ability to understand it, and less authority to act on it.

The CFO’s traditional role — analyzing financial data and making capital allocation decisions — is being automated away. What remains is oversight and governance. But most CFOs are still trying to do the analysis themselves, competing with machines they can’t beat.

Soum urged, the CFO of tomorrow needs to be a governance architect, not a financial analyst. They need to understand how algorithmic systems make financial decisions, ensure those decisions are ethical and compliant, and maintain human oversight over automated capital allocation. But almost no CFO is trained for this role.

The CTO is Building Systems They Don’t Control

Soum, waved at Sumit and continued, the Chief Technology Officer was the architect of the enterprise technology stack — the person who decided what systems to build, how to build them, and how they would work. But AI has shattered that illusion of control.

Today’s CTO is building systems powered by large language models trained on data they didn’t collect, using algorithms they don’t fully understand, producing outputs they can’t always predict or explain. The CTO is no longer the architect. The CTO is now a systems integrator — someone who connects pre-built AI components and hopes the results align with organizational needs.

This creates a fundamental problem: when an AI system produces a biased result, or makes a decision that harms a customer, or violates a regulation, who is responsible? The CTO built the system, but didn’t build the underlying models. The CTO integrated the components, but didn’t control the data. The CTO deployed the solution, but can’t fully explain how it works.

The CTO of tomorrow needs to be an AI governance specialist, not a technology architect. They need to understand how to audit algorithmic systems, ensure they’re fair and explainable, manage the risks of AI-driven automation, and maintain human oversight over technological change. But this is not what CTOs are trained to do.

The CHRO is Managing Yesterday’s Workforce

Soum took a pause, made eye contact with Rekha and said, here is where the real crisis lies.

The Chief Human Resources Officer manages compensation, benefits, hiring, and culture. These are 20th-century HR functions. But in an AI-driven enterprise, the CHRO’s job has become irrelevant — or should be transformed into something far more powerful.

Algorithms now optimize workforce allocation. Machine learning models identify the best person for every role, predict which employees will leave, and recommend compensation adjustments in real-time. Chatbots handle employee questions. Automation handles payroll, benefits administration, and compliance. The traditional CHRO functions are being automated away.

But here’s what’s happening instead: the CHRO is becoming irrelevant at the strategic level, while the organization is losing its soul.

When algorithms make hiring decisions, they optimize for measurable factors: skills, experience, credentials. But they can’t measure empathy, creativity, cultural fit, or leadership potential. When algorithms optimize workforce allocation, they maximize utilization and minimize cost. But they can’t preserve organizational culture, develop human potential, or create meaning in work. When algorithms drive organizational change, they optimize for efficiency. But they can’t preserve the human dignity and psychological safety that enable organizations to thrive.

Soum catching his breath raised an uncomfortable point, the result is organizations that are increasingly efficient but increasingly hollow. Workforces that are optimized but demoralized. Systems that work perfectly but create no value that humans actually care about.

He continued, here’s the provocative idea: in an AI-driven enterprise, the Chief Human Resources Officer should become the Chief Executive Officer.

Not because the CHRO should make all the business decisions. The algorithms will do that. But because in a world where algorithms run the enterprise, the most critical executive function is preserving what makes the organization human. This is not a minor role adjustment. This is a complete inversion of the traditional C-suite hierarchy.

The Other CXO Roles in the New Order

If the CHRO becomes CEO, what happens to the other C-suite roles? Pretty much every leader in the rook looked expectantly at Soum. He continued.

The Chief Technology Officer becomes the Chief AI Governance Officer. Responsible for ensuring that all AI systems are fair, explainable, compliant, and aligned with organizational values. Not building technology, but governing it.

The Chief Financial Officer becomes the Chief Value Officer. Responsible for ensuring that algorithmic financial decisions create sustainable value, not just short-term profit. Measuring success not just in dollars, but in stakeholder value, organizational resilience, and long-term viability.

The Chief Executive Officer becomes the Chief Strategy Officer. Responsible for setting the direction that algorithms execute, ensuring that technological capability is aligned with organizational purpose, and making the judgment calls that algorithms can’t make.

The Chief Revenue Officer becomes the Chief Experience Officer. Responsible for ensuring that every algorithmic interaction with customers preserves and enhances the human relationship, rather than replacing it with efficiency.

The Uncomfortable Truth

Soum, delivered his final few provocative concluding remarks.

The executives who will lead in the AI era are the ones who can accept that machines will run the enterprise, and focus instead on ensuring that the enterprise serves human purposes. They’re the ones who can let go of the illusion of control and focus instead on the reality of influence. They’re the ones who can measure success not just in efficiency and profit, but in human flourishing and organizational purpose.

This requires a fundamental shift in how we think about executive leadership. It requires humility — the recognition that algorithms are better at many things than humans are. It requires wisdom — the understanding that better is not always good, and efficiency is not always valuable. And it requires courage — the willingness to restructure organizations around principles that most executives have never been trained to lead.

The question is not whether this transformation will happen. The market forces driving it are too powerful. The question is whether executives will lead this transformation deliberately, or whether they’ll be swept aside by it.

References

American Psychological Association. (2025). Workplace stress: The impact on employee wellbeing. https://www.apa.org/science/about/publications/workplace-stress

Boston Consulting Group. (2025). Are you generating value from AI? The widening gap. https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap

Deloitte. (2025). 2025 Global Human Capital Trends. https://www2.deloitte.com/us/en/insights/focus/human-capital-trends.html

European Commission. (2024). Artificial intelligence act. https://digital-strategy.ec.europa.eu/en/policies/eu-artificial-intelligence-act

Forrester. (2025). The state of enterprise AI. https://www.forrester.com/report/the-state-of-enterprise-ai

Gartner. (2025). Winning the last mile: Transforming AI services delivery team performance. https://www.gartner.com/en/documents/7057598

LinkedIn Learning. (2025). 2025 Workplace Learning Report. https://learning.linkedin.com/blog/learning-and-development/2025-workplace-learning-report

ManpowerGroup. (2026). Talent Shortage Survey. https://www.manpowergroup.com/en/news-releases/news/global-talent-shortage-reaches-turning-point-as-ai-skills-claim-top-spot

Society for Human Resource Management. (2025). 2025 Talent Management Survey. https://www.shrm.org/hr-today/trends-and-forecasting/research-and-surveys/pages/2025-talent-management-survey.aspx

TSIA. (2026). State of Managed Services. https://www.tsia.com/blog/state-of-managed-services-2026-ai-economics

Cornerstone. (2025). Future of executive search: Trends shaping CXO hiring in 2025–2026. https://cornerstone.co.in/future-of-executive-search-trends-shaping-cxo-hiring/

Eightfold. (2025). AI transformation: The new role of the CHRO — Talent survey. https://eightfold.ai/learn/ai-transformation-new-role-chro-talent-survey/

Gartner. (2025). Unlocking AI value in HR and the enterprise. https://www.gartner.com/en/articles/ai-in-hr

IMD. (2025). AI and the CXO: Redefining customer experience. https://www.imd.org/ibyimd/artificial-intelligence/ai-and-the-cxo/

Josh Bersin. (2025). The pivotal role of Chief HR Officer in AI transformation. https://joshbersin.com/2025/10/the-pivotal-role-of-chief-hr-officer-in-ai-transformation/

Kavi Global. (2025). Leadership in the age of AI: What every CXO must know. https://www.kaviglobal.com/blogs/leadership-in-the-age-of-ai-what-every-cxo-must-know-2

LinkedIn. (2025). How AI will transform CxO roles in 2025. https://www.linkedin.com/pulse/how-ai-reshape-job-cxos-2025-leading-through-strengths-lawrence-yong-fbrfc

LinkedIn. (2025). HR’s role evolves with AI: From execution to decision enablement. https://www.linkedin.com/posts/drkerkvliet_reimagining-hr-in-an-ai-enabled-organization-activity-7426978778255495168-J5UF

LinkedIn. (2025). HR’s strategic role in the AI era. https://www.linkedin.com/posts/ingevanbelle_futureofwork-hrleadership-ai-activity-7430183513846489088-Rw2v

McKinsey & Company. (2025). Superagency in the workplace: Empowering people to unlock AI’s full potential at work. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

SAP. (2025). People-machine blend: How CHROs can lead agentic AI adoption. https://www.sap.com/germany/blogs/how-human-resources-officers-lead-agentic-ai-adoption

SHRM. (2025). The CHRO & CIO: Reinventing work for the AI era. https://www.shrm.org/executive-network/insights/people-strategy/chro-cio-reinventing-work-ai-era

The Harbinger Group. (2025). Leadership in the agentic AI era: Strategies for business success. https://www.harbingergroup.com/blogs/leadership-in-agentic-ai-era-strategies-for-business-success/

IIT Kanpur EICTA. (2025). Why every CEO and CXO needs to learn AI in 2026. https://www.eicta.iitk.ac.in/knowledge-hub/artificial-intelligence/ai-for-ceo-and-cxo

IIT Kanpur EICTA. (2025). Future of CXO roles in the age of AI. https://www.eicta.iitk.ac.in/knowledge-hub/artificial-intelligence/future-of-cxo-roles-in-the-age-of-ai