Business Model Innovation: A Profitable Path Forward in Uncertain Times

In today’s volatile business landscape, marked by economic shocks, geopolitical tensions, and accelerated technological disruption, how can CXOs forge a profitable path forward for their enterprise?

According to BCG and MIT Sloan, while 90% of executives believe AI will transform their business, only 11% have scaled it successfully. This disconnect reveals a deeper challenge: legacy business models are ill-equipped to support innovation at scale. As customer expectations shift and digital-native competitors rewrite industry rules, leaders must ask: Are we truly differentiated? Are we structured to capture value from AI? Are our activities aligned to win, not just compete?

The answer lies in reconfiguring the business model—not to chase disruption, but to harness it—unlocking new pathways to growth, profitability, and lasting strategic advantage.

Rethinking Innovation

Contrary to the belief that market leaders owe their dominance to pioneering innovations or massive research investments, reality tells a different story. Cisco, for example, maintained a lean R&D spend but outmanoeuvred tech giants like AT&T’s Bell Labs by strategically acquiring and integrating technologies. Similarly, Apple revolutionized the MP3 market—not by inventing it, but by crafting a seamless experience through iTunes and the iPod ecosystem, fundamentally transforming the way the world consumed music. TikTok didn’t invent short-form video but leveraged an AI-driven content discovery model to disrupt global entertainment and advertising.

These examples underline a critical insight: Innovation doesn’t have to start with superior technology—it can start with reconfiguring existing inventions around the customer's unmet need.

The Four Dimensions of Business Model Innovation

To innovate meaningfully, companies must rethink the foundational elements of their strategy:

1. Value Proposition

A compelling product and/ or offering must deliver a 10X advantage over alternatives to disrupt the market. Southwest Airlines simplified air travel with low-cost, point-to-point routes, while Walmart offered unbeatable prices by optimizing its supply chain. Tesla redefined the EV market—not by being the first to build electric cars, but by providing a holistic ecosystem: more extended battery range, over-the-air software updates, a direct-to-consumer sales model, and a proprietary Supercharger network. This product and business model innovation integration delivered a radically superior ownership experience, making EVs aspirational and mainstream, something legacy automakers had struggled to achieve.

2. Delivery as a Differentiator

How a product is delivered can be a unique source of value. Instacart, for instance, has differentiated itself in the crowded grocery delivery space by building deep integrations with major retailers, deploying AI to optimize picking and delivery routes, and offering ultra-flexible delivery windows—even within 15–30 minutes in some locations. Rather than owning inventory, Instacart acts as a high-efficiency digital layer over existing supermarkets, enabling scale without physical overheads. This smart orchestration of delivery—not just speed, but convenience and control—has helped it win share in a market where customer expectations for fulfilment are increasingly high.

3. Target Market Clarity

Understanding your customer is essential. Disney+ succeeded not by indiscriminately targeting the entire streaming market, but by zeroing in on families, nostalgic millennials, and fans of legacy franchises like Marvel, Star Wars, and Pixar. Unlike Netflix’s broad “something for everyone” approach, Disney+ built its content library, user experience, and brand messaging around its core audience, offering curated, safe, family-friendly content with a premium storytelling edge. This tight focus allowed Disney+ to quickly build a loyal subscriber base, cross-sell within its ecosystem (e.g., merchandise, parks, theatrical releases), and achieve explosive early growth without diluting its brand.

4. Revenue and Cost Structure

Business model innovators often redefine how value is monetized. Consider the razor-and-blade model used by Gillette and HP, where core products are sold at low cost to drive recurring profits from ancillary products. Peloton disrupted the fitness industry by selling premium exercise bikes and adopting a hybrid hardware–subscription model. While the upfront cost of the equipment is significant, the core of Peloton’s recurring revenue comes from its monthly digital content subscription, giving users access to live and on-demand classes. This model creates a predictable revenue stream and strengthens customer stickiness, turning a one-time purchase into a long-term service relationship. Peloton transformed how wellness is monetized by blurring the lines between fitness hardware and media platform.

Why AI Doesn't Scale: Five Lessons for Innovators

According to Gartner (2023), 80% of AI pilots fail to scale. However, the issue is not the technology itself. It is the environment surrounding it. Most businesses lack the structural and cultural foundations needed to support enterprise-wide adoption. Despite the AI hype, only 11% of companies have scaled AI across units (MIT Sloan & BCG, 2023), while 80% of pilots stall (Gartner). The issue? AI doesn’t just expose technical challenges—it amplifies structural weaknesses in business models. Many organizations approach AI as a technology investment rather than a strategic lever tightly woven into the business model. Without clarity on value creation, alignment across functions, and a scalable operating model, AI remains stuck in silos. In essence, the inability to scale AI is a symptom of deeper business model fragility, not a failure of the technology itself.

1. No Strategic Anchor

Many AI initiatives are disconnected from core business goals and are often confined to innovation labs or IT. Without a company-wide AI vision, efforts lack momentum, funding, and impact. Firms with a defined strategy are 2.3x more likely to succeed (McKinsey, 2023).

2. Cultural & Leadership Bottlenecks

AI adoption stumbles when viewed as a threat, not a tool. Employee resistance, paired with leaders overwhelmed by daily operations, means AI never gets C-suite attention or sponsorship. Successful programs like Zedwell Hotels' AI rollout succeeded because leaders committed and owned the outcomes.

3. Structural and Operational Mismatch

Old operating models don't support AI. Scaling requires agile workflows, cross-functional teams, and new governance. Most organizations still work in silos, preventing AI from achieving integrated impact.

4. Data, Ethics, and Measurement Gaps

AI struggles when success is undefined and data is ungoverned. With 73% of enterprise data unused (Forrester), even the most advanced models lack fuel. Ethical risks—bias, privacy, regulatory exposure—must be proactively governed, not fixed after deployment.

5. Talent, Ownership & Risk Aversion

Who owns AI? Who funds it? In many firms, fear of failure and unclear accountability stall progress. Companies that scale AI treat it like product development, encouraging experimentation and building in-house talent via AI Centers of Excellence.

CXO Mindset: Behaviors That Enable Innovation

According to a recent McKinsey report, 70% of executives say innovation is a top-3 priority, but fewer than 30% believe their company is good at it. Why the gap? The challenge is less about knowing what to do and more about how to execute.

Here are some of the behaviours and mindsets that I have seen successful leaders adopt:

• Make Hard Trade-Offs: Strategy is choosing what not to do. Avoid "red ocean" traps by focusing on distinctive value and fit.
• Communicate Strategy at All Levels: Innovation fails without buy-in. Leaders must teach teams to say "No" to initiatives that  are not aligned with the company’s overall strategy.
• Think in Activity Systems: Ask whether one activity reinforces another. That’s where true competitive advantage lives.
• Institutionalize Learning and Governance: Make AI and innovation repeatable by embedding them into operating cadences.

Making Strategic Choices: A CXO Checklist to SCALE their Innovation efforts

CXOs and business leaders can use these strategic questions to audit their company’s positioning and readiness for Business Model Innovation.

S – Strategy Fit

• Are we offering a truly distinctive experience—or just copying competitors?
• Are we making trade-offs that are hard for competitors to imitate?
• Is our pursuit of growth diluting our uniqueness or core positioning?

C – Customer-Centricity

• Does our cost advantage come at the expense of customer experience?
• Are our productivity gains translating to profit, or leaking to customers or suppliers?

A – AI Readiness

• Have we defined a clear AI vision aligned with our overall transformation goals?
• Do we have the talent, data, and governance model to scale AI beyond pilots?

L – Leadership & Culture

• Are we empowering teams to experiment without fear of failure?
• Are we communicating our strategic priorities clearly across all levels?

E – Execution Reinforcement

• Do our activities and decisions reinforce one another to strengthen our competitive position?

The Path Forward: Align, Redesign, Scale

Business model innovation isn’t optional—it’s the new strategic imperative. AI just exposes what’s broken faster.

What will separate the winners in this era is not the tools they use or how many AI pilots they initiate, but how boldly they redefine how value is created. This requires courage, clarity, and a commitment to holistic transformation.

Leaders must ask:

Which of these blockers are we facing? What trade-offs are we willing to make? And how can we redesign our business models to scale innovation rather than just implement it?

I would like to hear your thoughts.