Innovation

The Hard Job of Driving Value from AI

| Charlie Ranalli

Generative AI arrived quickly, with bold promises dominating headlines. But anyone who has lived through past technology shifts knows the real story: the hard job of driving value is not proving it can work – it is adopting it to transform how companies work. Just as with PCs, e-commerce, or mobile, impact will not be fast or easy.

Customers, colleagues, and friends — nearly everyone I exchange perspectives with is already using GenAI to work faster, smarter, and better. That matches findings from a recent paper by Andrew McAfee and colleagues, which shows the technology delivering rapid productivity gains across many occupations. (And yes, I used GenAI tools in researching this article.)

The challenge is that individual gains – especially in knowledge-based work – don’t automatically scale into business or economic impact, at least in most sectors. To matter, they must flow into optimized processes, unit economics, and value levers like prices, margins, or productivity in high-volume workloads. As McAfee and his co-authors note, that means “complementary innovations and organizational reinvention.”

The good news is we finally have some evidence to work with. Early experiments show where AI can create traction and where it tends to stall. They also make one thing clear: getting value out of AI means tough choices and visible commitment from the top team.

The Road to Structural Gains

Across industries, AI pilots are everywhere – copilots for coders, chatbots for service, tools to automate routine tasks. They show what’s possible, but most remain stuck at the pilot stage. Local productivity improves, yet the broader structure of how work gets done stays the same.

This is familiar. Every major technology shift follows a pattern: early enthusiasm, a rush of experimentation, and only later the hard work of embedding new tools into the fabric of business.

The PC revolution is a widely researched case. Email, spreadsheets, and collaboration tools became available in the 1980s. Yet measurable productivity gains only showed up in the mid-1990s, once workflows were redesigned, people retrained, and cultures changed. It took 15 years before the technology showed up in macroeconomic statistics.

E-commerce followed a similar path. Despite 30 years of innovation, investment, and exceptional consumer convenience, only about 16% of U.S. retail spending is online today.

Gartner’s 2025 Hype Cycle places generative AI just past the Peak of Inflated Expectations, signaling that today’s surge of pilots and proofs of concept will only translate into value once organizations take on the harder work of reinvention.

Generative AI is best understood as a general-purpose technology, much like electricity, PCs, or the internet: broad in potential, but dependent on complementary change before its impact can be fully realized. Research by Andrew McAfee and colleagues suggests this wave could diffuse faster than earlier ones because the infrastructure is already in place, tools are widely accessible, and adoption often requires less retraining. Yet the speed of progress still depends heavily on an organization’s own maturity – some have ways of working and modern tech stacks ready to absorb AI, while others face years of groundwork before they can capture meaningful value.

Early signals remain ambiguous. A Stanford study found that workers aged 22–25 in AI-exposed roles saw employment fall by about 13% since 2022, while more experienced peers held steady – suggesting the first effects of AI might be showing up in hiring rather than productivity. A more recent analysis from Yale’s Budget Lab offers a different lens: instead of losses, it examines shifts in the occupational mix – which tasks are being automated, which are being augmented, and how work itself is changing as AI spreads. Apparent declines in some roles may instead reflect a reallocation of tasks. The evidence remains faint — more about changing work than vanishing jobs.

As a technology executive and entrepreneur who lived through the dot-com wave, the rise of e-commerce, mobile, and social media, I’ve seen this pattern repeat: first the hype, a dose of disillusionment, and finally the long, grinding work of structural change. Generative AI may spread faster than past waves, but the hard job to generate value will be the same.

Why AI Value Will Be Slow to Materialize

Integration Matters More than Deployment

Customer service bots, document summarizers, and coding copilots are easy to deploy. They spread fast but don’t transform most industries. The next step is Agentic AI – systems that combine business logic and different types of AI to perform multi-step workflows, interact with humans, and span business functions.

The challenge is integration. Agents don’t operate in isolation. They need redesigned interfaces and workflows: decision points, exception handling, escalation rules, and system integration. As McKinsey observed after studying 50+ agentic AI builds: “It’s not about the agent, it’s about the workflow.”

“Fewer than 10% of generative AI use cases have made it past the pilot stage.”
“Only 21% of organizations using gen AI report that they have fundamentally redesigned at least some workflows.”
“Among the 25 organizational practices tested, workflow redesign has the strongest relationship with reported EBIT impact.”
– McKinsey, State of AI: How Organizations Are Rewiring to Capture Value (2025)

Reengineering Operating Models

Some industries will see direct disruption – translation, for example, where AI can deliver acceptable quality at near-zero cost. But most require operating model reinvention: reconfiguring how decisions are made, how accountability flows, and how value is created.

McKinsey’s State of AI 2025 survey illustrates the gap: “Fewer than 10% of generative AI use cases have made it past the pilot stage.” Tools are spreading, but few firms have revamped their operating models to absorb them.

No Sustainable Edge in Base Technology

Generative AI is trained on public domain knowledge and released broadly. As MIT Sloan has argued, AI itself is unlikely to provide sustainable competitive advantage.

McKinsey’s survey is blunt: “Only 21% of organizations using gen AI report that they have fundamentally redesigned at least some workflows. Among the 25 organizational practices tested, workflow redesign has the strongest relationship with reported EBIT impact.”

The edge comes from differentiated data, proprietary processes, and organizational creativity. Cloud platforms can be acquired easily; unique ways of working cannot.

The Human Factor

AI changes organizational dynamics. Vertical supervising tasks give way to orchestrating systems of people and agents. Leaders must re-skill employees, manage resistance, and build trust in AI-driven systems.

Human-in-the-loop design is critical. Generative AI introduces risks of hallucinations, bias, and IP leakage that can’t be solved by technology alone. In a Harvard Business Review article, Andrew McAfee and his co-authors argue that employees need to be trained to recognize these risks, escalate them, and build confidence in using AI responsibly.

And it’s not only frontline or entry-level roles. A recent MIT Sloan article argues that AI will increasingly automate coordination and monitoring – traditional managerial functions – while creating demand for new roles in oversight, design, and orchestration.

Adoption will be uneven. Early enthusiasts embrace new tools; skeptics push back, especially after errors. Clear accountability, transparent communication, and cultural adaptation are as critical as technical readiness.

Platform Readiness: Data, Systems, and Governance

Finally, structural adoption depends on platform readiness. AI requires clean, governed data — still a rarity in most organizations. Just as critical is the underlying architecture: many ERP, CRM, and supply chain systems are built on rigid interfaces and hard-coded business logic. To take advantage of AI, those systems need more flexible designs that can support dynamic workflows. AI can’t be simply bolted onto legacy systems.

Governance must also mature alongside technology. Privacy, compliance, and risk management can’t wait until systems are scaled if they are exposed to the outside world – they need to be embedded from the start.

The Productivity Promise Is Still Real

Despite these challenges, the long-term upside is enormous. McKinsey estimates that Agentic AI could unlock $450–650 billion annually by 2030 in advanced industries such as manufacturing, logistics, and energy.

Functions like supply chain, software development, and customer service can all be reimagined. But because the base technology is broadly available, the competition to innovate will be fierce. The winners will be those that integrate faster and deeper, building on proprietary assets.

From my experience with past technology shifts, this is where the champions pull ahead. When mobile and social networks disrupted consumer engagement, the companies that succeeded weren’t the ones with the flashiest apps – they were the ones that cleverly reimagined marketing, sales, and service for two-way, mobile-first interactions. The same will be true with AI.

From Experimentation to Transformation

The practices below reflect what I’ve seen in working with clients, along with insights from global leaders and academics. Together, they highlight what helps organizations move beyond experimentation toward real transformation.

Activate the Leadership Team

Transformation begins at the top. This is not just a technology project but a significant change effort. The senior team has the credibility to ask the hard questions, the clout to mobilize resources quickly, and the leverage to remove obstacles. If they aren’t in the room and visibly committed, the rest of the organization will sense it – and resistance will dampen progress.

Map Opportunities

With customer teams, we start broad and deep. The goal isn’t just to identify obvious efficiencies, but to uncover disruptive opportunities. We map ideas across two dimensions: strategic business pillars, and the angles of innovation that AI can unlock.

This stage is about thinking differently – exploring what could change the rules of the game rather than just automate today’s tasks. It’s a step for conceiving opportunities, not a commitment to chase them all.

Invest in a Balanced Portfolio

Once opportunities are conceived, the next step is to commit to a portfolio that balances ambition with pragmatism. In practice, the portfolios that resonate most with leadership teams usually contain three kinds of bets: disruptive ideas that carry risk but could redefine value creation; quick wins that deliver visible results and momentum; and defensive moves that may not create lasting advantage but are quickly becoming industry baseline.

Start with the Questions

AI is not an end in itself. The right starting point is a business question: If I could forecast X more accurately, what would change? If I could automate Y, how much capacity would I free up?

One industrial client is embedding AI agents into a reactive B2B service chatbot. The initial aim is faster response, but the next goal is to extend into proactive commercial processes – identifying cross-sell opportunities, guiding orders, anticipating needs. The key question wasn’t an open-ended “what can AI do,” but “can AI turn knowledge into growth while managing risk up front?”

Advance Initiatives and Capabilities

Run promising use cases first but do so with discipline: set substantial business goals, track adoption and value, and be ready to kill experiments that don’t deliver. In parallel, invest in the capabilities that enable scaling: data assets, governance, orchestration technology, and workforce skills.

One of my retail clients launched a Copilot program to organize and classify knowledge. The first phase focuses on individual and small team productivity, but the explicit goal is to mature into AI-enabled collaborative innovation. Sequencing matters: early initiatives build experience and momentum, while capabilities ensure the organization is ready to accelerate what works.

Close Business, Technology, and Risk Collaboration

AI can’t sit in a technical silo. Business leaders must co-own initiatives. To build trust and ensure each step forward stands on firmer ground, risk and compliance need to be involved from the start. Governance has to be continuous, with clear paths for when agents fail.

One useful approach, highlighted in HBR and practiced by most early adopters, is to move deliberately in stages: experiment in a sandbox, then run tightly scoped pilots, and only scale once risk and governance structures are proven to hold.

The Case for Simplicity

When teams start mapping AI opportunities, the challenge isn’t scarcity – it’s abundance. The possibilities are endless, and the actions required to pursue them quickly become complex and interdependent. Without a simple top-level frame, discussions can fragment across business, operations and technology perspectives.

A practical way to bring structure is to group AI applications into broad categories, for example:

Predictive & Optimization – improving foresight and decision quality through analytics and modeling.

Autonomous Decision & Action – embedding intelligence into processes that can sense and act in real time.

Knowledge-Management & Generative – expertise, content, and amplifying human capability.

End-to-End – connecting and orchestrating workflows across functions to create compound value.

Illustrative opportunity matrix. Sanitized from project material to show structure, not content.

This structure, borrowed from a recent workshop, isn’t meant to be followed verbatim – it must be reframed based on each organization’s industry and AI posture, and paired with strategic pillars to give a full 360-degree view. It simply shows how to organize a portfolio around general concepts rather than specific technologies or use cases.

The Takeaway

AI opens unprecedented opportunities but is not a silver bullet. Pilots and use cases are necessary, but they are not sufficient. Like PCs, e-commerce, and mobile before it, AI’s real value will only come when organizations reinvent their structures to embed it deeply in business and operating models.

The winners won’t be those who run the most experiments. They will be the organizations willing to rewire themselves so that AI becomes part of the fabric of how business is done. For leaders, that means rolling up your sleeves and doing the hard job of transformation.

References

The impact of generative AI as a general-purpose technology
MIT Sloan School of Management, 2024
https://mitsloan.mit.edu/ideas-made-to-matter/impact-generative-ai-a-general-purpose-technology

Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence
Stanford Digital Economy Lab, 2025
https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/

Evaluating the Impact of AI on the Labor Market: The Current State of Affairs
Yale Budget Lab, Yale University, 2025
https://budgetlab.yale.edu/research/evaluating-impact-ai-labor-market-current-state-affairs

One Year of Agentic AI: Six Lessons from the People Doing the Work
McKinsey & Company, 2024
https://www.mckinsey.com/capabilities/quantumblack/our-insights/one-year-of-agentic-ai-six-lessons-from-the-people-doing-the-work

The State of AI: How Organizations Are Rewiring to Capture Value
McKinsey & Company, 2025
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Why AI Will Not Provide Sustainable Competitive Advantage
MIT Sloan Management Review, 2025
https://sloanreview.mit.edu/article/why-ai-will-not-provide-sustainable-competitive-advantage/

How to Capitalize on Generative AI
Harvard Business Review, 2023
https://hbr.org/2023/11/how-to-capitalize-on-generative-ai

Why Robots Will Displace Managers – and Create Other Jobs
MIT Sloan Management Review, 2025
https://sloanreview.mit.edu/article/why-robots-will-displace-managers-and-create-other-jobs/

The Economic Potential of Generative AI: The Next Productivity Frontier
McKinsey & Company, 2023
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers

| Charlie Ranalli

This is a belated review of a book very, very useful for enterprise intrapreneurs and start-up entrepreneurs.

Business Model Generation provides a comprehensive and practical toolbox to design and evaluate business models: a reference framework (the widely adopted Business Model Canvas), a set of patterns (e.g., unbundling, long tail, multi-sided platforms), design technics (a lot in common with Design Thinking), strategy (in the authors’ own words “approaches to reconsider business strategy though the lens of the business models canvas”) and finally a process for business model design.

It is full of real-life examples of how companies invented or disrupted markets with ingenious business models.

For teams that want to push the boundaries of business model innovation, this book provides an extremely practical framework to walk the exploratory journey with focus and technique.

Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers
by Alexander Osterwalder and Yves Pigneur

Becoming Future Fit: Challenges and Opportunities for Consumer Product Companies

| Charlie Ranalli

Faced with changing consumer preferences, accelerating technology change, and competition from nimble startups, today’s consumer products (CP) companies know they need to make major changes to their operations within the next half-decade to remain successful. But while many have started down the path of transformation, progress is uneven, with some companies stalled by a combination of conflicting leadership priorities and a shortage of the talent and capabilities needed to make change happen. Unless companies find ways to overcome these hurdles in the near future, they will fail to achieve their transformation goals and grow increasingly out of step with the demands of tomorrow’s consumers rather than becoming the models of efficiency and responsiveness they wish to be.

These are among the findings in a new MIT SMR Connections/EY LLP Global Consumer Industries research study based on a survey of 370 CP business leaders in 10 countries. Conducted in June and July 2021, the survey asked these leaders about the challenges they face as they endeavor to make major changes in operations ranging from manufacturing and supply chain to finance, marketing, and talent acquisition to meet changing consumer needs.

A multi-disciplinary team of advisors and academics then sliced and diced the results of the survey, identified patterns, and provided provocative recommendations.

“The way business models are architected in the large consumer goods companies, the way they account for profitability and marketing expenses, needs to change. They need to rethink the entire measurement system so they can get a more holistic view of the relationship between them and the customer.”
Len Schlesinger
Baker Foundation Professor, Harvard Business School

Published in a series of Sloan Management Review Articles, a (downloadable) paper and a summary infographic:

Is Your Company’s Operating Model Trailblazing — or Trail-Gazing?
September 2021

3 Priorities for Accelerating Your Operating Model Transformation
October 2021

Becoming Future Fit: Challenges and Opportunities for Today’s Consumer Products Companies
December 2021

Optimizing Operations for Now — and the Future
December 13, 2021

Why Investing in Technology Is No Longer a Choice

| Charlie Ranalli

In a sharp infographic, based on a survey of 229 companies, a team of Bain & Company consultants make a convincing case of why investing in technology is no longer a choice.

The numbers also reflect the challenge of, in their own words, “making the most out of [the] new models” as they found that only 14 are leaders, getting good outcomes like better customer experiences and lower cost, while the next 39% are followers, making progress in adopting modern operating models and architecture, but not getting the outcomes they want. The rest are almost evenly split between learners and late adopters.

Why being a technology leader matters? How about 8 times higher than the rest in customer loyalty ranking and 2.5x more likely to say that they innovate better than the rest of their industry.

Why Investing in Technology Is No Longer a Choice
By Vishy Padmanabhan and Lauren Brom
Bain & Company, August 2021

The New Elements of Digital Transformation

| Charlie Ranalli

The team behind The Digital Advantage: How Digital Leaders Outperform their Peers in Every Industry and The Nine Elements of Digital Transformation reflected on their influential research after surveying 1300 executives in more than 750 global organization.

Their earlier research on digital transformation identified two dimensions through which leading companies outperform their peers: digital capability and transformative leadership capability. They found that the elements of leadership capability have endured, but new elements of digital capability have emerged.

Particularly opportune is the addition of a digital architecture as a critical platform that enables nimble innovation. In my own experience, the lack if a well-designed and implemented architecture prevents the roll-out of initiatives large and small, consuming more technical resources and frustrating business partners.

The New Elements of Digital Transformation
By Didier Bonnet and George Westerman
MIT Sloan Management Review, November 2020

Fast Times: How Digital Winners Set Direction, Learn, and Adapt

| Charlie Ranalli

In Fast Times, a team of McKinsey consultants share the recipe they apply to help their customers be first movers and win the digital race.

“[Fast Times] is for senior executives who are frustrated by the slow pace and limited return on investment (ROI) of their digital transformations, and are unsure what’s holding them back” in the word of the authors.

While not a detailed blueprint to design a Digital Transformation initiative, they cover critical imperatives to develop a Strategy, Capabilities, Adopt and Scale, and they cleverly do it answering provocative questions like “Are you clear about the which transformation model is best for your company?” or “Have you hired digital stars?”

They provide insightful tips on Speed, Scale, Talent and Culture. A must read for leaders already embarked in a digital journey or in need of a reset.

Fast Times: How Digital Winners Set Direction, Learn, and Adapt
by Arun Arora, Peter Dahlstrom, Klemens Hjartar, and Florian Wunderlich

Digital Transformation Is Not About Technology

| Charlie Ranalli

Most business executives think – often reflected by who they bring to the room when you discuss the topic with a CEO – that Digital Transformation is about technology. It is not.

Certainly, top notch technology capabilities are a critical ingredient in all Digital Transformation success stories, but there is a lot more to it. This is the purpose and central theme of this blog: debunking the notion that technology is the most important factor in making a start-up or centuries old companies more competitive with digitally driven innovation, and expanding the field of view of executives to include the broader set ingredients that they will have to mix and match to lead their companies into the digital future.

Digital Before Transformation

Software is eating the world. In a now famous 2011 Wall Street Journal piece, technology entrepreneur and venture capitalist Marc Andreessen brilliantly made the case of why most companies were in the way of becoming software companies – across industries, from entertainment and banking to cars, retail and logistics.

“Six decades into the computer revolution, four decades since the invention of the microprocessor, and two decades into the rise of the modern Internet, all of the technology required to transform industries through software finally works and can be widely delivered at global scale” he wrote.

While banks still own branches, airlines fly planes and Amazon last year ordered 100,000 electric delivery trucks, technology is now in charge of engaging with customers, managing risk and fares, or deploying assets – and make companies winners or losers depending on how good their algorithms are.

Most of the underlying technology has been around for some time. But now its maturity, ubiquity, and affordability – and the ingenuity of the engineers, entrepreneurs and innovators deploying it – are giving it a dramatically more significant role in shaping business models and strategies. A Transformative role.

Time to Transform

Some industries and companies can be wholly reconfigured by technology – remember Blockbuster or Tower Records? – but not all. McKinsey puts is very well: “the number of companies that can operate as pure-play disrupters at global scale are few in number, and rarer still are ecosystem shapers that set de facto standards and gain command of the leverage created by hyperscaling digital platforms.”

But even in asset or activity-intense sectors that can’t be entirely switched to digital-only experiences, modern technology is driving major change, and visionary executives the world over have taken note and quietly started reshaping strategies, business models and organizations to exploit these new opportunities. They updated their leadership styles, cultures and platforms to design and deploy entirely new ways of doing things. They embarked in a Digital Transformation.

The Impact

Academics and consulting outfits have very carefully analyzed the advances in digitization and linked them with financial performance, and the relationship is undeniable.

A seminal two-year study by the MIT Sloan School of Management analyzed more than 400 large firms and found that digitally transformed businesses are 26% more profitable than their industry competitors, drive 9% more revenue through their employees and physical assets and are 12% more valuable than their peers.

The researchers developed a digital maturity model to show how different companies are reacting to technological opportunity, and cleverly analyzed how businesses invest in technology, but more importantly, how the true leaders create the leadership and change management capabilities necessary to drive innovation, which they called transformation management intensity.

They proved that the ability to modernize strategies, organizations and processes is as – or more – important than the technology itself in the quest to be a digital leader.

Another by McKinsey found that focusing on the right digital practices, B2B companies –currently trailing B2C companies in digital maturity – can create long-term value, with the most advanced in their transformation programs driving five times more revenue growth than their peers.

Digital Leadership

Digital transformation is about Strategy, People, Innovation and Execution. Photo by standret.

So, what is Digital Leadership about then?

It is about reinventing strategies, operating models, and processes. It is about putting the customer in the center of attention of the entire organization and designing their experience from the outside in. It is about fact driven decision making and agile change management.

It is about fostering a culture of nonstop innovation and fearless renewal, of mercilessly abandoning established ways of doing things and adopting digitally enabled models.

Technology allows all of this, from the advanced analytics platforms that support decision making and action to the omnichannel platforms that support seamless customer experiences. From process automation to remote collaboration. But despite the broad availability and growing affordability, technologies alone are useless without the leadership to drive change.

Transformation is more important than Digital. And Transformation is about Strategy, People, Innovation and disciplined Execution, the components of the framework proposed in this blog.


Originally written late 2017, updated December 2020.

Doing Agile Right: Transformation Without Chaos

| Charlie Ranalli

Having originally learned Agile – and its family members Scrum and Kanban – leading a software development organization, applying it to change management and digital innovation required taking a step back and reflecting how the concepts, methods, and artifacts work in this new mission and environment. It took a lot of work!

In Doing Agile Right, Bain & Company’s Darrell Rigby and his colleagues Sarah Elk and Steve Berez provide an excellent overview of Agile concepts, how to implement and scale them to enable nimble innovation, and, in their own words, how to achieve balance between the fast-paced change facilitated by Agile and the financial and operational discipline provided by traditional management practices.

“Every organization must optimize and tightly control some of its operations, and at the same time innovate. Agile, done well, enables vigorous innovation without sacrificing the efficiency and reliability essential to traditional operations. The authors break down how agile really works, show what not to do, and explain the crucial importance of scaling agile properly in order to reap its full benefit. They then lay out a road map for leading the transition to a truly agile enterprise”

Doing Agile Right: Transformation Without Chaos
by Darrell Rigby, Sarah Elk, Steve Berez

Why You — Yes, You — Need Enterprise Architecture

| Charlie Ranalli

The authors define enterprise architecture as the holistic design of people, processes, and technology to execute digitally inspired strategic goals. They argue that every unpleasant customer interaction via a company app, website or telephone call exposes architectural inadequacies. Left unsolved, these issues will destroy formerly great organizations.

They suggest adopting three principles to tap the benefits of enterprise architecture: break processes and products into components, empower cross-functional teams, and allow business design to influence strategy.

Jeanne Ross and Cynthia Beath are coauthors of Designed for Digital: How to Architect Your Business for Sustained Success (MIT Press, 2019). Ross was principal research scientist for MIT’s Center for Information Systems Research for almost 27 years. Beath is professor emerita of information systems at the McCombs School of Business at the University of Texas at Austin.

Jeanne Ross and Cynthia Beath, “Why You — Yes, You — Need Enterprise Architecture”
MIT Sloan Management Review, August 2020