Competitive Business Leaders Need Clear AI Vision to Break the Ceiling of Innovation - SPONSOR CONTENT FROM IBM

Recorded: May 27, 2026, 8:02 p.m.

Original

Navigation Menu

Account Menu

Hi,

Guest

Search Menu

Latest

Magazine

Topics

Podcasts

Store

Reading Lists

Data & Visuals

Case Selections

HBR Learning

HBR Executive

Ask AI

Navigation Menu

Account Menu

Hi,

Guest

Search Menu

Navigation Menu

Account Menu

Hi,

Guest

Search Menu

Navigation Menu

Account Menu

Hi,

Guest

Search Menu

Close menu

Please enter a search term

Submit

Popular Topics

Managing Yourself
Leadership
Strategy
Managing Teams
Gender
Innovation
Work-life Balance
All Topics

For Subscribers

Reading Lists
Data & Visuals
Case Selections
HBR Learning
HBR Executive

My Account

My Library
Topic Feeds
Orders
Account Settings
Email Preferences
Log Out
Sign In

Latest
Podcasts
The Magazine
Store
Webinars
Newsletters
All Topics
Reading Lists
Data & Visuals
Case Selections
HBR Learning
HBR Executive
My Library
Account Settings
Log Out
Sign In

Your Cart

Your Shopping Cart is empty.
Visit Our Store

Guest User

Subscriber

My Library
Topic Feeds
Orders
Account Settings
Email Preferences
Log Out

Reading List

Reading Lists

SPONSOR CONTENT FROM IBM

Competitive Business Leaders Need Clear AI Vision to Break the Ceiling of Innovation

SPONSOR CONTENT FROM IBM

May 27, 2026

Post

Annotate

Save

Post

Annotate

Save

What does it mean to lead in the age of AI? How do leaders guide their organizations to use AI for innovation?
As AI pervades every industry and every region, and its power and promise continue to accelerate, leaders are facing a serious test. Many organizations that have woven AI into their operations to share intelligence, boost efficiency, automate repetitive work, and cut expenses have seen incremental wins. That’s a great start, but improving financials isn’t enough on its own to reshape competitive advantage.
A “paradox of efficiency” is at play: When AI lifts everyone’s productivity at once, industries don’t become more competitive—they become more alike. Costs fall, speed increases, but differentiation erodes. Leaders who stop there are competing in a faster, flatter market.
“With AI, if you do a reasonable job, everyone gets a lift,” says Manish Goyal, senior partner at IBM Consulting, who advises enterprises on AI-led transformation. “AI can raise the floor for everyone, but leadership advantage comes from using AI to break the ceiling, redefining how the business competes, not just how it operates.”
Leaders must now make the leap to fully embrace AI as a transformational tool, even before its full implications for their customers, partners, and workforce have come into focus.
Such bold bets to differentiate inherently come with risk. But the far larger danger is making bets that are too small and staying the course while competitors reimagine how AI might transform their business and their industries.
Making the Bigger Leap

Seventy-nine percent of executives surveyed agree AI will significantly add to their revenue by 2030, per IBM IBV’s The Enterprise in 2030 report. But the leaders who gain a competitive advantage will be those who articulate a clear vision for aligning their organizations and reinvest their AI-powered efficiency gains in the capital, talent, and decision-making bandwidth that drives innovation and growth.
“It takes a unique organization to fully embrace transformation, with leaders engaged and knowledgeable at every level and direction coming from the top down,” says Anthony Marshall, Global Leader of IBM’s Institute for Business Value (IBM IBV), which leads research on business strategy and transformation.
The organizations that move beyond efficiency and into differentiation tend to make a few early choices differently.
1. Modernize data architecture.
Only 41% of enterprise data on average is usable by AI, according to the IBM IBV research. However mundane data hygiene seems, it’s paramount to success. For organizations to get the maximum value from their AI investments, they must connect their data across the enterprise and make it accessible everywhere.
“Bad data means bad outputs, and bad outputs undermine confidence in the technology,” Marshall says. “The moment you don’t trust AI, you’re lost. It’s very hard to recover from that.”
2. Pick the bets that break the ceiling instead of just raising the floor.
Executives must identify and articulate benefits significant enough to fundamentally change the organization’s competitive standing, not just improve efficiency. A useful test is whether an AI investment changes how the company competes. Leaders should start by examining which products, services, or decisions are truly distinctive to their business, and then ask how AI could meaningfully reshape those differentiators.

Automating back‑office processes may reduce costs and improve speed, but it rarely shifts a company’s strategic position. By contrast, using AI to reinvent underwriting in insurance, redesign clinical trials in life sciences, adjudicate claims in healthcare, or transform eligibility and benefits administration in government can fundamentally change how those organizations create value and compete in their markets.
Ultimately, leaders should pursue the use cases that force the toughest conversations about high risks and high rewards. Sticking with safe bets will mean missing out on big wins.
“Sixty-eight percent of the executives we surveyed in The Enterprise in 2030 worry their AI will fail because it won’t integrate into business activities,” Goyal says. “At the same time, in IBM IBV’s 5 Trends for 2026 report, nearly three-quarters say a fluid environment creates new business opportunities.”
One of the most effective ways leaders can close that gap is by taking a “client zero” approach and applying AI to their own organization first. Doing so forces leaders to confront integration, trust and adoption challenges directly, builds operational fluency across the enterprise, and creates credibility that is difficult to achieve through pilots alone. Organizations that learn by becoming their own proving ground are better positioned to translate AI capabilities into differentiated offerings for customers.
3. Pull in all leaders and assign accountability.
Not long ago, the chief AI officer role didn’t exist. Today it’s central to driving investment decision making and cross-functional orchestration.
“You want many roles and perspectives involved in decision making,” Goyal says. “Not just the AI and data teams, but legal, HR, finance—they all need to be present to discuss the principles of how the organization will and won’t use AI and how change will affect the workforce, the workflow, and the customers.”
The biggest mistake organizations make is treating governance as a finishing step—or, worse, as someone else’s problem. “You can’t bolt on governance at the back end,” Goyal says, “or something you’ve spent time on could be blocked for security and compliance.”
Organizational Advantage
But strategy alone does not create advantage. That advantage emerges only when organizations execute on their AI choices, mobilizing leaders, teams, and governance systems to translate ambition into new behaviors, new capabilities, and new ways of working.
Reconceptualizing the way to do business requires leaders to give AI-enabled teams the time and space to experiment with the technology at scale, identifying ideas that will work and investing in reskilling employees to develop them.
“The leaders need to clearly demonstrate how AI will improve employees’ experience as the path to a better career,” Marshall says. “They need to reward people who learn new AI skills. And leaders need to use it, too, even if it’s uncomfortable for them or if there’s a learning curve. They need to lead the shift by demonstrating it.”
Next comes reimagining the workforce: rethinking what employees are skilled at doing and deciding what they could be doing with AI. It’s critical that employees conducting this transformation have the confidence to experiment and the understanding that acquiring AI literacy increases their value both to the organization and to their own career development.
“Leaders need to explain why AI is important,” Goyal says. “Employees should understand what AI reskilling opportunities can do for them and how it can expand what they’re capable of accomplishing.”
Overseeing AI transformation in several stages and building modular architecture may make an ambitious initiative less overwhelming.
Leaders can start by identifying low-risk, high-friction processes they can change in the near term. Next, they can identify workflow changes that carry greater risk but offer a more significant advantage. Beyond those comes longer-term, high-risk business model innovation with transformational potential.
Rather than building a monolithic AI tool, a modular architecture allows organizations to make changes without causing bottlenecks and to swap AI tools in and out without disrupting progress.
Intentional Transformation
“It’s hard to develop a strategy to take your best employees off their work and put them into roles that don’t exist yet, and orient them toward uncertain outcomes,” Marshall says. “That’s a serious challenge. But bold visionary leadership will be rewarded.”
That challenge explains why many leaders hesitate and why alignment matters. When consensus exists across strategy, culture, and operating models, leaders become more comfortable acting decisively, even amid uncertainty.
Signs of that confidence are already emerging. Seventy-seven percent of chief data officers surveyed say the benefits of deploying AI agents outweigh the risks, and 69% of CEOs surveyed report that AI is already changing aspects of their business they consider core. That openness extends beyond the executive suite. Nearly two-thirds of global employees surveyed in the 5 Trends for 2026 study say they’re comfortable with the idea of working alongside AI agents.
AI’s impact on organizational culture is already visible. Two-thirds of responding employees say AI is changing their company culture, according to 5 Trends for 2026, and 88% of them describe that shift as positive. Nearly half say they would even be open to AI agents managing them.
Together, these signals create room for intentional transformation. Rather than treating uncertainty as a brake on progress, the most innovative leaders are using this moment to move beyond experimentation, committing to AI strategies that reshape how work gets done and how value is created.
“The bigger risk today isn’t in acting on AI,” Marshall says. “It’s in under‑committing and making changes that improve efficiency but never alter the competitive equation.”
In the end, AI will reward not technological ambition but organizational coherence. Competitive advantage will accrue to leaders who align early, coordinate decisions across the enterprise, and make disciplined choices about where AI will redefine how they compete—and where incremental improvement is enough.
AI may raise the floor for everyone, but only coherent leadership will determine who breaks the ceiling.

What does it really take to lead through AI‑driven transformation? Explore executive insights and perspectives.

Post

Annotate

Save

Subscribe Explore HBR The Latest All Topics Magazine Archive Reading Lists Case Selections HBR Executive Podcasts Webinars Data & Visuals My Library Newsletters HBR Press HBR Store Article Reprints Books Cases Collections Magazine Issues HBR Guide Series HBR 20-Minute Managers HBR Emotional Intelligence Series HBR Must Reads Tools About HBR Contact Us Advertise with Us Information for Booksellers/Retailers Masthead Global Editions Media Inquiries Guidelines for Authors HBR Analytic Services Copyright Permissions Accessibility Digital Accessibility Manage My Account My Library Topic Feeds Orders Account Settings Email Preferences Help Center Contact Customer Service Follow HBR Facebook X Corp. LinkedIn Instagram Your Newsreader About Us Careers Privacy Policy Cookie Policy Copyright Information Trademark Policy Terms of Use Harvard Business Publishing: Higher Education Corporate Learning Harvard Business Review Harvard Business School Copyright © Harvard Business School Publishing. All rights reserved. Harvard Business Publishing is an affiliate of Harvard Business School. About Us Careers Privacy Policy Cookie Policy Copyright Information Trademark Policy Terms of Use Harvard Business Publishing: Higher Education Corporate Learning Harvard Business Review Harvard Business School Copyright © Harvard Business School Publishing. All rights reserved. Harvard Business Publishing is an affiliate of Harvard Business School.

Competitive business leaders must establish a clear vision for artificial intelligence to achieve innovation and break through current competitive ceilings. While organizations that have integrated AI to improve efficiency, share intelligence, automate tasks, and reduce costs have achieved incremental wins, this focus on efficiency alone creates a paradox where increased productivity leads to market homogenization, allowing competitors to operate in a flatter landscape. As noted by Manish Goyal of IBM Consulting, simply raising the floor for everyone through AI is insufficient; true leadership advantage stems from using AI to redefine how the business competes, rather than just how it operates.

To gain a distinct competitive edge, leaders must make a bolder strategic leap. While executives anticipate significant revenue growth from AI, the leaders who secure long-term advantage will be those who align their organizations and reinvest efficiency gains into the capital, talent, and decision-making bandwidth necessary for innovation and growth. Anthony Marshall of IBM’s Institute for Business Value (IBM IBV) emphasizes that this transformation requires a unique organizational commitment, ensured by leaders who are engaged and knowledgeable at all levels, with direction flowing from the top down.

The shift from efficiency to differentiation requires fundamental changes in organizational approach, beginning with data management. Organizations must modernize their data architecture because the usability of enterprise data is crucial for effective AI deployment; as Marshall points out, poor data quality results in poor outputs and erodes confidence in the technology. Therefore, connecting data across the enterprise and ensuring its accessibility are paramount for maximizing the value of AI investments.

Furthermore, leaders must select AI applications that fundamentally alter competitive standing rather than just improving operational speed. Automating back-office functions, while reducing costs, rarely shifts a company’s market position. In contrast, using AI to reinvent core functions such as insurance underwriting, clinical trial design, healthcare claims adjudication, or government benefits administration can fundamentally reshape how organizations create value. Leaders should pursue use cases that force difficult conversations about high risks and high rewards, moving beyond safe bets to target areas that fundamentally change market differentiation.

Effective adoption requires involving diverse perspectives in decision-making. Goyal states that the Chief AI Officer role necessitates the presence of legal, human resources, and finance stakeholders, not just data and AI teams, to discuss the principles of AI implementation and its impact on workforce workflows and customers. Organizations frequently fail by treating governance as an afterthought; integration of security and compliance must be built in from the start, rather than bolted on later.

Organizational advantage is ultimately realized through execution that mobilizes leaders, teams, and governance systems to translate ambition into new behaviors and capabilities. This involves giving AI-enabled teams the necessary time to experiment at scale, investing in employee reskilling, and rewarding learning new AI skills. Leaders must actively demonstrate the benefits of AI to employees, showing how it enhances career paths, and must lead the change by embracing the technology themselves.

Reimagining the workforce is equally critical; employees need the confidence and understanding that acquiring AI literacy increases their value both to the organization and their careers. Leaders must clearly articulate why AI is important and explain the reskilling opportunities available to employees. To manage this transformation, leaders can adopt a modular architecture for AI initiatives, allowing for staged implementation: starting with low-risk, high-friction process changes, moving to higher-risk workflow refinements, and finally progressing toward long-term, transformational business model innovation.

Intentional transformation relies on achieving consensus across strategy, culture, and operating models, which fosters the decisiveness needed amidst uncertainty. Signs of this emerging confidence are visible across the organization, with executives recognizing AI’s impact, and employees showing comfort with AI agents. This positive cultural shift allows organizations to move beyond mere experimentation and commit to AI strategies that reshape work and value creation. Ultimately, competitive advantage will accrue to leaders who achieve organizational coherence by aligning early, coordinating decisions across the enterprise, and making disciplined choices about where AI will redefine market competition, recognizing that while AI may raise the floor for everyone, coherent leadership determines who breaks the ceiling.