Insider Brief
- By 2030, leading enterprises will operate as AI-first organizations, embedding artificial intelligence into decision-making, products, workforce design, and governance, shifting competitive advantage away from incremental adopters toward firms built to learn and adapt in real time.
- IBM Institute for Business Value research, based on a survey of more than 2,000 executives across 33 geographies and 23 industries, finds a widening gap between ambition and readiness, with nearly 80% expecting AI-driven revenue by decade’s end but fewer than 25% able to identify its source.
- The report outlines five forces shaping the next decade—pressure for larger strategic bets, reinvestment of AI-driven productivity gains, tailored multi-model AI architectures, rising importance of human judgment alongside agentic AI and the emergence of quantum computing as a future disruptor.
By the end of this decade, the world’s most successful companies will no longer treat artificial intelligence as a productivity tool but as the core architecture of how business operates, according to new research from the IBM Institute for Business Value. The report argues that enterprises are moving toward an “AI-first” model in which decision-making, innovation, workforce design and even governance are continuously reshaped by intelligent systems, leaving incremental adopters at a structural disadvantage.
The research, based on a global survey of more than 2,000 senior executives across 33 geographies and 23 industries conducted with Oxford Economics, finds a widening gap between ambition and readiness. Nearly four in five executives expect AI to contribute meaningfully to revenue by 2030, yet fewer than one in four can clearly identify where that revenue will come from. IBM frames this uncertainty as a defining leadership challenge of the decade: companies must place larger, riskier bets faster, often without full visibility, or risk being overtaken by more adaptive competitors .
A central theme of the report is the shift from “AI-enabled” organizations — those that automate existing workflows — to “AI-first” enterprises designed to learn and adapt in real time. Executives surveyed expect AI investment to rise roughly 150% between 2025 and 2030, with spending increasingly directed toward product innovation, new services, and business model reinvention rather than cost reduction alone. By 2030, respondents expect nearly two-thirds of AI budgets to support growth and transformation, up from less than half today.
“It’s this dynamic that will define the winners of the next decade: not deploying the most powerful technology and making the biggest cuts to headcount, but building AI that knows the business, reflects its values and amplifies the expertise of its people,” Senior Vice President of IBM Consulting Mohamad Ali wrote in the report.
IBM organizes its findings around five predictions that together define what it calls the “smarter enterprise.”
Competitive Pressure Will Make Big Bets Unavoidable
IBM finds that by 2030, competitive advantage will hinge on speed and willingness to operate amid uncertainty. More than half of executives say execution velocity will matter more than perfect decision-making, pushing organizations toward larger and riskier strategic bets.
IBM noted the telecom industry illustrates this shift. IBM’s research shows telecom leaders are moving away from AI investments focused on cost control toward revenue creation, with two-thirds of projected AI spending by 2030 aimed at product and business model innovation. Digital twins and AI agents are enabling telecom operators to simulate new service launches, predict customer behavior, and monetize usage patterns in real time, while cross-industry partnerships are opening markets such as smart cities, telehealth, and immersive media. Telecom firms that delay these bets risk losing ground to hyperscalers and fast-moving digital entrants.
Productivity Gains Will Fund Industry Reinvention
AI-driven productivity improvements are already material, but IBM argues they are only the first phase of transformation. Executives expect AI to lift productivity by more than 40% by 2030, with most gains realized well before then.
Automotive manufacturing provides a clear example. Today, carmakers are using AI to streamline supply chains and manufacturing operations. IBM’s report shows those savings are increasingly being reinvested into AI-enabled vehicles that learn driver behavior, predict maintenance needs, and deliver software-driven features over time. Digital and software-related revenue, currently a minority of automotive income, is projected to become a majority share over the next decade, turning efficiency gains into long-term growth engines.
In IT services, the pressure is even more acute. IBM finds that firms built on billable hours face an existential challenge as AI compresses delivery timelines. Leading IT services companies are reinvesting productivity gains into outcome-based offerings, shifting from selling time to selling results. More than 80% of IT services executives say they are using AI-driven savings to fund growth initiatives rather than banking them as profit.
The Most Valuable AI Will Be Tailored, Not Generic
As large AI models become widely accessible, IBM concludes that differentiation will depend on customization rather than scale. Enterprises that assemble portfolios combining foundation models with smaller, task-specific systems trained on proprietary data expect significantly better outcomes.
Aerospace and defense organizations demonstrate this approach, according to IBM. The report finds that these firms are prioritizing custom, mission-specific AI models to operate securely in classified and edge environments. Nearly four in five aerospace and defense leaders say they already have a clear view of the AI models they will need by 2030, far exceeding other industries. Security, reliability, and ethical accountability—not model size—are driving architecture choices in this sector.
IBM’s analysis links tailored, multi-model strategies to higher productivity gains, faster delivery cycles, and stronger operating margins across industries.
AI Will Not Replace Human Judgment
Despite rapid automation, IBM’s research suggests that human judgment will become more valuable, not less. Executives expect job roles to change rapidly, with more than half of today’s skills becoming obsolete by 2030, but AI-first organizations are also more likely to create new roles and redesign organizational structures.
Healthcare is highlighted as illustrating this dynamic. IBM finds that healthcare organizations are using agentic AI to automate administrative tasks such as clinical coding, patient scheduling, and discharge planning. These gains free clinicians to focus on patient care, where empathy and complex decision-making remain essential. Two-thirds of healthcare executives say AI is driving net-new job creation or organizational redesign, rather than simple workforce reduction.
Across industries, agentic AI is expected to be embedded in finance, sales, marketing, IT, and R&D by 2030, reshaping org charts and increasing demand for roles that supervise, govern, and audit intelligent systems.
Quantum Computing Will Drive the Next Disruption
Looking beyond AI, IBM identifies quantum computing as a coming inflection point that many enterprises are underestimating. While 59% of executives believe quantum-enabled AI will transform their industry by 2030, only 27% expect to be using quantum computing by then.
IBM says banking illustrates both the risk and opportunity. IBM’s report shows financial services firms are far more likely than other industries to expect quantum computing to deliver business value by 2030, particularly in areas such as portfolio optimization, risk modeling, and cryptography. Early experiments combining quantum and classical computing have already demonstrated measurable improvements over traditional approaches, suggesting that early movers could gain meaningful computational advantages.
IBM also points to pharmaceutical research, where quantum simulations are beginning to address molecular problems beyond the reach of classical systems, signaling potential breakthroughs in drug discovery and materials science.
A Structural Reset, Not a Technology Upgrade
Taken together, IBM’s five predictions describe a fundamental reengineering of the enterprise. AI-first organizations are becoming less hierarchical and more modular, continuously adapting based on real-time data rather than periodic strategic reviews.
The report concludes that by 2030, competitive advantage will belong to enterprises that treat AI, human judgment, and emerging technologies such as quantum computing as an integrated system. For leaders, the message is direct: the question is no longer whether to adopt AI, but whether the organization is structurally prepared to compete in an economy defined by perpetual reinvention
