The enterprise in 2030
Across industries, the pattern is the same: AI is changing what companies do and how they do it. Yet a striking blind spot remains. 79% of executives say AI will significantly contribute to their revenue by 2030, but only 24% can clearly see where that revenue will come from. That gap between expectations and outcomes presents the leadership challenge of this decade.
With no clear destination in sight, winning CEOs won’t chase competitive advantage. They’ll code it into existence.
That requires tailored technology—digital agents, AI models, and data that capture the essence of each organization’s business logic. Generic algorithms and off-the-shelf agents alone won’t differentiate. The real advantage (and ROI) comes from artificial intelligence that no competitor can replicate.
The dawn of the smarter enterprise
Leaders are recognizing that the future of business is a hybrid of people and software—a lot of software. Every process that can be automated will be. Every role will be enhanced by intelligent systems that learn and adapt.
Most business leaders—57%—now say their competitive advantage will come primarily from the sophistication of their AI models by 2030. While people will remain essential, organizations will need to build differentiating technology for even the best teams to deliver an edge in an AI-first world.
“By 2030, we will do things that were previously too expensive to be ROI-positive. We will also build products that simply couldn’t exist without AI semantic understanding.”
Alex Schultz
Vice President Analytics and Chief Marketing Officer, Meta
The real advantage will come from how organizations design and orchestrate thousands of decentralized AI agents that work alongside employees, each one tuned to the company’s purpose, culture, and competitive edge. Leaders will need to ask: Where should AI augment people—and where should people augment AI? The most successful organizations will reimagine how humans and machines collaborate to achieve more than either could on their own.
This is the difference between AI-enabled and AI-first. And IBM Institute for Business Value (IBM IBV) research shows this transition is already underway.
In partnership with Oxford Economics, we surveyed 2,000 executives in the third and fourth quarters of 2025 about how they expect their organization to evolve over the next five years. Responses from leaders across 33 geographies and 23 industries, reveal a seismic reconfiguring of operational practices and strategic assumptions due to AI-powered digital transformation.
Between 2025 and 2030, business leaders predict AI investment will surge approximately 150% (as a percentage of revenue). While a large portion of AI spend (47%) is focused on efficiency today, executives expect almost two-thirds (62%) to be dedicated to product and service and business model innovation by 2030. This may reflect the fact that, in a smarter enterprise, efficiency and innovation should be one and the same.
Top C-suite priorities
It’s difficult to imagine the revolutionary capabilities AI will develop over the next five years. Building an organization that can succeed in the future means preparing for continual tech-driven disruption—abandoning the comfort of incremental change and embracing constant evolution that matches the pace of machine learning. Everything else is just playing catch-up.
What will it take to win in an AI-first business landscape? We have five predictions about what will define success in 2030. (Download the PDF for the full set of findings from our proprietary research, as well as industry-focused analysis and steps leaders can take to gain the AI-first advantage.)
1. Competitive pressure will make big bets non-negotiable.
Winning in 2030 will depend on a combination of creativity, confidence, and speed: 55% of executives say competitive advantage in 2030 will depend more on speed of execution than making perfect decision-making. These leaders know they’ll have to make bigger bets faster—with less complete information at their disposal.
Organizations that have embraced the unknown expect to accelerate much faster than their peers. Our analysis shows that organizations leaning into AI-first operations anticipate 70% greater improvement in productivity, 74% greater reductions in process cycle times, and 67% greater improvement in project delivery times than their peers by 2030.
This is as much of an operational challenge as a strategic one. To move at speed with new technologies, organizations need to foster a culture of outcomes-focused experimentation: rapidly deploying minimum viable products (MVPs), iterating and tracking performance, and deciding which MVPs to scale to deliver the most business value. They also need a stable ecosystem, with partners that can support the agility AI-first organizations require. And they need AI capabilities and models fine-tuned with their organization’s proprietary data, coupled with agents that can access the most up-to-date information as data is processed and flows across the organization in real time.
“A startup can now operate at the same scale as a large enterprise, but move at a much faster speed. That means smaller companies can really disrupt the markets they’re going after.”
Aaron Levie
CEO and Co-founder, Box
2. Today’s productivity gains will fund tomorrow’s industry transformation.
Today’s AI investments are driving unprecedented productivity gains. And these efficiency improvements are just the opening act.
A two-phase revolution has already begun. More than half (53%) of executives say AI will have transformed business models in their industry by 2030. Phase one, focused on using AI to eliminate waste, accelerate processes, and amplify human capability within existing business models, is already well underway. Executives expect AI to increase productivity by 42% by 2030—and 67% of executives expect to have captured most of their AI-enabled productivity gains by then.
Phase two leverages the resources freed up from those productivity gains to reimagine entire industry verticals—and the first to get it right could earn an unassailable advantage. Already, 70% of executives say they’re looking to use the value creation from AI to fund investment and growth across the organization.
How to boost AI-powered productivity
Instead of banking productivity savings as profit, organizations can reinvest them in exponential growth opportunities. These investments in innovation then transform the business model, which in turn fuel further growth. It’s a flywheel effect where productivity gains don’t just reduce costs, they actively drive revenue growth by helping companies capture greater market share.
3. The best AI will be one-of-a-kind. Your kind.
Tomorrow’s competitive advantage won’t come from using the largest AI models. It will come from using AI in a way that no one ever has before.
When every organization has access to the same large foundation models, the differentiating factor becomes how well these different models are combined and customized—and how your unique enterprise data is incorporated to achieve targeted business objectives. By 2030, 82% of executives expect their AI capabilities to be multi-model—and 72% expect small language models (SLMs) to become more prominent than large language models (LLMs) in their organizations in the same timeframe.
Directing a dynamic AI model portfolio isn’t the same as overseeing traditional software deployments or even cloud migrations. Because these models learn, adapt, and evolve in real time, this type of portfolio needs constant tuning, ethical oversight, and strategic direction. Leaders need a skillset that is part technologist, part strategist, part behavioral scientist. No wonder 74% of the executives we surveyed say AI will redefine leadership roles across the enterprise by 2030. Two-thirds say AI will create entirely new leadership roles, with 68% expecting to have a Chief AI Officer.
The key is connecting the dots: 68% of executives worry their AI initiatives will fail due to lack of integration with core business activities. There is a difference between AI adoption—adding tools to existing processes—and creating integrated intelligence that becomes inseparable from business strategy.
“AI’s future isn’t about bigger models. It’s about smarter integration with people and processes.”
Jinesh Dalal
Head and Vice President, Technology Development, C-Metric
4. AI won’t do all your thinking for you.
Today’s job roles will be unrecognizable in the enterprise of the future. Already the half-life of human skills is shrinking: 67% of executives in our research say job roles are becoming shorter-lived and 57% expect most current employee skills to become obsolete by 2030.
As pre-AI workflows become obsolete, employees will need to envision entirely new functions that can manage AI-first operations. Instead of teams of people who use AI to augment individual job roles, they need orchestrators with new skills who can manage AI across multiple domains and integrate insights that span traditional departmental boundaries.
Roughly two-thirds of executives say agentic AI will play a significant role in finance, sales, marketing, IT, supply chain, and research and development by 2030. These agents generally come in one of two forms: personal agents that empower employees to work smarter and enterprise agents that optimize end-to-end workflows.
Where should AI augment people—and where should people augment AI?
In healthcare, for example, 65% of executives say they’re empowering employees to use AI for task automation in their specific roles. It’s now possible for manual validation processes that once took months to be completed in hours. The efficiency gains could free up human expertise for patient-facing work—where empathy, intuition, and complex decision-making remain irreplaceable.
But these gains aren’t a given. Today, 68% of executives view current organizational structures as impediments to realizing AI’s full value. And by end of 2026, executives expect 56% of the workforce will require reskilling due to AI-driven automation.
Upskilling is essential, but the skills leaders are most focused on aren’t technical. Execs say problem-solving and innovation are the most important skills for their employees to have today—and they expect generative AI (genAI) to make these skills even more important over the next three years.
“We have to push our creativity to see how many things we can do without human intervention. That is a mandate.”
Jacobo Díaz García
CFO and Head of Digital Banking, Bankinter
5. Quantum will cause the next seismic shift.
As business leaders focus on staying ahead of the AI curve, they’re at risk of missing a seismic shift in quantum computing—a miscalculation that could leave even the most adaptive enterprises exposed.
While 59% of executives say quantum-enabled AI will transform their industry by 2030, only 27% expect to be using quantum computing by then. This gap between quantum’s potential and industry preparation creates massive opportunity for the organizations that act decisively today.
A few factors have emerged as the most powerful predictors of future success in quantum. For example, quantum-ready organizations—those that rank in the top 10% of our 2025 Quantum Readiness Index—are three times more likely to belong to multiple ecosystems than other organizations.
The organizations that expect quantum-enabled capabilities to drive the highest portion of 2030 revenues are also more focused on building strong ecosystem alliances and identifying early use cases that might help them gain quantum advantage. Yet only 32% of organizations are actively building quantum alliances that align with their competitive advantage.
The strategic imperative is to build the flexible operations, infrastructure, and partnerships needed to capitalize on quantum as it matures. This requires transitioning to a quantum-centric supercomputing architecture where quantum computers work in tandem with powerful high-performance computing and AI infrastructure.
By 2030, the organizations that have gained breakthrough quantum capabilities will be able to explore fundamentally different problem-solving capabilities that make new solutions practical.
But organizations must start preparing today. This means developing quantum skills and expertise, experimenting on real-world quantum hardware, and developing quantum-centric supercomputing infrastructure. The smarter enterprise won’t wait for quantum to arrive—it’s already building structures that will be able to translate quantum advantage into value as soon as it appears.
“Building a robust, proactive plan for quantum resilience is going to take some investment—and I deliberately use the word investment, because it’s not a cost.”
Kristie Chon Flynn
Data Protection Officer, Google
Download the full report for more in-depth analysis of our proprietary executive research, industry-specific data, insights from executive interviews, and steps organizations can take to position themselves for success in 2030 and beyond.
