IBM’s latest executive survey predicts a 150 % surge in AI investment and sees 79 % of leaders banking on AI as a primary revenue source by 2030 but warns many lack integration strategies
Artificial intelligence is no longer a speculative experiment reserved for early adopters. According to new data from IBM’s Institute for Business Value, nearly eight in ten executives (79 %) now believe AI will be a primary driver of revenue by 2030, and companies expect AI investment to surge by roughly 150 % over the next five years. But the report also sounds a clear alarm: unless AI is fully integrated into the core architecture of enterprise systems, not just isolated as proofs-of-concept, many organizations risk falling behind. In short, 2026 is the year AI must become the backbone of business models and operations.
Watershed Moment for AI in Business
The headline numbers from the IBM Institute for Business Value’s latest report, titled The Enterprise in 2030, are striking: 79 % of senior executives believe AI will be a significant revenue driver by 2030, up dramatically from roughly 40 % today, yet only 24 % have a clear view of where that revenue will come from. Meanwhile, AI investments are expected to increase by approximately 150 % between now and 2030, reflecting a shift from pilot-stage experiments toward truly strategic commitments.
That combination of optimism and ambiguity defines the era we now enter: AI is moving from isolated proofs of concept to becoming the central nervous system of enterprise. And 2026 is the year that transition must accelerate.
Strategic Imperative: From Efficiency to Innovation
One of the most revealing trends uncovered in the IBM study is where companies plan to allocate their AI budgets over the next five years.
Today’s Reality: Efficiency First
Currently, nearly half (47 %) of AI spending is focused on efficiency and automation, using AI to streamline tasks, reduce waste, and augment human productivity.
Tomorrow’s Vision: Innovation and Growth
By 2030, executives expect that 62 % of AI spending will be dedicated to innovation, meaning new products, services, business models, and competitive differentiation rather than incremental cost savings.
This shift signals a broader evolution: AI is no longer just a tool for getting existing processes right. It is becoming a generative platform for growth, shaping strategy, market positioning, customer engagement, and operational architecture itself.
Revenue Expectations and Strategic Uncertainty
While 79 % expect AI to meaningfully contribute to revenue by 2030, only 24 % currently understand clearly how those gains will materialize. That gap underscores two truths:
- Leaders believe in AI’s transformative potential.
- Most organizations have not yet figured out how to unlock it.
This paradox, high expectation but low clarity, reveals a strategic divide between ambition and execution. Wu Tsai, Senior Vice President at IBM Consulting, frames it bluntly: “AI won’t just support businesses, it will define them.”
Yet without a roadmap for integration, linking AI to core processes, products, and customer outcomes, these lofty expectations risk becoming hollow promises rather than realized gains.
Productivity Gains and Enterprise Transformation
Productivity is the currency of the modern enterprise, and the IBM research estimates that AI could boost productivity by about 42 % by 2030. Leaders also expect to capture most of their AI-enabled productivity gains by then. However, the research points to a critical caveat: 68 % of executives fear their AI initiatives will fail due to lack of integration with core business activities.
This paints a nuanced picture: while AI can drastically enhance output and efficiency, capturing those gains at scale requires more than isolated tools or pilot programs. It requires ecosystem thinking, where AI is woven into enterprise architecture, data flows, decision systems, and customer interfaces.
Shift to Innovation-Led AI Spending
The IBM study makes a fundamental point about how AI budgets are changing:
Today (2025): 47 % of AI spend is focused on efficiency.
2030 (Projected): 62 % of AI spend will go toward innovation, new revenue streams, product and service differentiation, and business model transformation.
This change reflects deep evolution. Early AI adopters concentrated on survival — cutting costs and eliminating repetitive tasks. But as platforms and capabilities mature, leaders are waging strategic bids for competitive advantage. They see AI not as a tool to sustain the status quo but as a driver of future markets.
In this paradigm, efficiency and innovation are not separate goals. Innovation becomes the most effective path to efficiency at scale, because novel business models and revenue flows can fundamentally alter productivity curves.
Organizational and Workforce Realities
The shift to AI-first organizations will not just change technology, it will reshape people, skills, and leadership itself.
Leadership and Roles
By 2030:
- About 25 % of enterprise boards are expected to have an AI advisor or co-decision maker.
- 74 % of executives surveyed say AI will redefine leadership roles across the enterprise.
- Two-thirds believe AI will create entirely new leadership positions.
Leadership in an AI-enabled enterprise will require hybrid skills, combining technical fluency with strategic insight and human judgment.
Workforce Transformation
The study also identifies workforce impacts:
- Job roles are becoming shorter-lived, according to 67 % of executives.
- 57 % expect most current employee skills to become obsolete by 2030.
- Employees with strong problem-solving and innovative thinking capabilities will be more valuable than ever.
The message is clear: AI does not replace people. It reframes the work that people do.
Organizations that win will equip their talent with the mindset and capabilities needed to work in hybrid human-AI systems where creativity, judgment, and strategic intuition complement AI automation.
Multi-Model and Small Language Model (SLM) Revolution
The IBM research also forecasts a shift in the kinds of AI models that will power enterprise systems:
- 82 % of executives expect their AI capabilities to become multi-model by 2030.
- 72 % expect small language models (SLMs) to surpass large language models (LLMs) in certain enterprise applications.
This suggests an evolution in architectural thinking: rather than a one-size-fits-all giant model, organizations will deploy ensembles of specialized models — smaller, faster, efficient, and purpose-built for domains such as finance, customer service, supply chain, or research support.
These models promise greater efficiency, reduced cost, and closer alignment with enterprise data and processes.
Integration Challenges and Strategic Bets
Despite the optimistic figures, the report highlights a sobering reality: 68 % of surveyed executives fear their AI efforts will fail because of lack of integration with core business strategy and systems.
This concern may be the most important insight of all: AI success isn’t just about technology adoption, it’s about enterprise architecture transformation. Organizations that treat AI as a separate capability will struggle. Those that embed AI into operational workflows, strategic planning, customer journeys, and competitive differentiators will thrive.
Conclusion: 2026 as the Defining Pivot Year
The message from IBM’s Institute for Business Value is unmistakable: 2026 is when AI must graduate from isolated experiments to becoming embedded in the core of enterprise architecture.
By 2030, AI will redefine competition, reshape workforce structures, and drive revenue growth in ways previously unimaginable. But only those organizations that take bold, integrated, and strategic action now, moving beyond cost savings toward innovation-led transformation, will win in the coming decade.
The future isn’t just about AI adoption. It is about AI integration, AI-led innovation, and organizational reinvention at scale. The next five years won’t just shape corporate strategy in 2030, they will determine which companies become classics of the AI era and which become cautionary tales.
