IBM Survey: 85% of Firms Lag Behind in AI Implementation

The rapid growth of AI has created distinct tiers of corporate adoption, with most organisations struggling to implement AI effectively across their operations. New research from IBM indicates that only 15% of firms have established themselves as leaders in AI implementation, while the majority remain in early experimental phases.

The gap between leaders and learners has emerged during a period of unprecedented AI tool availability, with large language models and machine learning systems becoming widely accessible to enterprises. However, the mere presence of AI capabilities has not translated into successful deployment for most organisations.

The findings emerged from IBM's ‘AI in Action’ report, based on research conducted by polling firm The Harris Poll across 2,000 firms in five major economies including the United States, United Kingdom, India, Japan and Germany. The research targeted organisations with annual revenue exceeding US$500m or more than 1,000 employees.

Divide between AI investment and data strategy

The research identifies a clear divide between firms leading in AI adoption and those following behind. Among organisations classified as ‘AI Leaders’, 71% report an aggressive investment approach to artificial intelligence – technologies that enable computers to simulate human intelligence and decision-making. This contrasts with 19% among firms designated as ‘Learners’.

“Of the organisations that were considered AI Leaders, two-thirds reported that AI has already driven 25% or greater improvement in their revenue growth rate,” says Shobhit Varshney, VP & Sr. Partner, Americas AI Leader at IBM Consulting.

AI Leaders and Learners: Differences in management alignment

The data reveals substantial differences in executive coordination between the two groups. Among AI Leaders, 72% report alignment between C-suite executives and information technology leadership on AI maturity goals, compared with 36% of Learners.

These leading firms focus investment on four primary areas: customer experience systems, IT operations and automation tools, virtual assistants – software programs that interact with users through text or speech – and cybersecurity measures to protect against digital threats.

The research also indicates that 85% of AI Leaders follow a structured roadmap for AI implementation rather than taking an opportunistic approach. This roadmap encompasses four dimensions: strategy, toolkits, data management and applications.

Strategy requires vision and investment, while toolkits must be supported by technical staff and flexible infrastructure. Data management focuses on accessibility and governance, with applications addressing targeted use cases.

“A Learner will typically copy predefined scenarios using out-of-the-box technologies. But a Leader develops custom innovations,” says Dr. Stephan Bloehdorn, Executive Partner and Practice Leader for AI, Analytics and Automation at IBM Consulting DACH.

AI Leaders display enhanced technical capabilities

The report highlights how AI Leaders demonstrate greater technical capability in customising artificial intelligence systems. Sixty-one per cent report using Application Programming Interfaces (APIs) – software intermediaries allowing applications to communicate – to develop solutions. This compares with 28% among Learner organisations.

Data management emerges as a critical factor, with 61% of AI Leaders expressing confidence in their ability to access and manage organisational data for AI initiatives, versus 11% of Learners.

The Harris Poll, a market research firm operating since 1963, conducted the research between February and April 2024. The survey targeted IT and business decision-makers with deep knowledge of their company's AI-based tools and processes.

The study covered five major markets – the United States, Japan, Germany, the United Kingdom and India – focusing on large enterprises with substantial revenue or employee numbers. This approach aimed to capture insights from organisations with the resources to implement significant AI initiatives.

The findings emphasise that successful AI implementation requires a combination of strategic vision, investment in technical infrastructure and robust data governance frameworks. Leaders in the field demonstrate particular strength in developing custom solutions rather than relying on pre-packaged options.

As Shobhit Varshney explains: “We dove into the data to uncover how these AI Leaders were implementing AI within their businesses that could help others learn from their success.”

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