Strategic Intelligence: Overcoming Barriers to Enterprise AI Adoption

Artificial intelligence (AI) has been the hottest enterprise technology since OpenAI released ChatGPT in November 2022, kick-starting the generative AI boom. Despite substantial investment and strategic focus on AI over the last three years, most enterprises remain in the early phases of adoption and have yet to realize meaningful value from AI. The analyst has developed a framework that identifies four core pillars for successful enterprise AI adoption: strategy, data and technology, talent, and governance. This report will outline the key barriers to enterprise AI adoption for each pillar, along with strategies to overcome them.

Scaling AI requires significant resources and presents major challenges, but it is crucial if enterprises are to derive value from AI. Many enterprises have been unwilling or unable to commit to such a complex, expensive, and high-stakes transition. However, those with the resources must commit and do the hard work to scale AI. Not doing so would be a strategic failure, potentially ceding ground on AI to competitors.

Every enterprise needs an AI governance strategy to deploy AI responsibly. Enterprises face 15 key AI risks spanning transparency, accountability, safety, reliability, and social impact. Despite these risks, one in four businesses is not taking any steps to adopt a responsible AI strategy, underscoring considerable AI governance gaps.

Our findings are based on the analyst analyst research and expertise, primary research interviews with senior enterprise decision-makers, and polls run across the analyst’s Verdict and Business Trade Media International networks of B2B websites.

Key Highlights

• Enterprises must decide whether to deploy a suite of AI agents to automate business processes and improve customer experiences, and, if so, whether to build them in-house, work with specialist system integrators, or take a hybrid approach. A 2025 Capgemini survey found that 62% of organizations prefer partnering with solution providers and system integrators to implement or tailor AI agents within existing product suites. Drivers include the ready availability of pre-built agents and out-of-the-box integrations with legacy systems, reducing time-to-value. Enterprises should build AI agents that can be embedded in customer-facing products or core IP, where in-house development offers greater control over user experiences, performance, and data while reducing vendor dependency.
• Demand for AI talent continues to outpace supply, with nearly half of enterprise leaders citing skills gaps as a major barrier to AI adoption, according to a 2025 McKinsey survey. AI’s rapid pace of development is shrinking the half-life of many skills. A 2025 World Economic Forum report found that employers expect around 40% of workers’ core skills to change between 2025 and 2030, with AI and technological literacy becoming increasingly important. Enterprises, educational institutions, and governments globally face a tough battle to develop the current and next generations of AI talent. Our research indicates enterprises face two key types of AI skills shortages: technical and foundational.
• Considerable AI governance gaps exist as intense competitive pressure to deploy AI, limited in-house AI governance expertise, and regulatory ambiguity push enterprise AI adoption ahead of controls.

Scope

• This report analyze the core pillars for successful enterprise AI adoption, the key challenges associated with each pillar, and the strategies to overcome them.

Reasons to Buy

• This report is essential reading for enterprise leaders seeking to implement and scale AI technologies. It provides a comprehensive analysis of the core pillars of successful enterprise AI adoption, the key challenges associated with each pillar, and the strategies to overcome them.