Overcoming Barriers to Enterprise AI Adoption: Strategic Intelligence

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 in 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. GlobalData 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 GlobalData analyst research and expertise, primary research interviews with senior enterprise decision-makers, and polls conducted across GlobalData’s Verdict and Business Trade Media International networks of B2B websites.

Four Pillars of Enterprise AI Adoption

GlobalData’s framework identifies four core pillars that support successful enterprise AI adoption:

1. AI Strategy

Enterprises need a clear strategy for determining where AI can create value.

The report examines strategic barriers that may prevent organizations from moving beyond experimentation. It also considers the decisions enterprises face when scaling AI across business processes and customer experiences.

2. Data and AI Technology

AI depends on appropriate data and technology foundations.

Enterprises must assess whether their existing technology environments can support AI deployment at scale. They also need to determine where to build capabilities internally and where to work with external providers.

3. AI Talent and Skills

AI talent remains a major adoption challenge.

The report identifies two broad types of AI skills shortages:

• Technical skills
• Foundational skills

The rapid pace of AI development is also changing the skills organizations require.

You can use the analysis to understand the talent challenges that may affect AI implementation and scaling.

4. AI Governance

Responsible AI governance is essential as enterprises deploy AI more widely.

The report identifies 15 key AI risks spanning areas including:

• Transparency
• Accountability
• Safety
• Reliability
• Social impact

Despite the importance of governance, considerable gaps remain.

The report highlights the need for enterprises to develop governance strategies that keep pace with AI deployment.

Enterprise AI Agents: Build, Buy or Partner?

AI agents are creating another important strategic decision for enterprise technology leaders.

Organizations must consider whether to deploy AI agents to automate business processes and improve customer experiences.

They must also determine the most appropriate implementation model.

Options include:

• Building AI agents internally
• Working with specialist system integrators
• Using external solution providers
• Adopting a hybrid approach

The report examines these strategic choices and the factors influencing them.

It also highlights where in-house development may offer greater control over customer experiences, performance, data, and proprietary intellectual property.

AI Skills Gap: The Enterprise Talent Challenge

The availability of AI talent is a significant barrier to enterprise adoption.

Nearly half of enterprise leaders cited skills gaps as a major barrier to AI adoption in the 2025 McKinsey survey referenced in the report.

At the same time, AI’s rapid development is changing the skills required by organizations.

This creates a difficult challenge for enterprises. They need to build current capabilities while preparing their workforce for continued technological change.

The report can help you assess the implications for AI talent strategy, workforce planning, and capability development.

Responsible AI Governance: Managing Enterprise AI Risk

Enterprise AI adoption can move faster than governance capabilities.

Competitive pressure, limited internal expertise, and regulatory ambiguity may cause organizations to deploy AI before appropriate controls are established.

The report highlights a significant governance gap. One in four businesses is not taking steps to adopt a responsible AI strategy.

You can use the analysis to understand the governance challenges associated with enterprise AI and consider how governance frameworks may need to evolve alongside deployment.

Scope

The report examines:

• Four pillars of enterprise AI adoption
• AI strategy barriers
• Data and technology challenges
• AI talent and skills shortages
• Enterprise AI governance
• AI risk
• AI agent deployment
• Build-versus-buy decisions
• Partnership and system integrator models
• Responsible AI adoption
• Strategies for overcoming adoption barriers
• Report-specific methodology
• GlobalData’s thematic research methodology

How Companies Can Use This Enterprise AI Report

This report is designed to support practical AI strategy and transformation decisions.

CEOs and business leaders can use it to understand the organizational barriers that may prevent AI from delivering value.

CIOs and CTOs can assess technology, data, AI infrastructure, and build-versus-buy decisions.

Chief AI Officers can use the framework to structure AI adoption and scaling priorities.

Chief Data Officers can evaluate the data foundations required for enterprise AI.

HR and talent leaders can assess technical and foundational AI skills gaps.

Risk and compliance teams can use the governance analysis to identify areas requiring greater attention.

Technology providers and system integrators can understand where enterprises may require external support.

Strategy and transformation teams can use the framework to benchmark AI readiness and prioritize investment.

Who Should Buy This Enterprise AI Adoption Report?

This report is particularly relevant for:

• CEOs and C-suite executives
• CIOs
• CTOs
• Chief AI Officers
• Chief Data Officers
• Digital transformation leaders
• Enterprise architects
• AI strategy leaders
• IT strategy teams
• Data and analytics leaders
• HR and talent executives
• Risk and compliance leaders
• Technology procurement teams
• System integrators
• AI solution providers
• Management consultants
• Investors and corporate strategy teams

If you are responsible for implementing, scaling, governing, or investing in enterprise AI, this report can support your decision-making.

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 that 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.

Reasons to Buy

This report goes beyond explaining why AI matters.

It helps you identify what may be stopping your organization from scaling AI and where intervention may be required.

You can use the analysis to:

• Identify key enterprise AI adoption barriers
• Assess AI strategy challenges
• Evaluate data and technology requirements
• Understand AI talent shortages
• Strengthen responsible AI governance
• Assess AI agent deployment decisions
• Consider build, buy, and partnership models
• Identify organizational barriers to scaling
• Support AI transformation planning
• Benchmark enterprise AI readiness
• Prioritize AI investments and capabilities

As a result, the report can help you turn broad AI ambitions into a more structured adoption strategy.

Benchmark Your Enterprise AI Readiness

AI adoption is not simply a technology challenge.

Strategy, data, technology, talent, and governance all influence an organization’s ability to scale AI.

The report provides a consistent framework for assessing these areas. You can use it to identify capability gaps and compare your organization’s priorities against the broader challenges facing enterprises.

This may help you determine where additional investment, expertise, or organizational change is required.

Research Behind the Enterprise AI Adoption Analysis

The findings draw on GlobalData’s analyst research and expertise.

The analysis also incorporates:

• Primary research interviews with senior enterprise decision-makers
• Polls conducted across GlobalData’s Verdict network
• Polls conducted across Business Trade Media International’s B2B website network

This combination provides both analyst interpretation and enterprise decision-maker perspectives.

Move From AI Experimentation to Enterprise Scale

AI adoption is accelerating, but scaling it successfully remains challenging.

Enterprises must address technology, talent, strategy, and governance together. Otherwise, investments may fail to translate into meaningful business value.

This report provides a practical framework for understanding those challenges.

If your organization is moving from AI pilots toward enterprise-wide deployment, the analysis can help you identify the barriers that matter most and determine where to focus your next AI investment.