Overcoming the Key Barriers to AI Adoption in Business

Ever wondered why companies are struggling to adopt AI? It’s no surprise that data availability and quality are common blockers—garbage in, garbage out comes to mind—however, many of the key obstacles have little to do with data at all.

In a recent 2025 survey of marketing professionals and CEOs conducted by the Marketing Artificial Intelligence Institute, other pain points emerged. 

Lack of education and training (62 % of respondents), awareness and understanding (52 %), and lack of strategy (40 %) were the primary barriers to integrating AI into marketing functions. 

Perhaps a little surprising, only 38 % of marketing organizations have AI education or training, and only 34 % have generative AI policies and only 25% maintain AI roadmaps. With the plethora of new tools and capabilities changing almost daily, it is perhaps not surprising that marketing departments are struggling to keep up!

The adoption of AI in marketing departments is progressing, but several factors contribute to its uneven—and sometimes slow—integration. Key challenges include limited AI literacy, organizational hesitancy, and structural data barriers.

While AI tools are increasingly prevalent, their adoption in marketing remains inconsistent. Many employees appear to be bringing their own AI tools to work because authorized, supported options are lacking. This is of course very risky: sharing company data with a large-language model may expose the business to data-security breaches, GDPR violations, model hallucinations, and inconsistent marketing outputs. So what are the primary gaps hindering adoption?

1. Knowledge Gaps and Training Deficits

A significant barrier to AI adoption is the lack of understanding and training among marketing professionals. Surveys reveal that around two-thirds of marketers cite insufficient knowledge about AI as the primary obstacle. This is not unexpected: martech vendors are only now rolling out AI features, and a flood of new providers enters the market each month. Moreover, only a minority of companies have established AI education or training programs, which compounds the problem.

2. Data Barriers

Garbage in, garbage out applies here too: if a marketing team feeds sensitive data into a model, that data must be accurate and high-quality. Data security tops the list of concerns, with marketers wary of how AI systems handle confidential information. At a recent AWS conference I attended, it was noted that a proliferation of AI proofs of concept never reach production, in part because underlying data is messy or incomplete.

3. Job Fears, Resistance, and Algorithm Aversion

Cultural factors also slowing adoption. Many professionals exhibit “algorithm aversion,” a reluctance to trust AI systems over human judgment—especially in creative or ethical contexts. This scepticism is compounded by fears of job displacement and a desire for human oversight. Those fears are not however unfounded: job cuts linked to AI efficiencies are already appearing in the headlines and I fear we will see companies announce job freezes in the near term

4. Lack of Strategy

Some companies have purchased enterprise licenses for ChatGPT or other models, but that does not mean they are using AI programmatically to achieve specific goals. Without a strategy, clear goals and measurements of success, licenses will become expensive shelfware.

The temptation is to delay until the landscape stabilises, but at today’s pace of change, the tools we use now will look primitive in two years. The better path is to start now: define a marketing AI strategy, identify the processes and roles that can be aided by AI, and articulate the benefits—higher ROI, cost savings, improved conversion rates, and so on. Tools will come and go, but a clear roadmap and set of AI policies will evolve with you as you learn and scale AI adoption