Enterprise adoption of AI is increasing but old challenges remain

Eden Zoller, Chief Analyst, Applied AI

Eden Zoller, Chief Analyst, Applied AI Enterprises continue to face significant barriers with adopting and scaling AI--whether predictive or generative--with security and data privacy concerns cited as the primary obstacle, followed by compliance issues, integration complexity, and talent shortages. Many enterprises struggle with legacy systems not designed for modern AI workflows, creating compatibility issues with technology stacks, data formats, and APIs. Data quality remains a fundamental barrier, while budget constraints and unclear ROI further complicate investment decisions.Generative AI both amplified existing challenges and introduced some new ones, including concerns about risks from fraud, inaccurate content generation, copyright infringement, biased outputs, and loss of trust.Despite these challenges, confidence in AI remains strong, and most enterprises expect AI budgets continue to increase.
While economic uncertainty, unclear ROI and AI hype may dampen some of these plans, generative AI’s agentic cousin has accelerated interest and experimentation, especially since agentic AI allows to build on top of, and extend, generative AI investments.Organizations that overcome this adoption barriers report significant benefits, particularly in R&D acceleration and customer intelligence improvements. This indicates that addressing technical dept and other adoption barriers can unlock substantial business value as AI initiatives mature, and more AI proof of concept projects transition into production.

How AI Vendors Can Help

AI vendors can address these challenges by developing comprehensive solutions that prioritize security-by-design principles and robust data privacy protections. They should offer tailored AI governance frameworks that align with industry-specific regulatory requirements, particularly for smaller enterprises struggling with compliance.Vendors should provide:

Integration middleware bridging legacy systems with modern AI workflows

Low-code/no-code platforms to help overcome talent shortages

Scalable infrastructure with edge AI capabilities and lightweight models

Clear ROI measurement tools

Nothing builds deeper loyalty or fosters recurring engagements more effectively than customer success stories and genuine partnership: actively listening, continuously learning, and openly sharing knowledge and experience. This collaborative approach will allow organizations to harness AI’s full potential.Further reading: