Aptean Research Reveals Why General-Purpose AI is Falling Short of Corporate Expectations - Yahoo Finance Australia

Organizations Using Industry-Specific AI Report Better Operational Results

Aptean 2026 Artificial Intelligence Research

ALPHARETTA, Ga., July 28, 2026 (GLOBE NEWSWIRE) -- One year after an MIT study1 reported 95% of generative AI pilots were failing to deliver measurable value, new international research from Aptean, points to a primary cause: general-purpose AI lacks the relevance and accuracy that businesses demand. The survey also reveals the rise of "Shadow AI" tools, the need for increased governance, and continued optimism for the long-term impact of AI solutions.

The Case for Industry-Specific AI

Aptean's 2026 Artificial Intelligence Research found that over the past year companies using vertical, industry-specific AI outperformed those relying solely on general-purpose tools on seven of eight operational key performance indicators measured. "Seventy-seven percent of leaders told us general-purpose AI simply can't handle the complexity of their operations. That's not a knock on the technology; it's a mismatch of design," TVN Reddy, CEO of Aptean stated. "A generic model doesn't know your business, your compliance rules, or what success looks like in your industry. Purpose-built AI does, and that's often the difference between success and failure."

Survey respondents clearly stated their desire for vertical AI solutions, purpose-built for a single industry or function and trained on the data, terminology, and compliance rules specific to that domain. 88% of leaders said purpose-built, industry-specific AI is critical or very important to their business. When asked why they preferred industry-specific solutions, the top three reasons cited were easier integration with existing systems, more relevant and accurate outputs, and better strategic guidance from vendors who understand their sector.

The survey identified systems integration as a barrier to AI adoption. Eighty-two percent of respondents said integrating AI with core systems is more difficult than AI technology itself. Most businesses lack this capability in-house and turn to outside vendors for help: 92% of respondents agreed their organization would benefit from external expertise to maximize AI's value, and integration with existing systems ranked as the top factor in vendor selection.

A Call for Governance and Guardrails

After the failure of initial AI pilot programs, many employees elected to bypass company-sanctioned tools in favor of outside AI technologies. The research found 40% of employees using these Shadow AI tools at work without formal approval. This behavior stems from three factors: a gap between employee adoption speed and organizational readiness, a lack of sanctioned tools, and individuals are prioritizing their immediate needs over the organization's governance measures. Despite their own use of Shadow AI, 96% of respondents say a formal AI governance framework — with defined accountability and ethical standards — is necessary, and 75% say the lack of a governance framework is a major barrier to success.