Artificial Intelligence (AI) is a major focus of corporate transformation strategies. Executives often highlight AI’s potential to improve productivity, enhance decision-making, and create competitive advantage. Yet many organizations are struggling to turn that potential into real value. A large number of companies report limited business impact from AI deployments, and adoption remains a significant challenge.
The Reality of AI Adoption
Research shows a clear gap between investment in AI and employee experience:
- High investment, low realized value: In a 2025 Gartner survey, 88 percent of organizations reported they had not seen significant business value from AI tools.
- Interest does not equal adoption: Another Gartner survey found 65 percent of employees are excited to use AI, but 37 percent do not use it even when they have access, often because coworkers are not using it or guidance is lacking.
- Trust issues persist: Employees frequently express concerns about AI’s reliability and governance, which reduces willingness to rely on outputs.
Employees are generally curious about AI but hesitate to adopt it when tools are unfamiliar, poorly explained, or disconnected from real workflows.
Why Trust and Interpretation Matter
AI systems often act as “black boxes,” producing outputs that employees find difficult to understand. When users cannot interpret recommendations, they are less likely to trust or apply them, especially in high-stakes situations. Lack of governance, training, and defined use cases further complicates adoption, and employees may use AI without proper guidance, creating security and compliance risks.
Gallup research shows that employees are more likely to use AI regularly when managers actively demonstrate its relevance to their work.
The Role of Communication and Analytic Translation
A key barrier to adoption is the communication gap between technical teams and business users. This is where analytic translators play a crucial role. Experts like Dr. Wendy Lynch emphasize that translators bridge this gap by converting complex AI outputs into actionable insights that align with business priorities and workflows.
Without this translation, even advanced AI tools can produce outputs that seem irrelevant or confusing, reducing trust and adoption.
Human and Organizational Factors
The challenges of AI adoption are organizational, not just technical:
- Clear relevance: Employees adopt AI more readily when it addresses real tasks and outcomes.
- Manager support: Leadership that demonstrates and coaches AI use increases adoption.
- Governance and training: Policies and practical training help employees feel confident using AI.
Without these factors, even significant technology investments can fail to deliver results.
How Companies Can Close the Gap
To maximize AI investments, organizations should:
- Ensure AI outputs are explainable and meaningful to end users.
- Employ analytic translators to connect technical insights with business priorities.
- Integrate AI into actual workflows rather than forcing employees to adapt to technology.
- Implement governance and training to build trust and confidence.
- Demonstrate clear business impact from AI tools to encourage adoption.
Conclusion
AI has the potential to transform work, but this potential is often unrealized because adoption challenges are not addressed. Tools that employees do not trust, cannot interpret, or do not use end up as expensive shelfware. Focusing on people, communication, governance, and practical relevance can turn AI from a costly experiment into a reliable driver of productivity. Analytic translators, thoughtful change management, and alignment with real business problems are critical for making AI work for employees and organizations alike.
