10 things that could go wrong during an AI transformation

AI transformations can bring significant efficiency gains and innovations to an organization. But without proactive training, these initiatives risk running into common pitfalls. Preparing your people for AI transformation can greatly reduce these risks.

Here are 10 potential setbacks in an AI transformation and the training approaches that can help prevent them:

1. Lack of clear understanding of AI’s role

AI can only be effective if everyone understands how it will be used and why it’s valuable.

The risk: Without clarity, team members might overestimate or underestimate AI’s capabilities, leading to confusion about goals and limitations. Misunderstandings about AI can also breed resistance if people feel their roles are threatened.

Proactive training solution: Hold foundational AI awareness sessions to explain AI’s role, capabilities and limitations in the organization. Encourage questions, making space to address any concerns about job displacement or impact. Employees will feel more secure and informed, resulting in a smoother transition.

2. Fear of job displacement

AI transformations often bring concerns about job security to the surface.

The risk: Many employees worry that AI will replace their roles, creating anxiety and resistance. This fear can undermine team morale and engagement.

Proactive training solution: Implement workshops to address how AI will enhance, not replace, human work. Leaders can facilitate conversations around the potential for upskilling and career development opportunities. Role-specific sessions can highlight how AI will support employees’ work, not replace it, shifting the perception from threat to asset.

3. Poor collaboration between tech + non-tech teams

Successful AI transformation requires alignment between technical and non-technical teams.

The risk: AI transformations rely on effective collaboration between technical experts and non-technical teams. Without this collaboration, misunderstandings, misaligned goals and wasted resources can arise.

Proactive training solution: Invest in cross-functional communication training to improve collaboration. Teach both tech and non-tech teams to discuss AI capabilities in accessible terms. Create opportunities for shared learning, like job shadowing, to enhance empathy and understanding between teams, which boosts the efficiency and relevance of AI initiatives.

4. Misaligned expectations for AI outcomes

Clear, realistic expectations help keep all stakeholders aligned on AI’s potential and limitations.

The risk: When expectations aren’t aligned, stakeholders may expect unrealistic outcomes or misunderstand what AI can deliver. This disconnect often results in frustration or a perceived failure of the initiative.

Proactive training solution: Train leaders and stakeholders to set realistic AI project goals. This could include providing case studies on successful AI projects to outline what to expect. Aim to equip leaders with frameworks for project measurement so they can realistically assess AI outcomes and adjust goals as needed.

5. Insufficient data literacy

Data literacy is helpful for employees to understand and engage with AI effectively.

The risk: AI projects can rely heavily on data, and team members lacking data literacy may struggle to contribute meaningfully, slowing project progress.

Proactive training solution: Offer data literacy training focused on working with AI, including foundational concepts of data collection, quality and ethics. Employees who understand data’s role in AI can better engage in these projects and contribute to high-quality outcomes.

6. Ethical and compliance concerns

An ethical approach to AI helps ensure responsible use and maintains trust.

The risk: AI implementations can sometimes raise ethical and legal concerns, especially regarding data privacy and algorithmic bias. These issues can lead to regulatory challenges or even reputational damage if not handled proactively.

Proactive training solution: Deliver ethics training focusing on responsible AI use, data privacy and bias. Role-specific sessions should address relevant regulatory standards and best practices. Training people to spot potential ethical issues early helps them act responsibly, aligning the organization’s AI use with values and laws.

7. Over-reliance on AI for decision-making

AI works best as a complement to, not a replacement for, human decision-making.

The risk: People may start depending too heavily on AI for decisions, losing sight of the importance of human judgment and critical thinking. This can lead to poorly made decisions, especially in complex or nuanced situations.

Proactive training solution:  Provide decision-making workshops that highlight the importance of human oversight in AI-augmented work. Emphasize scenario-based training where AI outputs are interpreted critically. Help teams build confidence in combining AI insights with human judgment, creating a balanced approach.

8. Skills gaps in AI-related competencies

AI transformation requires new skills that might be missing within the current workforce.

The risk: AI transformations often require skills that many employees might lack. Skill gaps can lead to delays and underutilization of AI tools.

Proactive training solution: Identify essential AI-related skills (such as prompt engineering) and set up specialized training paths. Upskilling in these areas empowers employees to use and understand AI tools better, improving project efficiency and reducing reliance on external experts.

9. Lack of adaptability to evolving technology

Adaptability is essential for teams to keep pace with rapid AI developments.

The risk: AI is constantly evolving, and employees who are uncomfortable with change may struggle to adapt, stalling innovation.

Proactive training solution: Build a growth culture through adaptability training emphasizing resilience and openness to new technologies. Integrate regular learning labs focused on the latest AI developments to keep employees up-to-date and comfortable with ongoing evolution. By fostering adaptability, you prepare teams to thrive in dynamic AI environments.

10. Insufficient focus on AI’s human impact

A human-centered approach to AI aligns technology with user needs and values.

The risk: When teams become too focused on technical achievements, they may overlook AI’s impact on customers, employees or communities. This can result in AI implementations that lack empathy or fail to meet users’ actual needs.

Proactive training solution: Encourage empathy-centered training that highlights the human impact of AI, including its effects on end-users. Role-playing and design-thinking workshops can help employees approach AI from a human-centric perspective. This approach leads to AI applications that genuinely serve people, aligning the technology with the organization’s values.

AI transformations are promising but challenging journeys that need thorough preparation on both the technical and interpersonal fronts. With the proper training, organizations can prepare their people to anticipate and manage potential pitfalls, empowering them to drive AI initiatives that are successful, ethical and human-centered.

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