AI Change Management: 41 Tactics to Use (August 2024)

As Matt Kropp told me in our interview, BCG has a 10-20-70 rule for AI at work:

• 10% is the LLM or algorithm
• 20% is the software layer around it (like ChatGPT)
• 70% is the human factor

This 70% is exactly why change management is key in driving AI adoption.

But where do you start?

As I coach leaders at companies like Apple, Toyota, Amazon, L'Oréal, and Gartner in our Lead with AI program, I know that's the question on everyone's minds.

I don't believe in gatekeeping this information, so here are 41 principles and tactics I share with our community members looking for winning AI change management principles.

41 Ways to Drive AI Change Management

As AI statistics show, this technology can drive massive improvements in productivity, creativity, and employee satisfaction.

Already, 66% of leaders wouldn't hire someone without AI skills.

And, according to Asana and Anthropic research, most companies are only at level two of the five stages toward AI maturity.

So how do you move up?

This is what the experts are saying:

Pre-Implementation

1. AI Policies. Develop clear AI policies, principles, and guidelines early in the adoption process. (Rebecca Hinds/Asana.)

2. Recognize AI Concerns. Understand that AI concerns evolve as maturity increases. Address skepticism early, then focus on ethical and responsible AI use as adoption progresses. (Rebecca Hinds/Asana.)

3. Set Ambitious AI Goals: Establish significant AI-driven goals, such as saving $100 million through AI initiatives. (Spatero/Microsoft)

4. Strategy and Objectives: Start with clear objectives for AI use, focusing on the desired impact rather than just implementing technology. (Matt Kropp, BCG X.)

5. Target High-ROI Functions: Prioritize AI rollouts in functions that can quickly demonstrate ROI, such as sales and customer support (Microsoft).

6. Cross-Department Implementation: Roll out AI tools to entire teams simultaneously to promote peer learning (Microsoft).

7. Internal AI Councils: Establish AI councils with representatives from various departments to oversee AI implementation (Microsoft).

8. AI Champions: Identify and promote internal AI champions who can advocate for and support AI adoption. (Moderna)

9. Invest in Secure AI Tools: Pay for premium AI tools that ensure data privacy and security, reducing concerns about data misuse. (Alexandra Samuel)

10. Create Connective Tissue with AI: Strive for AI integration that connects various tools and systems seamlessly. This enables a more holistic and efficient workflow, where AI serves as a true assistant in the flow of work. (Amy Leschke-Kahle).

11. Consider Your Own Model: Even just alongside a major LLM, consider training your own model, for example by working with Amazon Sagemaker, which can take your data and train a model. (Andy Wu/Harvard Business School).

12. Diversity in AI Development: Actively seek diverse perspectives in AI initiatives. This can be done by partnering with organizations focused on diversity or creating internal diversity-focused AI working groups (Helen Kupp Lee and Nichole Sterling/Women Defining AI)

13. Promote Ethical Use of AI: Address ethical concerns by ensuring transparency and responsible use of AI. This involves being open about AI interactions, enabling opt-in features for users, and continually improving AI processes to enhance trust and acceptance. (Stephen Creasy/McKinsey & Company)​.

AI Change Management – Training

14. AI Basics: Provide comprehensive training on AI fundamentals to all employees. (Spatero/Microsoft). Make sure people understand the history of AI, especially in your company (AJ Thomas/X.) Provide training on AI fundamentals to all employees, not just technical teams. (Rebecca Hinds/Asana.)

‍(See our recommendations for the best generative AI courses.)‍

15. Delegation Skills: Teach employees how to delegate tasks effectively to AI tools. (Spatero/Microsoft)‍

16. Prompting Skills: Train employees to move beyond traditional web search methods to more effective AI-driven approaches. (Spatero/Microsoft)‍

17. Judgment: Emphasize the importance of good judgment when interpreting AI outputs. (Spatero/Microsoft.) Ensure there’s always a “Human in the Loop.” (AJ Thomas/X)

18. Upskill and Certify Employees: Implement comprehensive upskilling programs. Develop badging systems to identify AI-ready employees and hold regular discussion groups to keep teams updated on best practices and emerging trends. (Stephen Creasy/McKinsey)

19. Provide tailored, function-specific AI training: Develop training programs specific to each department's needs (e.g., marketing, sales, engineering) rather than using a one-size-fits-all approach. (Rebecca Hinds/Asana.)

20. Curiosity as a Superpower: Foster continuous curiosity and learning within teams. Regular "curiosity sessions" or innovation workshops can help explore new AI technologies and brainstorm applications​. (AJ Thomas/X)‍

21. Evolving Ourselves: Understand what we do best, and where AI should take over. In this, be open to evolve ourselves. In particular, fight ‘human intuition’ about what ‘we should do.’ (Spatero/Microsoft, Alexandra Samuel, and Tomas Chamorro-Premuzic)

Leadership and Advocacy for AI Change Management

22. Senior Leadership Advocacy: Ensure CEOs and senior leaders actively promote the importance of AI. (Spatero/Microsoft)

23. Managerial Encouragement: Have managers encourage their teams to experiment with AI tools. (Spatero/Microsoft)

24. Empower Middle Managers: Middle managers play a crucial role in the adoption of generative AI. They can help shift the balance from administrative tasks to value-creating leadership. (Emily Field/McKinsey)

AI Change Management – Implementation:

25. Treat Employees like Grown-Ups: Trust employees to use AI responsibly and provide them with the autonomy to explore how AI can enhance their work (Amy Leschke-Kahle.)

26. Co-Design: Involve end-users in designing AI solutions to ensure they meet real needs and are readily adopted. (Matt Kropp/BCG X). Kickstart AI initiatives with interactive workshops and hackathons. (Edie Goldberg)

27. Design of Experiment: Encourage structured experimentation with clear hypotheses and critical evaluation through a "Design of Experiment.” (AJ Thomas/X)

28. Categorize: Have employees categorize tasks into those AI can’t do, can augment, or can automate. (Paul Leonardi)

29. Dedicated Experimentation Time: Allocate specific times for employees to experiment with AI tools. (Alexandra Samuel). 

30. Celebrate Learning: Encourage fun, creative exploration and celebrate learning outcomes, not just successes​. (Helen Kupp Lee and Nichole Sterling/Women Defining AI)

31. App Creation and Sharing: Encourage employees to develop small AI applications and share them within teams. (Christopher Fernandez/Microsoft HR) Use AI to create better jobs for humans by removing toil and enhancing job satisfaction. (Matt Kropp/BCG X)

32. Encourage Frequent AI Use: Set goals for frequent AI usage to foster familiarity and comfort with the technology among employees (Moderna).

33. Set Team Norms for AI Usage: Establish norms where it is expected that team members will use AI to summarize information before sharing it. (Alexandra Samuel)

34. Practical AI Applications: Encourage sharing practical, relatable examples of AI use in both professional and personal contexts to make AI more approachable. (Helen Kupp Lee and Nichole Sterling/Women Defining AI)

35. Set Up a Channel: Create dedicated channels on platforms like Teams or Slack for sharing AI experiences and seeking advice. (Moderna, Rebecca Hinds/Asana.)

‍Culture and Mindset

36. Pride in AI Use: Foster a culture where using AI is seen as a point of pride rather than a threat. At least create a culture where using AI-generated content is seen as normal and not as cheating. (Alexandra Samuel)

37. Job Security Assurance or Steps Ahead: Reassure employees that AI is a tool to enhance their roles, not replace them (Alexandra Samuel), or Transition roles by deepening tasks or upgrading them to more critical responsibilities. (Paul Leonardi)

Post-Implementation

38. Public Recognition: Use town halls, internal communications, and newsletters to celebrate AI successes. (Moderna)

39. Stay Updated Through Curated Sources: Identify and follow 3-4 key publications, thought leaders, or conferences in your industry to keep up with rapidly evolving AI developments. (Rebecca Hinds/Asana.)

40. Implement Structured Evaluation of AI Initiatives: Move beyond sporadic or unstructured attempts to assess AI success. Regularly evaluate the impact and effectiveness of AI implementations.

41. Conduct Regular Employee Surveys on AI Adoption: Collect feedback from employees about their experiences with AI and use their inputs to make tangible changes in how AI is implemented and used.

The Bottom Line

It's not going to be easy. But what good thing ever was?

These 41 expert-approved tips should help you get to AI maturity faster, outperform your peers, and create a better place to work.

For more, feel free to contact me, or check out Lead with AI – the course and community for business leaders embracing AI in their work, team, and org.