January 25, 2024 | 4 Min

Why Change Management is Critical for Successful AI Adoption

Mike Badamo
AuthorMike Badamo
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AIEcommerce

AI is here to stay, and your organization will use it at some point – probably sooner than you expect. Following some change management principles will help ensure that the adoption of AI provides value to your employees, your organization and its shareholders, as well as your customers.

What is change management?

Change management is a human-centered practice of guiding your organization through transition. As you look to implement AI, it’s critical to have a structured approach and set of processes that confidently leads your workforce into the new way of working. The best change management comes from: clear vision and purpose, strong leadership, effective communication, employee involvement, training and development, incremental changes, flexibility and adaptability, celebrating successes, monitoring and evaluation, and sustained change.

I’ve come up with four broad phases for getting AI into your organization and evolving to a new business-as-usual state. These phases will have many sub-phases, or maybe you’ll have several more main phases. That’s fine. The goal is to provide direction on how to embrace change, embrace AI, and give yourself a competitive advantage without wreaking havoc on what you’ve built so far.

Phase 1 – Acceptance, Assumptions, Awakening

Phase 1 is focused on accepting that AI is a force to be reckoned with and will fundamentally change our lives and businesses, along with checking some assumptions at the door before digging in, then working on awakening your team to the challenges and opportunities that lay ahead.

Some key aspects of phase 1 include:

  • AI is poised to be as disruptive and beneficial as the industrial revolution, the internet, and antibiotics

  • Immediate ROI is not the goal but rather laying the groundwork for the eventual transition from incremental to exponential

  • Thinking outside the box and “think big” have never been more applicable than with AI

  • AI has been around in various forms for decades, so success isn’t necessarily flashy but is certainly impactful; just because there isn’t hyped-up PR doesn’t mean progress isn’t happening

  • Technical maturity in your organization isn’t as high as you believe or need it to be, but your customers can and do move quickly with technology – your competitors will try, too

  • Executives and business leaders must be fluent in AI (even if it’s basic understanding) and boldly share their vision for the future with AI

  • Be transparent about the collective endeavor and its challenges, but don’t shy away from sharing success stories from your industry and others

Phase 2 – Educate, Delegate, Create

Phase 2 is focused on educating your workforce on AI and assembling teams of experts, delegating tasks to appropriate teams and resources to support them as they jump into AI work, and creating a new roadmap with content to evangelize the new way forward.

Some key aspects of phase 2 include:

  • Creating special, multi-disciplinary teams of passionate and capable individuals who will be tasked with researching and conceptualizing, creating proposals, narrowing positioning, and building expertise and excitement

  • Working to align the promises of AI against your strategy, and staying true to your purpose and values; know that your strategy is probably outdated and doesn’t explicitly include AI in a meaningful way

  • Look for inspiration and validation beyond your own walls and industry to avoid echo chambers, such as your own customers, analysts and academic research

  • Functional units and managers identify bottlenecks and areas for resource optimization prime for AI, along with processes, tasks, and projects that could benefit from AI

  • Data is elevated in importance, and with it — quality, governance, and preparation

  • Build a content library focused on internal enablement

  • A short list of potential paths is agreed upon and initial scopes and project teams are assigned

  • AI is evangelized internally, and your public-facing AI story takes shape and is readied for release

Phase 3 – Positive Trajectory, Cultural Shifts, Lessons Learned

Phase 3 is focused on completing AI projects that orient your progress up and to the right by delivering cumulative value through learning and forging foundations for more complex work, establishing a culture of learning and leveraging technology and agility in new and deeper ways, and applying lessons learned from early adoption to the broader organization.

Some key aspects of phase 3 include:

  • AI work begins in earnest, setting out to solve real problems

  • AI work will likely be low risk and potentially low reward, but should provide value and learning, iterating toward more complex work and more rewarding outcomes

  • Fail early and fast but fail forward, then celebrate wins to build confidence and competency

  • The special teams from phase 1 aren’t disbanded but instead become their own operating units or perhaps go back to lead in their original departments

  • The organization’s mindset shifts from skeptical interest to grounded optimism

  • Paths to ROI become clearer and easier

Phase 4 – Transformation, Strategic Achievement, Acceleration

Phase 4 is focused on business transformation through the bonding experience of surviving change, achieving strategic goals that create differentiation and true advantage, while accelerating the pace of innovation getting to value faster.

Some key aspects of phase 4 include:

  • Larger, more complex projects are underway and create a bridge to your longer-term vision

  • The foundations and frameworks of earlier phases result in a stable, functional AI-inclusive business that prides itself on operational agility and technological prowess

  • Broader organization structure changes are finalized and implemented, and pre-AI processes are retired

  • The business understands how to drive value from AI and is better equipped to evaluate and rapidly implement new AI – and technology generally

Change management isn’t easy but it’s necessary for taking your business to the next level by implementing AI. You must: understand the change, plan the change, communicate the change, engage employees throughout the change, provide support and training throughout the change, execute the change, monitor and evaluate outcomes, then sustain the change.

Congratulations! You’ve achieved your business transformation. You’re now a first-class AI organization.