People & Process are the Cornerstone of Successful AI Rollouts

Corporations across industries are abuzz with discussion and adoption of AI. According to IBM’s release in early 2024, 35% of organizations are using AI and more than 40% are exploring/considering it. While the organizational appetite to dive into AI is clear, the organizational readiness to support this endeavor is murky. A recent Forrester study showed that without proper AI planning, training, and guardrails, organizations’ lack of experience and skill in transforming internal processes may only get more untenable. This will cause more wasted time and money, failed initiatives, and change resistance. 

While AI technology is undoubtedly part of our near future, it should not be adopted without consideration of internal people and processes. Prosci’s 2023 study showed that only 33% of organizations evaluate their organization’s change readiness prior to undergoing an organizational change and this evaluation and preparedness correlates closely with successful initiatives. From the use of generative AI to produce content to use of AI to craft chatbots, the possibilities of how AI can transform businesses seem limitless. However, their impact can be profoundly negative if people and process are not at the forefront of consideration. 

Like the data management phrase “garbage in, garbage out,” when AI is anchored to flawed processes or not anchored to organizational procedures at all, you risk training AI to further automate poor processes and impact individuals’ adoption. 

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Here are some steps we recommend to begin your AI journey successfully: 

• Prioritize your business needs: Many organizations are bringing on AI without a clear scope. Start by setting business priorities and then considering where AI can help support. Start small and then expand. This will support your ability to finetune your AI process and technology while also supporting people within your organization to be brought into the change with less change angst. 

• Optimize your processes: Begin documenting your core internal processes and evaluating them for areas of improvement. This should include nomenclature documentation. All of this upfront work will pay dividends to support accurately training AI. It also minimizes the risk of having AI automate broken processes. 

• Mitigate bias: With great technology comes great responsibility. AI decisions should not be made in a vacuum. Form a coalition of individuals across your organization and even trusted advisors to create an interdisciplinary and representative group to help review AI plans and training for bias. It likely wouldn’t be intentional, but still has a negative impact if individuals with similar backgrounds and focal areas are the only ones solving for AI. Representation matters. 

• Govern it: Process governance around what AI is going to be used for and what it should not be used for is essential. Setting boundaries and identifying internal standards and expectations will help you to ensure your data is protected, your AI usage is understood, and that AI will work to support you. Otherwise, you risk AI running amuck.  
  

Whether you’re already on your AI journey or just looking into it, do yourself a favor; pause to plan for how this will impact your organizations’ people and processes and support both of those areas. Don’t focus solely on the technology.  

Prioritize process optimization and change management to ensure that technology effectively aligns with your organizational objectives.