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Creating an AI Transformation Strategy: Lessons Learned from Digital Transformation

Artificial intelligence, or AI, is a hot topic for today’s business. AI offers a lot of opportunities for increased efficiency, time savings, and advanced analytics, among other key benefits, for organizations across industries. However, with many different ways to leverage and incorporate AI into your business processes, it’s important that your organization has a clear plan for your AI roadmap. As companies embark on their AI transformation journey, we’re noticing that many lessons learned from the last wave of organizational transformation – Digital Transformation – are applicable. Below, we’ve outlined those learnings and components for success that are helpful when creating an AI transformation strategy.

Creating an AI Transformation Strategy 

1: Start with an AI Strategy   

From product innovation to market research to optimizing clinical trials, AI has a lot of possibilities. In 2024, it’s estimated that 22% of businesses are “aggressively pursuing the integration of AI across a wide variety of technology products and business workflows.” However, it’s important to first begin with setting up an AI strategy for your organization. Beware of falling into the trap of diving into AI, spending time and money investigating it, and not seeing a return on investment. Take the time upfront to define your business objectives. Where are you seeing gaps and bottlenecks? Are there quality issues? What are your opportunities for growth?  

Once you have identified the use cases for your AI strategy, determine if AI is needed. What do you want to accomplish with AI? Not every challenge can or should be solved with AI. Where AI is appropriate, it can have a powerful positive impact. Today, AI has been proven to be beneficial in predictive models or finding optimal inventory or allocations. For instance, AI can be a great solution for automating repetitive tasks or generating marketing content. However, AI is not yet as helpful in defining metrics or finding business-relevant patterns, though the models are evolving and improving. In addition, it may not be the best choice for automating tasks where personal judgment or translation into multiple cultures or languages is needed.  

Before your organization jumps headfirst into AI, focus on identifying the use cases and how you can build an overall cohesive strategy. When embarking on this journey of defining your AI strategy, it’s critical to include AI technical experts as key decision-makers to ensure you can see through the AI buzz and truly identify the potential of AI for your organization.  

2: Executive Leadership Buy-In is Essential 

Executive education on AI objectives is critical for success. We know that the vast majority of leaders are interested in AI, but at the same time, 65% can’t explain how their AI models make decisions. In fact, AI models are sometimes uninterpretable for the experts building the models, given that the complexity can be too much for even the best data scientists to comprehend.  

Executive leaders certainly don’t need to understand the AI models technically, however, they do need to understand the foundations of what AI is and how it can contribute to the success of their business. Ensuring executives are educated on the fundamentals of AI will enable them to truly understand the possibilities, limitations, risks, cost, and overall business implications of AI. Understanding the nuances of where an AI investment could take the organization can help visionary leaders look around the corner and have a better understanding of what’s next. 

Change management is a huge component of introducing AI to your organization. That change should be driven from the top down. When executive leadership teams understand the impact and possibilities of an investment in AI, it’s more likely to gain wide organizational support. Even more so, when executive leadership communicates support and a clear vision for AI in their business, the team is much more likely to get on board. 

3: Good Data in, Good Data out 

AI is a broad term, so it’s critical that organizations define exactly what AI means for them. AI, at a high level, is when computer systems simulate tasks that would previously be performed using human thought. Often new “shiny objects” are marketed as AI tools, but may not be as sophisticated as you would expect.  

When you have that AI definition set for your organization, look at the data that will be used for your AI tool. Do you have the data that you need? How clean is that data? If your data is incomplete or inaccurate, that technical debt is only going to get worse if left unaddressed before your AI transformation journey. With proper data validation processes in place, getting your data right is more important than foundational analytics. 

4: Cross-functional Collaboration is Critical 

Successful transformations are cross-functional. Senior leaders and technical team members need to join forces starting with strategy and continuing through execution. This includes ensuring participation from business stakeholders with data and processes that will impact your AI program, since siloed decision-making when it comes to AI investments can lead to a lot of organizational risks from redundancies to poor data.  

Organizations that we’ve seen have success when jumpstarting their AI journeys often create a cross-functional steering committee with representatives from major functions to ensure alignment across the board from beginning to end.  

5: Define In-House Development Vs. Buying AI Tools 

Just like with digital transformation, organizations need to find the right business model for each AI use case. Depending on the need and your organization’s AI readiness, AI may be able to be built in-house or outsourced to a third party, or a combination of the two.  

On one side of the spectrum, in-house tools could be fully built and run by internal data science teams. A hybrid approach might include hiring external experts to support the build of in-house tools that are ultimately managed by internal teams. On the other side of the spectrum, you could buy third-party AI tools or pay for using existing models with minimal development and maintenance efforts internally.  

To make this decision, understand your organization’s readiness to adopt AI for each particular use case. Is there excitement, resistance, or confusion? What complimentary skillsets will be needed across your organization to create and manage your AI tool from a technical perspective as well as train and support users from a business perspective? From there, organizations should engage their steering committee to make sure they’re making the right investment in terms of resources and/or new tools. 

Next Steps 

AI can have significant positive results for your business – if executed the right way. By using these lessons learned from digital transformation and taking a diligent and planned approach to your company’s AI transformation, you will be making AI work for you and your clients. For further guidance, Clarkston’s team of digital strategy experts is here to help your organization meet your AI goals.   

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Contributions from Patrice Freeland 

Tags: Artificial Intelligence, Emerging Technology