“How do we bring AI into our organization?”
This is a question that every business leader should be asking, and it’s not optional. Widespread AI adoption is not a matter of ‘if’ – it’s a matter of ‘when’. If you are not getting started now, you will most likely get started in the near future.
You probably know that your competitors have gotten started with AI adoption, and the longer you wait to embrace AI/ML, the more you will fall behind (AI apathy in business equals extinction). You want to make sure that you develop short-term and long-term competitive advantages in your industry by leveraging AI capabilities.
When mapping your AI journey, there are 3 sequential steps you want to walk through:
1) Prove Value
The first step is proving the value of AI/ML in your organization on a small scale. This does not mean a simple demo, pilot, or isolated experiment, but rather a real use case with predefined KPIs.
We recommend starting small with an initial use case, gaining a strong understanding of your data needs, proving the value and capabilities of AI in your company, and then expanding from that initial success. This allows you to adopt a lean methodology where you establish hypotheses, test/validate them with real users, and iterate accordingly.
The companies adopting AI successfully consistently apply lean methodology (based on the scientific method) to quickly converge on true value and ROI. At the earliest stages of AI adoption, however, the most important element of ROI is learning – learning what works, what doesn’t work, and what it really takes to implement AI/ML into your business processes.
Check out my blog post titled, “Getting Started With Enterprise AI” to learn the top 3 things to consider before going all-out with AI adoption.
Once you have gained validated learning about successful AI adoption on a small-scale, it’s time to adopt AI in other business processes. These initial use cases can each be extended vertically to get the most value for each business process.
This means considering more of the non-technical aspects of AI, such as governance, evolution of roles and responsibilities, measuring the performance of AI systems from multiple dimensions, assessing the changes in current methodologies, best practices, etc. This requires more than just technical expertise – this requires leadership and strategic thinking.
Take your initial proven use case, apply it to other areas in your company, learn technical and non-technical hurdles to overcome, and create a clear vision of what your company will ultimately become as it progresses through its AI transformation.
A lot (if not most) of your learnings are transferrable horizontally across business functions, especially after going deep into individual vertical processes. Once you have proven value across different business functions, it’s time to scale AI systems in a way that fundamentally transforms the DNA of your company. At this stage, you want to maximize vertical and horizontal expansion of AI across your organization.
AI/ML use cases are rarely static. What mechanisms have you put in place for continuous improvement and continuous adoption? You need a clear plan to anticipate and handle ever-changing data, customer needs, organizational structure, etc.
Another important question to ask about individual business processes is, “Given what we have learned about AI adoption, should we re-engineer/reinvent this process?” This may end up having a large impact in your market, and it’s one of the most powerful competitive advantages. When you start affecting your market as a whole and changing the rules of your industry, you are the disruptor. This also protects you against competitors who may also be adopting AI and machine learning.
As mentioned in the beginning, large-scale AI adoption is a matter of ‘when’. Who is going to dominate your market by leveraging the power of AI? It will be either you, your competitors, or startups. Get started now to get value from AI in the short-term, and position yourself to win long-term (check out my blog post titled, “How To Get ROI From AI Projects” for more information on both).
If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at firstname.lastname@example.org and I will be happy to help you.
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