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Is Your Business Ready For AI? Find Out Now!

In 2017, “AI: Building on Your Legacy IT Foundation” was published in The Data Warehouse Journal of Business Intelligence.  It doesn’t seem that long ago, but even the title sounds dated.  My co-author and I predicted that, 

“Technology may improve significantly and may change the way you do business.”

Just kidding.  Nostradamus, or any 20-dollar fortune teller could have predicted that.  

In the article, we discussed what AI is and how organizations can benefit from this next level of analytics.  At the time, there was more hype than reality.  Gartner wasn’t even using the term “AI,” but it was one that IBM was betting on.  Cognitive Analytics is a part of AI, as is “personal analytics, conversational user interfaces, and virtual personal assistants.”   All were predicted by Gartner to have 5–10 years before mainstream adoption. Cognitive expert advisors and machine learning were at the peak of inflated expectations in 2016, with more than 10 years to mainstream adoption. 

Limitations of AI

We predicted that if certain limitations were overcome, the mainstream adoption of these technologies would be sooner than the 10-plus years that Gartner saw. The limitations that we saw that needed to be overcome included the following:

Availability of data. There must be sufficient training data to meet the desired level of accuracy and precision.  

Quality of data. The data on which AI relies can suffer from bias or be flawed or contradictory. Therefore, data curation and data management play a significant role in the adoption of AI.

Variability and uncertainty in big data. Data may be inconsistent or incomplete.

Limitations inherent in big data itself. A large number of potential results may require a domain expert to evaluate the results for significance.

Foundation/infrastructure of IT. This area includes the ability to support unstructured data as well as key platform-enabling technologies such as neuromorphic hardware, blockchain, IoT, and mature security.

Refining people-computer interactions. We cannot emphasize strongly enough the importance of humans providing appropriate curation of data and validating data context. 

All of these limitations have been addressed by the AI leaders.  Our caveats have been met.  We’re right in the sweet spot of 5 – 10 years out from 2017. If your organization is serious about making the most of analytics, there is no question that AI can potentially provide significant business and economic value now.  

If you haven’t already, establish or update your analytics roadmap so that it includes AI. Build on your historical strengths and successes and begin taking advantage of AI benefits today. 

Chatbots and AI image generation are ubiquitous on a personal level.  Perhaps ironically, it looks like AI is being adopted earlier by the public than by businesses.  It may be that there is more risk for a business to put its trust in the financial recommendations or medical decisions of a chatbot.  Ethics and legislation are being discussed that will clarify and pave the way for AI to be used more broadly. 

AI Readiness Assessment

The point of the original article was that AI would not be replacing analytics as we know it.  It would be building on the data foundation within the organization.  This assessment from the original article still has relevance.  Answer these 10 questions to assess whether your organization is ready to leverage the power of AI.

  1. Stakeholders. Does executive sponsorship exist for the pursuit of AI?  Have key stakeholders and business units been identified that will champion its adoption? Are business, IT, and finance behind the pursuit of AI?  Who’s driving it?
  2. Finance.  Who will pay for the innovation?  Does it need to come out of IT’s budget?  
  3. Human resources.  What human interaction will be required with AI?  Remember, AI isn’t intended to replace humans, it can extend our reach by making its users faster and more productive.  What new skills will need to be cultivated internally to best take advantage of AI? 
  4. Processes.  Do processes exist within the organization to evaluate new technologies? 
  5. Goverance.  How is it governed?  Do your corporate policies include governance of AI, its ethical use, and limitations within your organization?  Is this the Wild West all over again with ungoverned little helpers on every user’s desktop with no central governance?  Do policies exist to manage the data ingestion and curation process? 
  6. Vision.  Does the existing technology roadmap include AI, or does it need to be updated? 
  7. Technology.  Do you need to build your own LLM?  Can you leverage existing tools?  Do you know what data is required and where it is located, or whether it exists?  
  8. Data.  Can the data quality be relied upon to make business decisions?  At what level of criticality? 
  9. Value.  Have you evaluated which parts of your business will benefit most from AI (use and value cases)?  Have specific business cases been identified that will benefit most from AI? What new questions can you answer? 
  10. ROI.  With what metrics can you evaluate the return on investment? How will it be paid for?

AI is upon us.  Make sure AI is baked into your roadmap and governance.  Most of all, make sure you’re ready for it.  AI isn’t replacing analytics. It can only provide value if your data foundation can be trusted.

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