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Use DecisionsFirst To Operationalize AI

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At Decision Management Solutions we do a lot of work with IBM and an old friend of ours – Harley Davis – just published a nice blog post “How do you best operationalize AI?” We’ve had many conversations on this topic with Harley and his team and it’s no surprise we’re completely aligned on this.

Harley outlines three steps:

  1. Reimagine your process
    Specifically by identifying and modeling your decisions
  2. Assemble a decisions team
    One that has all three legs – business operations, IT and data science/ML
  3. Map the data
    Make sure the ML/AI algorithms can use the data available to make the decision where it is in the process

We have a whole approach for these kinds of projects – what we call DecisionsFirst™. We have successfully applied this with multiple clients, helping them operationalize their machine learning (ML) and AI investments and deliver more effective decision-making. We see three critical success factors:

  • DecisionsFirst Design Thinking
    Always begin by identifying your decisions, modeling them directly with the business owner and using design thinking approaches to ideate ML and AI opportunities
  • Mix and Match Technology
    Use ML and AI technology for sure but mix it with business rules technology (using a Business Rules Management System like IBM’s ODM) and use a decision model to coordinate all the pieces.
  • Continuous Improvement
    It’s not about the first version you deploy, it’s all about how regularly and effectively you can improve it. Invest in business enablement and continuous testing, experimentation and improvement.
Book Cover

For more on digital decisoning, check out this recorded webinar on our YourTube Channel and our resources page. You might also enjoy my new book, Digital Decisioning: Using Decision Management to Deliver Business Impact from AI. Harley had a nice quote about it:

Historically, organizations have codified their best practices in rules. Now they are hungry to take advantage of the possibilities of AI to leverage the wealth of information in their historical data and take even better decisions. They are struggling to do so. James Taylor makes it clear how to succeed: By combining business rules and machine learning in a single digital decisioning framework. With clear explanations and examples based on years of practice, this book lays out what can be accomplished with digital decisioning platforms – and how to go about it successfully.

Harley Davis
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