AI roadmap diagram

AI Roadmap​

Knowing where, when, and how to apply Artificial Intelligence in your business

What is Artificial Intelligence

AI is a rapidly growing field within computer science.  We start with a business leaders’ orientation to the field, terminology and industry examples of applying artificial intelligence.  This breaks down all the jargon and provides business leaders with a pragmatic framework and language for understanding the state of play for AI within your industry. With there being many types of AI, providing a common understanding and language for the types of AI and examples of where they can be applied to advance your strategy is a critical first step.

Process Model of the Enterprise

Together we build a process model of the enterprise.  Because you don’t automate departments, you automate processes, the creation of a process model of the enterprise is a critical first step in assessing where AI can have the biggest payoff across your processes.

AI Heat Mapping and Project Targeting

The Leadership Team who sponsors projects must develop a shared set of priorities for digital transformation and AI.  The heat mapping index and cross-functional  conversations create a breakthrough in alignment on our shared strategic and technology priorities. 

AI Projects and Business Cases

The creation of project charters, and payoff analysis frames the budget, timing and resolves conflicts for resource allocation across all efforts competing for resources

AI Roadmap and Budget

A capital and operating budget and time-phased roadmap is created to execute and monitor the AI agenda for the enterprise.

car crash

Where's the Pay Off?

Understanding the types of AI ready to be applied to your business and developing a plan is critical to your competitiveness.  A State’s Department of Transportation sponsored an AI program so that DOT, Department of Motor Vehicles, Local Police Departments, Insurance Companies and many more parties can efficiently capture and share critical crash information.

There is a lot of personal information in vehicle accident reports.  Using AI to learn to read hand-written reports, redact private information and and share motor vehicle accidents reports leads to the creation of a data lake and allows redacted data to be shared across the industry.  This data sharing supports the analysis for improved road safety and a reduction in the number of severe and fatal motor vehicle, bicycle and pedestrian accidents across the country.

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