Had a very good discussion on this topic at the AI Innovation Summit last week (full slide deck here). Here’s the synopsis:
Hype or Reality: Take the 10 Questions
It is tempting to look for a cut-off score to be able to declare that AI in your enterprise is ‘real’. And the right answer is that there is no such thing – this list is meant to stir the pot and get the Data and AI leadership thinking. In the best tradition of the Socratic method, asking questions is the best way to stimulate critical thinking.
AI for the sake of AI:
#1) Are the AI projects focused on delivering measurable business outcomes?
#2) Do you have the right instrumentation integrated to monitor the impact from AI projects?
Nurturing AI Talent:
#3) Is there a core AI capability under a CDO/CTO? Or is it a bolt-on as part of the CIO org?
#4) Are there long-term career paths for AI/ML Data Scientists & Engineers?
Data as a Core Asset:
#5) Is there a Data Governance team with an CXO commitment to truly enable Data Democratization?
#6) Is the legacy BI/EDW environment the main data platform for AI projects?
The Legacy of Deterministic thinking:
#7) Does the organization have an appetite for Experimentation across the Enterprise: not just cosmetic website changes?
#8) Does the business accept the idea of Probabilistic Recommendations?
Crossing the AI Chasm:
#9) Does you have an enterprise AI platform infrastructure?
#10) Have AI projects been integrated to Transaction systems (e.g. ERP, RPA) in the last 12 months?
To make AI truly real in your organization, you need to spark some kind of a revolution. And revolutions obviously (!) need manifestos. A series of bold, declarative statements that set the tone for the entire organization – only then, you have a shot at truly using data driven decision making real and drive competitive advantage.
Here’s the Manifesto that I presented:
#1: Be Ruthless About Outcomes: Quantified outcomes should drive AI project prioritization – not the other way around; mandate the instrumentation that can be linked back to an outcome KPI as part of each AI project
#2: Invest in Building Organizational Capability: Invest in a centralized AI/ML Data Science and Engineering capability; balance that with an eco-system of ‘Citizen Data Scientists’ who can provide capability at the edges in an organization. Create a career path that encourages mobility between the edges and the centralized teams
#3: Elevate Data to be a First-Class Citizen: Data is an Asset. Treat it like one: it deserves a governance structure; invest in a ‘Data as a Service’ architecture that goes beyond just data provisioning
#4: Integrate Probabilistic Systems into Operating Processes: Get the organization comfortable with the idea of probabilistic recommendations; ensure AI systems get better over time – where you don’t have enough observations to learn from, use experiments
#5: Invest in ‘AI Platform as a Service’: Invest in an AI@Scale platform that standardizes AI model lifecycle management; move away from monolithic systems to a marketplace of modular ‘code blocks’ that can be used to assemble solutions
At the discussion itself, #4 and #5 sparked the most discussion (availability bias?!) – according to some, the idea of AI Platform as a Service is critical – question being, how to pull this off? Which was a fortuitous because that is exactly what we are trying to do at Incedo.
Each one of the above deserves a detailed discussion – and the plan is to talk about them in the weeks to come.
Would be great to keep the conversation going. What questions would you want to ask of your organization? How different would your manifesto be?
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