Digital & Analytics
AI in your enterprise – Hype or Reality?
Step outside the digital natives from the Silicon Valley and Seattle, and AI as a source of h advantage begins to look like smoke and mirrors. In our conversations with multiple Fortune 100 executives, we see increasing levels of frustration. A not un-common refrain: “We are being asked to spend millions on AI initiatives: is this the best way to allocate capital?” We believe that this is the right question to ask – after all, there is no dearth of investments driven by technology hype cycles. Why should AI be any different?
While the pundits talk breathlessly about AI being responsible for the 4th Industrial Revolution, we believe that the reality is far more nuanced – and a good place to start is to ask the right questions to better understand the current state of AI in your enterprise. So here goes – 10 Questions. And like all good questions, these are meant to provoke a dialogue within your organization and through that, a better assessment of whether AI is ‘real’ and more importantly, the journey that you and your organization need to embark to make AI real.
We follow that up with a strawman Manifesto on what it will take to make AI real: you should create one for your own organization.
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 of 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 a 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. Do you have an enterprise AI platform infrastructure?
10. Have AI projects been integrated into 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 genuinely using data-driven decision making real and drive competitive advantage.
Here’s the Manifesto:
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 ecosystem 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.
Two key points are clear:
1. AI is here to stay – it is no longer about the Why or What, but increasingly about the How.
2. AI, like all technology driven transformation, is not a one-size fits all strategy.
Our end-to-end suite of AI services includes AI/ML implementation, business process & digital integration, customer 360 view, and continuous A/B testing, among others.
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