Why You Don’t Need a Data and AI Strategy
Dan Everett
The Techno Optimist - Let’s Create A Better World Using Technology The DataIQ 100 USA 2024
We in the technology world tend to think data and AI are the center of the business universe, much like the pre-Copernican idea that the sun and planets revolved around the Earth. However, the ultimate measure of data and AI success lies in value generation. At the most fundamental level, business value comes from improving management of revenue, cost, and risk.
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If we acknowledge that value generation is the ultimate purpose of data and AI initiatives, then we don’t need a data and AI strategy. We need a business strategy that includes how to manage and use data and AI to improve business outcomes. This might seem like a trivial distinction but it’s not.?
Our technology-centric view creates something known as anchoring bias, where the brain tends to limit evaluation and decision making based on a frame of reference or anchor. In this case data and AI become the central focus, while downplaying or ignoring value generation. The brain then filters information and its interpretation to confirm the frame of reference, skewing evaluation and decision making, resulting in misguided priorities and resource allocation.
This anchoring bias tends to result in data and AI strategies that focus on technical capabilities without a clear understanding of how they contribute to business outcomes.
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We have wonderful documentation on how to collect, store, and manage data, what machine learning algorithms and techniques to use, and what cloud compute resources we require. Yet we can’t clearly articulate meaningful business value like increasing net revenue retention, on-contract spend, or manufacturing yield.
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Shifting our frame of reference from we need a data and AI strategy, to we need a business strategy that includes how to manage and use data and AI to improve business outcomes helps align our priorities and resource allocation to business value generation. It also changes how we develop our strategy. We need to start by talking to and understanding stakeholders’ goals, KPIs, initiatives, challenges, and needs. Next map out the end-to-end value chain/cycle of activities and stakeholders that impact goals and KPIs. And then align data management and AI capability priorities and investments to the activities.
For example, I’m the Chief Financial Officer of SaaS Corp and one of my goals is profitable revenue growth. One of my KPIs for this goal is to maintain Net Revenue Retention rate at 120% or higher. To attain this goal customers must increase spending by 20% annually. One of the initiatives my Finance team is working on with Marketing, Sales, and Support, is upselling customers to premium tiers based on usage volume or additional functionality.
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The value cycle of activities and stakeholders for this initiative include.
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As the Chief Data and AI Officer for SaaS Corp, if you come to me and start talking about your “Data and AI Strategy” and all the cool technology capabilities you are going to build, I’m going to start thinking this is a waste of time and money. You need to align your technology priorities and investments with my business strategy and articulate meaningful value if you want my buy-in. Don’t come to me with generic statements like higher return on investment, and lower cost of ownership. Clearly demonstrate how your technology priorities and investments will help me attain or exceed my goal of 120% Net Revenue Retention rate.
By mapping out the value chain from goal and KPI, to initiative and metrics, to analytics and data you can develop a value proposition that demonstrates you understand how to use data and AI to support my business strategy.
Your conversation with me might go something like this. Dan, as CFO I know profitable revenue growth is one of your goals and maintaining or exceeding 120% Net Revenue Retention is a key performance indicator for this goal.
I can help you improve the upsell conversion rates, and expansion monthly recurring revenue for the Premium version upgrade initiative by making data easier to find, understand, and use for Marketing, Sales, and Support analytics. With high quality and trusted data,
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This is the kind of conversation that will get my attention. It's targeted at my needs, clearly mapped to my KPI, and speaks in my language. It's a compelling value proposition that increase the likelihood I will support your data and AI initiatives.
This is why you don’t need a data and AI strategy. If you want to get buy-in from your business partners, you need to shift your frame of reference to a business strategy that includes how to manage and use data and AI to improve business outcomes.
Experienced Manager. Masters level qualifications in IT and Business. Master of Business Administration.
7 个月We could consider strategy to be a two phased process. In phase1 we identify key success factors in our target area of endeavour. In phase2 we seek to align our enterprise competencies to these key factors. This could be by internal development or acquisition. You might disagree with the assertions above but ultimately, is the identification of a business strategy and its actuation in fact a definable process? To define and manage the process requires intelligence. Intelligence is needed both to identify the key success factors and also to manage alignment. My questions would be, does this intelligence need to be human. Could the AI strategy be to replace the business strategists?
CEO at Consider Solutions
7 个月Tom Olavi Bangemann
Author of 'Enterprise Architecture Fundamentals', Founder & Owner of Caminao
8 个月And yet, it sounds like a minority report
Business Analytics * Decision Intelligence | Data Delivery Solution * Change Management
8 个月Question is what you can win / profit with -a good business strategy or something else? if still undecided then - what needs fixing?