{"id":2131,"date":"2025-04-24T12:04:48","date_gmt":"2025-04-24T12:04:48","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=2131"},"modified":"2025-04-24T12:04:48","modified_gmt":"2025-04-24T12:04:48","slug":"why-most-data-ai-best-practices-are-just-expensive-mistakes-in-disguise","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/why-most-data-ai-best-practices-are-just-expensive-mistakes-in-disguise\/","title":{"rendered":"Why Most Data & AI \u2018Best Practices\u2019 Are Just Expensive Mistakes in Disguise"},"content":{"rendered":"

How many times have you heard this phrase \u201cbest practice?\u201d<\/p>\n

I\u2019ve lost count of how many times I\u2019ve sat in meetings, read RFPs, or listened to keynotes where the phrase \u201cbest practice\u201d gets wheeled out like some kind of silver bullet. This post was inspired by a post that Adrian Smith did last week.<\/p>\n

I do get it, well sort of. On the surface, best practices seem like a foundational promise, a proven path where only those who know tread are in the know, a safe bet on the poker table, or as some people might refer to it as an \u201cindustry standard\u201d. Most just use the phrase because it\u2019s what everyone else is doing!<\/p>\n

There is one thing I\u2019ve often noticed about the phrase, is this, most of what\u2019s touted as a best practice in the world of data and AI is nothing more than a well-dressed mistake. Even worse, it\u2019s often someone else\u2019s mistake, packaged, dressed up and marketed, sold to everyone as THE gospel.<\/p>\n

If you\u2019re serious about making your data and AI strategy real, not theoretical, not a \u201cwe ticked the box\u201d initiative, then now is the time to question everything. Especially the things you’ve been told are “best.”<\/p>\n

Because chances are, they\u2019re not.<\/p>\n

The Comfort of Consensus vs. The Cost of Compliance<\/strong><\/h4>\n

There is always a deep-rooted comfort in doing what others are doing. If everyone\u2019s building data platforms, spinning up COEs, hiring Chief AI Officers, or adopting frameworks from the latest Big 4 whitepaper, then surely it must be the right move?<\/p>\n

Except that\u2019s not strategy. That\u2019s just plain and simple compliance.<\/p>\n

The problem with compliance is that it\u2019s reactive. You\u2019re not making choices based on what your business uniquely needs, you\u2019re copying a blueprint from a company that has completely different problems, dynamics, and ambitions.<\/p>\n

You\u2019re not operating from first principles. You\u2019re operating from FOMO.<\/p>\n

The Seven Best Practices That Often Go Wrong<\/strong><\/h4>\n

Let\u2019s call out a few of the sacred cows. Not because they\u2019re always wrong, but because they\u2019re often misapplied, misunderstood, or implemented without any critical thinking.<\/p>\n

1. Build a Data Lake \/ Lakehouse \/ Warehouse:\u00a0<\/strong>you know the one. \u201cWhere you get to modernise your data infrastructure.\u201d Sure. But instead of asking why, many jump straight into building. Huge capital expenditure. Long timeframes. And no clear commercial outcome. Then two years later, people are still emailing spreadsheets around.<\/p>\n

2. Stand Up a CoE (Centre of Excellence):\u00a0<\/strong>sounds great in theory. In practice? It often becomes a silo of clever people working on things the business doesn\u2019t understand, value, or use. If it\u2019s not embedded in the core of the business, it\u2019s just an ivory tower with a Jira backlog.<\/p>\n

3. Hire a Chief AI Officer:\u00a0<\/strong>because, of course, we now need a new title to solve an old problem. One that is almost always a proxy for: \u201cWe don\u2019t know how to do this, but we hope someone will figure it out for us.\u201d Titles don\u2019t solve operating model gaps.<\/p>\n

4. Follow the MLOps Playbook:\u00a0<\/strong>yes, we need good process and rigour. But companies are layering in \u201cbest practice\u201d tooling, pipelines, and controls before they\u2019ve even figured out how to get the first model to production, let alone make it useful. Don\u2019t automate what you can\u2019t yet demonstrate works.<\/p>\n

5. Separate the \u2018Data People\u2019 from the Business:<\/strong>\u00a0this one\u2019s subtle but deadly. A lot of companies love to do this. Whether it\u2019s by org structure, funding lines, or physical location, when you create separation, you create translation overhead. That overhead more or less kills delivering value. Data needs to be in the bloodstream, not a separate organ.<\/p>\n

6. Buy Another Tool:\u00a0<\/strong>this is one of my favourites! You\u2019ve got a tool problem, right? Nope, Nada, Non, you are wrong. You\u2019ve got a thinking problem, a design problem, a cultural problem. Throwing another SaaS license at the situation is just kicking the can down the road and confusing the hell out of your frontline teams. Don\u2019t do it!<\/p>\n

7. Benchmark Against Competitors:\u00a0<\/strong>actually, reflecting on it, this is actually one of my favourites, because there is always some special person who thinks they are clever bringing this up! This one always gets applause in the boardroom and results in\u2026guess what\u2026nothing. Because you\u2019re looking sideways instead of forward. You don\u2019t know what your competitor\u2019s strategy is. You don\u2019t know what they\u2019re actually achieving behind the press releases, and their constraints aren\u2019t yours. So why are you copying their moves?<\/p>\n

So, What Do You Do Instead?<\/strong><\/h4>\n

Start from zero. Not from some templated strategy or borrowed playbook. Wipe the slate clean and ask: what are we really trying to do?<\/p>\n

Here\u2019s what that looks like:<\/p>\n

1. Start With Use Cases That Matter:\u00a0<\/strong>now you know me, this is the path to a better future. But, not all use cases are created equal. Find the ones that generate revenue, improve customer retention, or reduce cost in a measurable way. Don\u2019t chase AI for AI\u2019s sake. Chase outcomes that matter.<\/p>\n

2. Design the Operating Model Around Decisions, Not Pipelines:\u00a0<\/strong>how does a decision get made in your business today? Who makes it? With what data? How often? If you don\u2019t know that, you\u2019re not ready for AI. Start by mapping the decision loop, understand the processes and then infuse data into it, end to end.<\/p>\n

3. Build a System That Can Learn:\u00a0<\/strong>data and AI need feedback loops. If there\u2019s no way to measure what works and what doesn\u2019t, you\u2019re just building in the dark. Every model, dashboard, or recommendation should feed into a learning cycle. Otherwise, it\u2019s noise. I\u2019ve designed these feedback loops from strategic objective to outcomes and there are embedded loops in all the stages.<\/p>\n

4. Kill the Vanity Metrics:\u00a0<\/strong>no one cares how many pipelines you\u2019ve built. Or how many petabytes you\u2019ve stored. Or how many people signed up to your dashboard. If it doesn\u2019t drive a business result, it\u2019s theatre. Focus on commercial impact.<\/p>\n

5. Empower the Frontline:\u00a0<\/strong>if the people closest to the customer, product, or operations don\u2019t have the tools and access to act on data at the moment it matters, what\u2019s the point?<\/p>\n

6. Invest in Thinking, Not Just Tech:\u00a0<\/strong>too many executives want to buy their way out of complexity. The simple point here is that you can\u2019t. You must think your way through it. That means slowing down, asking better questions, and building the capability to solve hard problems with clarity and not just cash.<\/p>\n

7. Get Comfortable Being Uncomfortable:\u00a0<\/strong>a real data and AI strategy isn\u2019t about best practices. It\u2019s about bold bets, hard trade-offs, and custom solutions that don\u2019t fit into neat little boxes. It won\u2019t always feel safe. But it will move you forward.<\/p>\n

Wrap Up<\/strong><\/p>\n

If you\u2019re following \u201cbest practices\u201d without challenging whether they actually fit your business, you\u2019re not leading, you\u2019re outsourcing your strategy to the herd.<\/p>\n

This hurts, because most of the herd aren\u2019t going nowhere fast.<\/p>\n

You don\u2019t need better practices, you need better thinking, and a model that\u2019s built for your future, not someone else\u2019s past.<\/p>\n","protected":false},"excerpt":{"rendered":"

How many times have you heard this phrase \u201cbest practice?\u201d I\u2019ve lost count of how many times I\u2019ve sat in meetings, read RFPs, or listened to keynotes where the phrase \u201cbest practice\u201d gets wheeled out like some kind of silver bullet. This post was inspired by a post that Adrian Smith did last week. 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