Technology Transformation Antipatterns and how they derail decisioning success

Blog: Decision Management Solutions - Decision Modeling

Sven Blumberg, Thomas Delaet, and Kartikeya Swami of McKinsey Digital published a great paper recently – Ten ‘antipatterns’ that are derailing technology transformations.

Technology has a crucial role in enabling this faster and more flexible approach. In our experience, however, technology does not get sufficient attention on the executive agenda. This is a serious flaw given the importance of technology in driving successful digital transformations.

It’s a great list and if you are involved in any kind of technology or digital transformation you should read it. Four of the antipatterns struck me as particularly relevant to the kind of digital transformation we do here at Decision Management Solutions.

1. Force-fitting technology solutions: Are you choosing technology out of context?

Many of the projects we do these days involve helping clients operationalize machine learning (ML) or artificial intelligence (AI). We are often struck by how often the team working with this technology wants to use it for problems that are not a good fit. For instance, when decision-making is largely driven by regulations and policies or by agreements with partners and suppliers, using ML to automate this decision is the wrong choice. Similarly, some process-centric companies want to use process automation to tackle decision-making problems – regarding claims handling, origination or approvals as purely process problems instead of a combination of process and decision-making. Always mix and match the technology you use to fit the decision-making problem you have.

4. Initiating big-system-replacement programs: Are you focusing on system replacement rather than improving existing systems in a way that is faster and more cost-effective?

There’s a huge missed opportunity in many organizations to use decision automation to improve their existing systems. Often what’s wrong with the legacy system is that it makes decisions poorly, isn’t up to date with the policies or regulations that guide its decision-making or that it doesn’t make decisions at all, requiring lots of manual intervention. You don’t need to replace the whole system to fix these issues, you can focus instead on decision service extraction and improvement. For instance, see this post on legacy modernization with rules.

If you take this approach you can also inject better decision-making gradually. Putting a new decision service “in front” of the old system and getting the easiest use cases automated let’s you replace the old system for some transactions while minimizing risk and getting some value quickly. Focusing on continuous improvement let’s you slowly increase the amount the new service does, allowing you to gradually reduce the transactions handled by the old system until it is safe to retire it.

5. Focusing on architecture and tooling improvements without enhancing process and delivery discipline: Did you re-architect and implement new tooling but forget to adapt the delivery processes?

This is perhaps the most irritating of all! We keep meeting companies who have purchased a powerful Business Rules Management Systems (BRMS) but then tried to use their standard development approach for rules projects. They are trying to do a single big bang deployment of a new system, on the assumption that future change is hard, using their traditional requirements approach (whether that’s agile stories or waterfall requirements documents). They are treating writing rules as just a different programming paradigm. What they need to success is a truly decision-centric approach like our DecisionsFirst™ approach , that refocuses the requirements on decision models, drives a different level of business SME engagement and takes advantage of the agility offered by a BRMS to deliver continuous business-led improvement!

Of course this is even worse on the ML / AI front where many companies buy a ML platform and don’t think about delivery at all! (See this post on how you can do better with ML deployment).

6. Focusing on outputs rather than business outcomes: Are your technologists focused on output instead of business/technology outcome?

This is particularly a problem when it comes to machine learning and AI. We regularly see data science teams that are focused on the model they can build and on how precise, clever or “cool” it is. But a precise model is not a business outcome – it’s a technology one. One way you can tell that your data scientists are heading down this rat hole is if they say that data is their #1 problem. Check out this article on LinkedIn to understand why that’s a key clue: Data Scientists think data is their #1 problem. Here’s why they’re wrong.

If you are trying to transform or digitize your business then transforming and digitizing your decisions is a key element. To succeed in digital decisioning, avoid these antipatterns by applying our three critical success factors:

Put DecisionsFirst – identify, understand and model your decision-making
Mix and match technology – find the right technology to manage your decision
Focus on continuous improvement – establish a test/learn, improvement mindset and avoid big-bang projects

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