Don’t Overlawyer

What’s the right balance between being correct — and being able to act?

This article is co-written by Daniella Tsar and Johannes Schleith

Don’t Overlawyer

Our team regularly holds customer workshops to support ideation and problem solving with professionals in legal, tax, compliance and accounting.

During one of these sessions, we were starting to discuss new product ideas, when one participant, a legal professional, before describing a thought, suddenly stopped and shook his head,…

“I’m overlawyering here, let me start again.”

We loved the sentiment behind this statement, because it emphasized so well what so many of us are too often guilty of — over analyzing and getting caught in the weeds! Something all of us have been guilty of at one point or another.

We quote this statement almost weekly.

As we all know, uncertainty is uncomfortable. However, uncertainty can be very useful in challenging us to think exponentially and to come up with new ideas. Going down rabbit holes of perfectionism and edge cases doesn’t help any of us in this journey.

The expectation to get to the 100% without a learning journey stifles innovation and “trying out new things”. Edge cases and small print should be for (released) products. Proof of Concepts need breathing space for experimentation.

Safety Bias

As knowledge workers, we wish to be 100% correct 100% of the time. We translate this desire (… and education, and training, and professional experience) into the way we think about future products and services — and it is hurting our ability to experiment and move forward quickly.

This behavioural pattern is called safety bias. We overweigh potential losses over potential gains.

Embrace the unknown

Not knowing the right solution to a problem is painful. If we are honest, too often, we might not even know what is the right problem to solve in the first place.

In the world of software development, still, we believe in the magic of “just having the right idea. We cling on to the “first problem” and the “first idea” we identify, just to escape the uncomfortable space of not knowing. If our goal is to be in the state of knowing — we might rush to a potentially suboptimal outcome because we’re optimising for the wrong thing.

If we embrace the fact that we don’t know (yet) — we switch to a learning mindset.

Discover other, potentially better, problems to solve. Research your customers’ pain points, investigate their workflows and study and track your users’ behaviour, to detect the best opportunity. Explore a variety of ideas and allow them to fail fast if they don’t test well.

That’s what will lead you to the idea with the most potential.

Aiming for 100% ?

The moment AI drops into an innovation conversation, we often expect AI to “magically” solve all of our users and customers’ problems with a 100% accuracy.

This can tempt us to ignore the process of careful problem definition, creative problem solving and the detail of embedding AI in a valuable product.

AI won’t give us “100%” … and that’s a great opportunity!

Current AI methods are fantastic at solving specific, well-defined challenges. Being aware of the limitations of narrow AI [1] is in fact, a great place to explore collaboration and ways to enrich AI reasoning with human domain expertise.

If we start with the understanding that technology alone will not give us the perfect solution — we can design products and services that maximise human expertise.

A carefully designed human / machine choreography will give you the “100%”!

Such socio-technical systems enable human users to collaborate with the machine on steps such as the “interpretation of information” and the “final decision making”. It is a good idea, to design systems that bring in editorial domain expertise through easy-to-use touchpoints for customers and editorial as well as to continuously evolve the system based on user feedback and usage analytics.

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Experimentation Mindset

If we manage not to “overlawyer” — we can find ways to publish experimental Proof of Concepts and beta versions earlier to try out new features in live case studies.

It’s no news that big tech shifted to a more design-led mindset, e.g. IBM [2] and SAP [3]. The legal startup ecosystem and law firms have done great deal to bring Design Thinking to their core business, e.g. Legal Geek [4] and DLA Piper [5]. Service Design [6] and Design Thinking [7] provide a wealth of methods for culture change and customer-centric product development and business design.

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Fail (aka Learn) Fast!

There is a big difference between running fast, and running in the right direction!

Trust the process! There are proven methods socialised in the Design Thinking and Innovation community to guide experimentation. Schools of thought such as Agile, Lean UX or Design Thinking provide plenty of ideas how to make this happen! [8][9]

Shift your focus from “deliverables”, for example, the number of features, to ”enabling outcomes”, such as “ease-of-use” or “increased sign ups”. Build incrementally on a “firm foundation” [10] and through a detailed understanding of the problem. Collaborate closely and test early with customers through co-design-partnerships.

Creative problem solving techniques [11] can help to think outside of the box. Instead of efficiency gain for existing processes, critically re-think the process, explore new business models and different ways to achieve the desired outcome.

Don’t overlawyer — enable your team to experiment and fail fast.

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UX Research & Design at #trlabs London, w/ brilliant data science & vis team. curious about UX, IA & design thinking. Posts solely reflect my personal opinion.

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Johannes Schleith

Johannes Schleith

UX Research & Design at #trlabs London, w/ brilliant data science & vis team. curious about UX, IA & design thinking. Posts solely reflect my personal opinion.

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