If you think machine learning is a panacea for every business challenges and sell it as such, you’re doing it wrong. The best way to jeopardize your business is to go all in with machine learning by following the 5 tips below.

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So, how can you blow a machine learning sale?

Tip #1: Position machine learning as a cure-all remedy.

Don’t fire the non-machine learing developpers just yet. The truth is that the vast majority of software development is still – and will remain – conventionnal development and engineering.

Tip #2: Make sure to market off-the-shelf AI solutions, batteries included.

There is a growing skeptisim toward machine learning as a service (MLaaS). And knowing that the powerhouse of ML algorithms is data… batteries are rarely included.

Data has to be harvested, built, cleansed and collected in first-party databases or using reciprocal applications. That’s the main obstacle to MLaaS.

Tip #3: Remember that latest machine learning techniques will work, regardless of how tough the problem is.

As Andrew Ng states, “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”

No, this does not applies well to all cognitive tasks.

Tip #4: You need a machine learning program for your company to survive.

You need a data strategy first and foremost. In the absence of time, resources or skills to tame the ML beast, invest in understanding and managing your data wealth.

You can probably start with low-cost data lakes or just with simple governance rules. Minor changes in the way you collect, manipulate and store data will unlock extraordinary potential.

Tip #5: Automate your workflow as is. Machine learning in, people out.

When technology replaces humans, it usually comes with major change in workflow, habits roles and tasks. When the alarm-clock replaced the “knocker-uppers”, getting out of bed in the morning became a whole new experience.

Automating your workflow ‘as is’ isn’t a bad idea - it can act as a bridge between paradigms. However, selling and implementing ML requires a whole new approach to how we work. A good example is how ‘bug fixing’ needs to transition from coder fixing if-then-else rules, to experts fixing the underlying training data.