Two short anecdotes about transformations, and what it takes if you want to become ”AI-enabled”

Many product companies I talk to struggle to understand what “transformation to AI” means to them. In this post, I share some insights into what it means to be an AI-enabled business, and what you can do to get there. Not by enumerating things you have to do, but through two anecdotes. The first is about digitalisation — what it means for a non-digital company to transform into a digital company. This is because the transition to AI follows the same kind of path; it is a “same same but different” transformation. The second story is about why so many product companies failed in their investments in AI and Data Science over the last years, because they put AI in a corner.

But before we go there, keep in mind that becoming AI-enabled is a transformation, or a journey. And to embark upon a journey and successfully riding along to its destination, you are better off knowing where you are going. So: what what does it mean to be “AI-enabled”?

To be AI-enabled is to be able to use AI technology to seize an opportunity, or to obtain a competitive advantage, that you could otherwise not.

So, after finishing the transformation, how can you know whether you have succeeded? You ask yourself the question:

What can we do now that we could not do before? Can we take advantage of an opportunity now, that we could not before?

Or more to the point: *Will* we take advantage of an opportunity now, that we could not before?

There is nothing AI-specific about this question. It is valid for any transformation an organisation takes upon itself in order to acquire new capabilities. And, for this very reason, there is a lot to learn from other transformations, if you wish to transition to AI.

Anecdote 1: A tale of digitalisation

Over the last decades, there has been a tremendous shift in some large businesses referred to as digitalisation. This is the process where a company transforms from using IT as a tool in their everyday work, to using IT as a strategic asset to achieve competitive advantage. A few years back, I spent some time in the Oil & Gas sector, participating in large digitalisation efforts. And if you have not worked in O&G, you may be surprised to learn that this huge economy still is not digital, to a large extent. Of course, the sector has used computers since they came about, but as tools: CAD-tools for design, logistics systems for project and production planning, CRM systems for managing employees and customers, and so on. But the competitive power of one company over another has been in their employees’ knowledge about steel and pipes and machinery, about how fluids flows through pipes, about installation of heavy equipment under rough conditions, and many other things of this trade. Computers have been perceived as tools to get the job done, and IT has been considered an expense to be minimised. Digitalisation is the transformation that aims to change that mindset.

To enable IT as leverage in competition, the business must move from thinking about IT as an expense, to thinking of IT as an investment opportunity. By investing in your own IT, you can create tools and products that competitors do not have, and that give you a competitive advantage.

But investing in in-house software development is expensive, so to pin down the right investments to shift competition in your favour, you need all the engineers, the steel and machinery specialists, to start thinking about which problems and challenges you can solve with computers in a manner that serves this cause. This is because, the knowledge about how to improve your products and services, is located in the heads of the employees: the sales people talking to the customers, the marketing people feeling the market trends on their fingertips, the product people designing and manufacturing the assets, and the engineers designing, making and testing the final product artefacts. These humans must internalise the idea of using computer technology to improve the business as a whole, and do it. That is the goal of digitalisation.

But you already knew this, right? So why bother reiterating?

Because a transformation to AI is the exact same story over again; you just have to replace “digital transformation” by “transformation to AI”. Hence, there is much to learn from digitalisation programs. And if you are lucky, you already understand what it means to be a digital company, so you actually know what a transformation to digital entails.

Anecdote 2: The three eras of Data Science

The history of industrial AI and Data Science is short, starting back in 2010–2012. While there is some learning to be had from this history, I’ll say right away: there is still no silver bullet for going AI with a bang. But, as an industry, we are getting better at it. I think of this history as playing out over three distinct eras, demarcated by how many companies approached AI when they launched their first AI initiatives.

In the first era, companies that wanted to use AI and ML invested heavily in large data infrastructures and hired a bunch of data scientists, placed them in a room, and waited for magic to emanate. But nothing happened, and the infrastructure and the people were really expensive, so the method was soon abandoned. The angle of attack was inspired by large successes such as Twitter, Facebook, Netflix, and Google, but the scale of these operations don’t apply to most companies. Lesson learned.

In the second era, having learned from the first era, the AI advisors said that you should start by identifying the killer AI-app in your domain, hire a small team of Data Scientists, make an MVP, and iterate from there. This would give you a high-value project and star example with which you could demonstrate the magnificence of AI to the entire company. Everybody would be flabbergasted, see the light, and the AI transformation would be complete. So companies hired a small team of data scientists, placed them in a corner, and waited for magic to emanate. But nothing happened.

And the reason why magic does not happen in this setting is that the data scientists and AI/ML experts hired to help in the transformation don’t know the business. They know neither your nor your customer’s pain points. They don’t know the hopes, dreams, and ambitions of the business segment. And, moreover, the people who know this, the product people, managers, and engineers in your organisation, they don’t know the data scientists, or AI, or what AI can be used for. And they don’t understand what the Data Scientists are saying. And before these groups learn to talk with each other, there will be no magic. Because, before that, no AI transformation has taken place.

This is why it is important to ask, not what you can do, but what you will do, when you check whether you have transformed or not. The AI team can help in applying AI to seize an opportunity, but it will not happen unless they know what to do.

This is a matter of communication. Of getting the right people to talk to each other. But communication across these kinds of boundaries is challenging, leading us to where we are now:

The third era — While still short of a silver bullet, the current advice goes as follows:

1. Get hold of someone experienced with AI and machine learning. It is a specialist discipline, and you need the competency. Unless you are sitting on exceptional talent, don’t try to turn your other-area experts into Data Scientists over night. Building a team from scratch takes time, and they will have no experience at the onset. Don’t hesitate to go externally to find someone to help you get started.
2. Put the Data Scientists in touch with your domain experts and product development teams, and let them, together, come up with the first AI application in your business. It does not have to be the killer app — if you can find anything that may be of use, it will do.
3. Go ahead and develop the solution and showcase it to the rest of the organisation.

The point of the exercise is not to strike bullseye, but to set forth a working AI example that the rest of the organisation can recognise, understand, and critique. If the domain experts and the product people come forth saying “But you solved the wrong problem! What you should have done is…” you can consider it a victory. By then, you have the key resources talking to each other, collaborating to find new and better solutions to the problems you already have set out to solve.

During my time as a Data Scientist, the “Data Scientist in the corner” pitfall is one of the main reasons groups or organisations fail in their initial AI-initiatives. Not having the AI-resources interacting closely with the product teams should be considered rigging for failure. You need the AI-initiatives to be driven by the product teams — that is how you ensure that the AI solutions contribute to solving the right problems.

Summing up

1. The transformation to being an AI-enabled product organisation builds on top of being digitally enabled, and follows the same kind of path: The key to success is in engaging with the domain experts and the product teams, getting them up and running on the extended problem solving capabilities provided by AI.
2. AI and Machine Learning is a complicated specialist discipline, and you need someone proficient in the craft. Thereafter, the key is to deeply connect that resource with the domain experts and product teams, so that they can start solving problems together.

And: don’t put AI in a corner!

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Nobody Puts AI in a Corner! was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.

​Two short anecdotes about transformations, and what it takes if you want to become ”AI-enabled”Generated by ChatGTPMany product companies I talk to struggle to understand what “transformation to AI” means to them. In this post, I share some insights into what it means to be an AI-enabled business, and what you can do to get there. Not by enumerating things you have to do, but through two anecdotes. The first is about digitalisation — what it means for a non-digital company to transform into a digital company. This is because the transition to AI follows the same kind of path; it is a “same same but different” transformation. The second story is about why so many product companies failed in their investments in AI and Data Science over the last years, because they put AI in a corner.But before we go there, keep in mind that becoming AI-enabled is a transformation, or a journey. And to embark upon a journey and successfully riding along to its destination, you are better off knowing where you are going. So: what what does it mean to be “AI-enabled”?To be AI-enabled is to be able to use AI technology to seize an opportunity, or to obtain a competitive advantage, that you could otherwise not.So, after finishing the transformation, how can you know whether you have succeeded? You ask yourself the question:What can we do now that we could not do before? Can we take advantage of an opportunity now, that we could not before?Or more to the point: *Will* we take advantage of an opportunity now, that we could not before?There is nothing AI-specific about this question. It is valid for any transformation an organisation takes upon itself in order to acquire new capabilities. And, for this very reason, there is a lot to learn from other transformations, if you wish to transition to AI.Anecdote 1: A tale of digitalisationGenerated by ChatGPTOver the last decades, there has been a tremendous shift in some large businesses referred to as digitalisation. This is the process where a company transforms from using IT as a tool in their everyday work, to using IT as a strategic asset to achieve competitive advantage. A few years back, I spent some time in the Oil & Gas sector, participating in large digitalisation efforts. And if you have not worked in O&G, you may be surprised to learn that this huge economy still is not digital, to a large extent. Of course, the sector has used computers since they came about, but as tools: CAD-tools for design, logistics systems for project and production planning, CRM systems for managing employees and customers, and so on. But the competitive power of one company over another has been in their employees’ knowledge about steel and pipes and machinery, about how fluids flows through pipes, about installation of heavy equipment under rough conditions, and many other things of this trade. Computers have been perceived as tools to get the job done, and IT has been considered an expense to be minimised. Digitalisation is the transformation that aims to change that mindset.To enable IT as leverage in competition, the business must move from thinking about IT as an expense, to thinking of IT as an investment opportunity. By investing in your own IT, you can create tools and products that competitors do not have, and that give you a competitive advantage.But investing in in-house software development is expensive, so to pin down the right investments to shift competition in your favour, you need all the engineers, the steel and machinery specialists, to start thinking about which problems and challenges you can solve with computers in a manner that serves this cause. This is because, the knowledge about how to improve your products and services, is located in the heads of the employees: the sales people talking to the customers, the marketing people feeling the market trends on their fingertips, the product people designing and manufacturing the assets, and the engineers designing, making and testing the final product artefacts. These humans must internalise the idea of using computer technology to improve the business as a whole, and do it. That is the goal of digitalisation.But you already knew this, right? So why bother reiterating?Because a transformation to AI is the exact same story over again; you just have to replace “digital transformation” by “transformation to AI”. Hence, there is much to learn from digitalisation programs. And if you are lucky, you already understand what it means to be a digital company, so you actually know what a transformation to digital entails.Anecdote 2: The three eras of Data ScienceGenerated by ChatGPTThe history of industrial AI and Data Science is short, starting back in 2010–2012. While there is some learning to be had from this history, I’ll say right away: there is still no silver bullet for going AI with a bang. But, as an industry, we are getting better at it. I think of this history as playing out over three distinct eras, demarcated by how many companies approached AI when they launched their first AI initiatives.In the first era, companies that wanted to use AI and ML invested heavily in large data infrastructures and hired a bunch of data scientists, placed them in a room, and waited for magic to emanate. But nothing happened, and the infrastructure and the people were really expensive, so the method was soon abandoned. The angle of attack was inspired by large successes such as Twitter, Facebook, Netflix, and Google, but the scale of these operations don’t apply to most companies. Lesson learned.In the second era, having learned from the first era, the AI advisors said that you should start by identifying the killer AI-app in your domain, hire a small team of Data Scientists, make an MVP, and iterate from there. This would give you a high-value project and star example with which you could demonstrate the magnificence of AI to the entire company. Everybody would be flabbergasted, see the light, and the AI transformation would be complete. So companies hired a small team of data scientists, placed them in a corner, and waited for magic to emanate. But nothing happened.And the reason why magic does not happen in this setting is that the data scientists and AI/ML experts hired to help in the transformation don’t know the business. They know neither your nor your customer’s pain points. They don’t know the hopes, dreams, and ambitions of the business segment. And, moreover, the people who know this, the product people, managers, and engineers in your organisation, they don’t know the data scientists, or AI, or what AI can be used for. And they don’t understand what the Data Scientists are saying. And before these groups learn to talk with each other, there will be no magic. Because, before that, no AI transformation has taken place.This is why it is important to ask, not what you can do, but what you will do, when you check whether you have transformed or not. The AI team can help in applying AI to seize an opportunity, but it will not happen unless they know what to do.This is a matter of communication. Of getting the right people to talk to each other. But communication across these kinds of boundaries is challenging, leading us to where we are now:The third era — While still short of a silver bullet, the current advice goes as follows:Get hold of someone experienced with AI and machine learning. It is a specialist discipline, and you need the competency. Unless you are sitting on exceptional talent, don’t try to turn your other-area experts into Data Scientists over night. Building a team from scratch takes time, and they will have no experience at the onset. Don’t hesitate to go externally to find someone to help you get started.Put the Data Scientists in touch with your domain experts and product development teams, and let them, together, come up with the first AI application in your business. It does not have to be the killer app — if you can find anything that may be of use, it will do.Go ahead and develop the solution and showcase it to the rest of the organisation.The point of the exercise is not to strike bullseye, but to set forth a working AI example that the rest of the organisation can recognise, understand, and critique. If the domain experts and the product people come forth saying “But you solved the wrong problem! What you should have done is…” you can consider it a victory. By then, you have the key resources talking to each other, collaborating to find new and better solutions to the problems you already have set out to solve.During my time as a Data Scientist, the “Data Scientist in the corner” pitfall is one of the main reasons groups or organisations fail in their initial AI-initiatives. Not having the AI-resources interacting closely with the product teams should be considered rigging for failure. You need the AI-initiatives to be driven by the product teams — that is how you ensure that the AI solutions contribute to solving the right problems.Summing upThe transformation to being an AI-enabled product organisation builds on top of being digitally enabled, and follows the same kind of path: The key to success is in engaging with the domain experts and the product teams, getting them up and running on the extended problem solving capabilities provided by AI.AI and Machine Learning is a complicated specialist discipline, and you need someone proficient in the craft. Thereafter, the key is to deeply connect that resource with the domain experts and product teams, so that they can start solving problems together.And: don’t put AI in a corner!The process of transformation. Illustration by the author in collaboration with ChatGPT and GIMP.Nobody Puts AI in a Corner! was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.  artificial-intelligence, notes-from-industry, ai-transformation, change-management, product-management Towards Data Science – MediumRead More

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