Product Management & Machine Learning.
AI Product Managers — Evolution or Revolution?
How AI is changing the Product Management role.
AI Everywhere
Machine Learning has already transformed the software industry, and AI powered applications are part of everyday life.
Now, there is a ‘second wave’ of AI, which extend to reach almost every sector and industry, moving beyond today’s software, healthcare, financial services base. According to both PWC and McKinsey, AI will generate $13-$15 trillion in global GDP by 2030.
As a product manager you may already be using machine learning services, perhaps relying on partners to deliver the functionality your product needs. Maybe you’re using machine learning, but do you understand the implications?
Perhaps the fraud detection service you’re using is actually biased, with the associated risk to reputation? Or maybe you should push back on your in-house development team’s enthusiastic recommendation to ‘try a GAN approach’ so that you can get your product to market on time.
If you have only a superficial understanding, you may be missing out on key innovation triggers or lack sufficient insight in to how AI will transform your competitors and market.
In this article, I argue that as a Product Manager you’ll need one of three levels of expertise: “AI Aware”, “AI Enabler”, “AI Expert”, in order to provide the leadership needed to evolve your product and services as AI becomes ubiquitous.
The ‘AI Product Chasm’
Saleforce’s Director of Product Management, Mayukh Bhaowal, recently spoke about an ‘AI product chasm’. The software industry, although leading the way, is lacking Product Managers with the right skills to fully address the AI opportunity.
Mayukh goes on to list ‘5 new skillsets’ for Product Managers: ‘Problem Mapping’ — in particular knowing whether AI can address your business goal, ‘Data is the new UI’ — product owners used to specifying user experience need to be equally adept at understanding data and setting ML appropriate requirements , ‘Explainability & Ethics’ — topical, and essential, ‘Scaling from Research to Production’ — particularly if you’re relying on your own infrastructure to build and deploy.
Mayukh is not alone. O’Reilly’s ‘Radar’ has highlighted the ‘need for an AI Product Manager’, whilst there is an ever increasing number of AI product role vacancies and courses.
But what exactly is an AI Product Manager?
Today’s Product Roles
Before we can take a look at how product roles might change, let’s first untangle Product Manager, Product Owner and Product Marketing Manager roles.
Here’s a very useful framework from Product Focus —
Product Manager
The ‘Strategic Activities’ above belong to the traditional corporate or enterprise Product Manager. This is a leadership role, creating product direction, vision and strategy. There’s a strong understanding of the product’s market, commercials and the role usually requires strong people & management skills as teams, processes and people are brought together to get things done. This is also an ‘enabler’ role with accountability for product profitability and performance.
In the software world, the line between Product Owner and Product Manager is often more blurred. Even so, the widely adopted ‘Scaled Agile Framework’ (SAFe), acknowledges that the Product Manager is more market and customer focussed, whist the Product Owner is ‘solution, technology and (delivery) team focused.
Roman Pichler’s Product blog refers to an ideal product manager — a ‘ T type’, where the horizontal line in ‘T’ represents “transferable product management experience”, the vertical “Expertise to make the right decision for a given product”.
Given the imminent impact of AI on all industries, sufficient knowledge of AI is going to be required to make that ‘right decision’, and the Product Manager role must evolve accordingly.
Product Owner
Product Owner is a role mandated by Agile Software development, is delivery focussed, typically with a solid technical understanding. The role corresponds to the ‘Inbound Activities’ shown above, and can include customer trial and in-life activities. The Product Owner is much closer to the software team, with daily contact and direct input into each sprint.
When I first viewed Andrew Ng’s, Stamford Professor, Google Brain Founder’s 2016 “AI Nuts & Bolts” lecture — highly recommended, and still very relevant, I found myself considering his AI Product Manager definition, which in reality described a ‘Product Owner’.
I’m jumping ahead, but this greater understanding of software lifecycle and technical background means that the first AI Product Managers you meet are very likely to be ‘augmented’ Product Owners — Product Owners with an AI skill set.
Product Marketing Manager
This role is primarily responsible for getting the product ‘off the shelves’, translating a Product Manager brief into actual sales, the “Outbound Activities” listed by Product Focus. Unless the product itself is heavily differentiated by AI, or is a true AI product, for example a service or API, then it’s unlikely that the Product Marketing manager will need a good understanding of machine learning.
The above descriptions of Product Roles are simplifications. Depending on the size and culture of your organisation, there’s potential for lots of overlap — I’ve certainly been involved in strategic, detailed technical delivery and marketing work as a Product Manager.
You’ll see alternate definitions, but as product manager I like Forbe’s take on Gartner’s definition — “machine learning is a subset of data science while artificial intelligence is the business outcome of machine learning.”
Product Manager’s more commercial and market focus means they are closer to the business outcome and hence ‘AI’. Product Owner’s greater technical knowledge and delivery focus puts them nearer to the underlying machine learning.
But before defining the skills needed, let’s take a quick look at some important trends.
Accessing Machine Learning
Things are changing — increasingly, you don’t have to build machine learning solutions from the ground up using vast amounts of data.
Cognitive APIs — for Standard Use Cases
For a while it’s been possible to purchase end to end solutions from suppliers, or else utilise cognitive APIs (or more simply put, regular ‘APIs’) to embed a specific AI capability into your product.
As an example, in my last post, I showed how to build a self contained AI image analysis dashboard using Amazon Rekognition’s API. No data needed to create the model, all entirely serverless, and infinitely scalable. We’re simply using a RESTful API to get the results we need.
This is fine — but you’ve no opportunity to unlock the value of your business’s data, or to address custom use cases. Until recently, the only way to proceed would be to build your own machine learning solutions, with all of the associated complexity.
What’s changed is ‘AutoML’.
AutoML — for Custom Use Cases
‘AutoML’ services are emerging that allow you to build on existing machine learning models, whilst also automating a complex and time consuming portion of machine learning development — data analysis, model tuning and selection.
AutoML solutions are now available from Google, Microsoft, IBM, H2o, Amazon Web Services and others. This is a big step towards democratising access to AI.
You can extend text comprehension to specific domains, think IBM Watson’s Natural Language Understanding customisation, or create more granular image & video classification, for example via Google’s AutoML, without needing to bring huge quantities of data.
The requirement for less data (“small data”) is key, because having sufficient high quality, labelled data in order to run supervised learning and gain the necessary results, is one of the hardest aspects of building AI solutions and has slowed adoption of machine learning solutions beyond large players.
AutoML makes it much easier for your organisation to use its unique and valuable data to create new business outcomes or to help differentiate your products.
Going back to my AI app example, I could extend my cloud based image analytics to use Amazon Rekognition’s ‘custom labels’. Perhaps I need finer grained classification — a particular type of car vs ‘a car’. Or, I need higher classifier performance for my use case. I could accomplish this with much less data, using tools to help my team or external teams label the data — In Amazon’s case, ‘GroundTruth.’ Google simply call their equivalent service, ‘human labelling’.
So, with ‘AutoML’ there’s transfer learning, data transformation, experimentation, model tuning and selection happening in the background — your development teams don’t need to start working with the underlying CNN, LSTM, RNN, or which ever flavour Neural Network model and topology has been used. Even better, the latest and most powerful models are made available — you don’t need to research, select or manage them in-life.
Ground Up Machine Learning — for Advanced Use Cases
If you’ve got the data, the data teams and the development expertise, and you’re addressing cutting edge use cases, then you may be in a position to develop your own machine learning models. You’ll need robust data processes — AWS propose a modified version of CRISP-DM as best practice, whilst for Microsoft Azure, Teams Data Science Processes, is the preferred point of reference.
There’s lots of risk associated with this approach however — there’s a good chance your work won’t even reach production. When you’re building your own models, you need to keep in mind that Machine Learning is ‘Stochastic’. Given the same or similar inputs, you won’t necessarily get the same outputs. Of the 100s of machine learning papers published every day, few will scale to production.
Where the Machine Learning code will sit — on a device, at the ‘edge’, in the cloud, is an important consideration. The machine learning code itself is only a small portion of the whole needed to deploy and manage a model in production.
With the emergence of Cloud AI & AI PaaS, this should get easier. An AWS example: your data and development teams can build a custom model on TensorFlow, DevOps deploying as container behind a secure globally available endpoint. Your data team’s Extract Transform & Load (ETL) data work can be run on a fully managed Spark environment.
Still, building your own solutions requires significantly more knowledge and incurs greater risk.
The AI Product Manager
Given the trend towards more accessible machine learning, I believe that the Product skills ‘chasm’’ can be bridged — Product Management roles can and will evolve to address the AI revolution.
It’s likely that Product Managers will fall into either “AI Aware” or “AI Enabler” categories below, whilst Product Owners are more likely to become “AI Experts.”
Let’s take a look — refer back to the product life-cycle at the beginning of this article for some Product Lifecycle context.
The amount of AI skill needed by Product Managers is closely related to the approach taken to AI enable the product. If you’re going to be consuming AI Services and APIs, then the amount of machine learning knowledge needed within the product team is less.
However, all Product Managers will need to understand how AI will affect product risk, time to market and their product’s market itself. Proper understanding of data, how it is managed, transformed and used, how to correctly phrase requirements will also be essential.
No matter where you sit, as a member of a product team you will need to be “AI Aware”. The ability to determine whether machine learning can or should be used up front will save considerable time and effort.
Although small data techniques are emerging, data sourcing and management is still critical to developing new use cases, and the ability to understand and work with that data is going to become an important product skill. Ensuring the necessary opt-ins, and data systems are defining characteristics of a Product Manager or Product Owner able to act as an “AI Enabler”.
Product Owners working in software organisations will benefit from much deeper understanding of AI, proportionate to the amount of in-house AI development taking place. They are very likely to be “AI Experts”, with the ability to engage with machine learning teams across the business to effectively lead, whilst checking and challenging delivery decisions taken to bring their product to market.
In conclusion, whilst the software and internet sectors currently frame AI Product Manager roles as specialist Product Owners, as AI reaches beyond software, Product Managers will adapt and evolve to add key AI skills. This will ensure that the full value of AI can be brought into new industries and sectors.
