AI in Product Management Tools Plus Other Trends

18 Apr 2019 by Scott Middleton

As product people we’re often hyper focused on the trends and technology that will impact our customers lives but in this focus we often forget to take care of ourselves. We forget to understand how trends and technology is altering our own discipline.

When I shared current and future technology trends for product management tools at a recent ProductTalks meetup I received a lot of feedback afterwards from product people that they hadn’t thought about their own work in that way. They didn’t know of the tools and techniques that had become newly available.

So, this post aims to share the inspiration the audience had with everyone else. It’s also a follow up to a post about what tools you need in your product management tool stack.

Hopefully you find something that gives you a technology upgrade advantage.

I’m going to walk through the 7 categories of product management tool and share the trends relevant to each. The categories are:

  1. User Research
  2. Product Analysis
  3. Experience and Feature Design
  4. User Communication
  5. A/B testing and rollout
  6. Project Management
  7. Roadmaps

Those following at home will see I’ve changed the order slightly. Don’t hate me please. There is method to my madness that you’ll learn as you read.

User Research

In the category of User Research the major trends that have advanced product management and will continue to do so are crowdsourcing, targeted digital ad networks and computer vision.


User Research is a fantastic application of crowdsourcing and the use of technology to access crowds, or individuals within a crowd.

A crowd platform for user research lets you quickly access exactly the right people or close to the right people and learn from them. It has made it so easy to “get out of the building” and talk to customers.

With platforms like and UserTesting you can access experts in the industry you are targeting and access customers within hours for a relatively cheap amount (when you consider the cost of getting it wrong).

The use and power of accessing the crowd for research is only going to get faster and more powerful. It is just getting started.

Targeted Digital Ad Networks

10 minutes from now you could have tested whether the problem you are planning to solve is interesting and useful. You could have tested whether how you are going to talk about a new feature is the right wording to use. You could be testing with real objective, observable behaviour from thousands of people you have never spoken to.

So why do you keep arguing in a boardroom or using the HiPPO methodology of solving whether something will work? (HiPPO = highest paid person in the room)

You can do this with platforms like Facebook, LinkedIn and Google. You can target exactly the demographic or person that will be a customer or user and see how they react.

Computer Vision

For those working on physical products the advances in computer vision
mean that you can now use artificial intelligence to analyse video footage of how thousands of people interact with your physical products in various environments (like retail).

Product Analytics Tools

Product analytics is one of the most obvious and, in some ways, easiest areas to apply new technologies like AI. Computer vision for analytics, like you’ve just seen, is one such example. The other exciting trends in product analytics tools are in pattern detection and narrow, automatic action.

Pattern Detection

Pattern detection is the first, most logical step to apply artificial intelligence to product analytics.

Pattern detection means sifting through the mounds of available usage data and highlighting possible segments, possible journeys, possible trends. It is fraught with issues but it’s more about identifying possibilities for a human to sort through rather than making decisions for you so it’s OK if it isn’t right. The more information you have, the faster you have it, the better.

Narrow, Automatic Action

Tools are emerging that focus on providing really advanced pattern detection, recommendations and forecasting in very narrow areas of product analytics. That is they can identify a user that is stuck and then automatically take various actions to unstick the user.

For example, Brightback has emerged to focus finding and saving customers from churning. This is possible provided the intelligence is scoped to a very specific domain, like high volume enterprise SaaS or a certain type of ecommerce.

Artificial intelligence just isn’t ready for more general or complex application in product analytics. It also isn’t ready for smaller volume of customers/data points.

Feature & Experience Design

In feature design there are two interesting trends: nocode and AI-validation.


Everyone is now using great tools for wireframing and high fidelity mockups (Balsamiq, Invision, Sketch and plenty more). But, this is standard practice in most places. What is exciting and can be used today that will only get better is the ability for a product manager, without any design or coding experience, to create a fully functioning prototype almost as quickly as it would take to build wireframes (if not faster in some cases).

This is possible with the huge explosion of nocode tools that let you create apps yourself, with functioning backend logic, databases and more.
Here are just some examples of nocode tools to get you started: BuildFire, Glide, Dropsource and Bubble.

There is also an interesting advancement in User Communication that has overlap here.


Technology is emerging to allow you to take advantage of your existing data on usage and data from other companies to validate how well users will respond to your digital products before you build them.

Throw a wireframe or design into an AI-validation tool and it will apply data to your designs to validate how effective they will be. EyeQuant is a good example of this in action.

While I personally wouldn’t take the results as a definitive direction it gives you more data to make better decisions with.

User Communication

In User Communication the most interesting trend is the advancements in chat and voice assistants. They’re slowly but surely getting smarter and better to use. End-user’s expectations have been set better and product people applying them know how to better use them and where.

On the technology front, most user communication platforms (e.g. intercom, drift, zendesk) are getting better at automating replies. Now, before you think “that’s for customer success people, not product” think about your product as a whole (customer success is part of what you need to account for and design for) and also think about how you can leverage this to improve the product itself.

Intercom gives a shining example of how you can use your user communication tool to implement and test product features before you develop them yourself (if you decide to develop them at all).

Here is Intercom automatically responding with data from your systems:

A/B Testing and Rollout

In the area of A/B testing, technology is advancing in an interesting direction. The category is trying to destroy itself by creating advances that end-AB testing and allow for highly complex tests across multiple pages and page elements without the need for human involvement.

I’m not sure this means A/B testing is dead, it just means higher volume and more variables following the same principles of A/B testing.

You can see tools in this category being able to take a set of web pages, be given a goal and just begin shifting content, buttons and entire pages around in a funnel to workout what combination achieves the best result.
This is an emerging field, the examples are harder to come by, seems to be the best example of the future.

It’s interesting to see that their technology appears to really only be applicable to very high volume web funnels as I imagine that it only works with enough users to test with (like most machine learning at the moment).

Project Management

In Project Management the most interesting area is assistants. There are trends emerging around analytics and prediction but the challenges are slowing this area down.


Assistants for project management are emerging to help automate and simplify the primary activity of project management, communication. There are a variety of chat and voice assistants focused on different aspects of communication on project teams.
For example,

Product Roadmaps

Right now, product roadmaps are the most challenging category of the product management tool stack for new technology to make progress in. There are great tools out there for plenty of different roadmap use cases and team sizes. Technology wise they are addressing relatively simple problems.

But interesting technology trends is harder to come by. There are a few reasons for this:

  1. So many variables that affect roadmap decisions that aren’t captured
  2. Data set size is usually too small for machine learning to be meaningfully useful

Just because 100 people voted for it doesn’t necessarily mean you will build it (maybe you want to end-of-life that feature/product).

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