Author: Roger

I did:

Shaping product strategy

Had the week off work and actually managed not to look at emails or teams messages. It gave me some thinking time to consider product strategy, why we need one, what it might look like, how it’ll help us. My temporary conclusions/assumptions to test are:

• A strong vision really is essential for developing a product strategy. It’s the starting point for the hypothesis the strategy expresses and the end point that tells you if that hypothesis was correct.
• Beware of your data – Using data about successful users doesn’t tell you much about those your product didn’t work for, and they’re the ones you need to know about to figure out what you need to do differently. Generic industry data is probably more useful and less misleading than your own data.
• Preserving and amplifying an existing competitive advantage is easier than creating a new one, even if it’s more limiting.
• No single tool or framework or way of thinking about strategy is enough on its own. Putting together different approaches helps create different perspectives and so a more well-rounded strategy.
• There are two ways to use Ansoff’s matrix (probably other tools too); as buckets to put decisions you’ve already made in to see how they relate (we are going to focus on new products in our existing market, which means we aren’t going after new markets), and as a decision-making tool where you choose how risk tolerant you want to be and (we only want low risk expansion which means we’ll be providing existing products to existing markets).
• Direct distribution is at the core of any product strategy we develop because our users have to use our products to study our courses. The question we need to answer (especially as agentic AI use grows) is, should it stay that way or should we create other distribution channels?

I read/watched:

A Deep Dive Into #NoProjects

I’ve been reading Bob Marshall’s stuff for years. He’s a fantastically knowledgeable thinker with solid, well-informed opinions. This post uncovers the challenge with the shift from project-oriented organisational process for product development to more modern, agile, product-oriented ways.

Bob say, “The harder work — and the more important work — is the assumption shift itself. That means making assumptions explicit in governance conversations, building funding gates around learning rather than plan adherence, rewarding teams for honest discovery rather than confident prediction, and developing leaders who can coach the scientific thinking pattern rather than just demand results.”

How big a deal is AI?

Benedict Evans talking about AI, applying the logic of previous emerging tech shifts, and that we can’t know whether anything will be different this time around.

Living lab for using AI

Dan Shipper talks about how Every, a media and software company, is using AI, and what he thinks the near future of AI usage inside companies looks like.

I thought:

AI as intermediary

I’m adding another way for product managers to think about AI to my list. I’ve currently got AI as a tool, AI in products and AI in the market, and I’m adding AI as an intermediary.

Thinking about AI as an intermediary helps us consider what happens when AI gets in between our users and our product because it’s part of another product. For example, Google’s business model has always been to get in between organisations and their customers, and they are going to use AI to turn that up to eleven.

As AI becomes an intermediary in marketing and product interaction, we’ll have lots of change to consider:

• Email marketing – AI in email clients will summarise emails and present to points it thinks are important to the user, which means email marketing teams won’t be able to control the message their customers get.
• Advertising – AI will generate hyper-personalised ads. Ad teams won’t actually be able to know what every customer has seen, what message they’ve understood.
• Search – AI will provide answers to search queries in ways that make it unnecessary for users to visit websites. Marketing teams won’t be able to rely on search results to bring users to a website.
• Using products – AI agents will be performing actions on behalf of users. Product teams will be building products without user interfaces because AI agents don’t need them.

Cat and mouse

If you don’t know what your competitors might do in response to your strategy, you don’t know your competitors well enough. And if you don’t know what you’ll do in response to their response, you don’t know your strategy well enough.