Continuous Discovery Habits, Why AI Is Harder Than We Think, The New Lanchester Strategy.

Continuous Discovery Habits · Teresa Torress · 2021

Teressa Torress is a household name in the Product community, and this might be the most influential product book written for a decade. It became an instant classic with Product Managers for good reason.

I suspect it resonated with PMs because so many teams are struggling to do product discovery well. Teressa made it seem easy.

Once you’ve mapped your opportunity space as a tree, prioritisation and research become easier. You’ll be able to systematically identify the most important thing to do next (research, experiment or build).

Meeting customers regularly increases your both the quality and quantity of insight, and helps you navigate your opportunity space.

There’s a ton of value in this book, so I recommend reading it in full.

Key Highlights

• Agile helped teams build more software, but it doesn't help us build the right thing. The fruit of discovery work is the time saved by not building something that won't work.
• Historically, teams viewed discovery as a one-time activity at the beginning of projects and focused too much on delivery. Teresa advocates for continuous discovery, having weekly touch-points with customers to conduct small research activities, with a desired outcome in mind.
• Successful products provide value to both the business and the customer. How we frame a problem influences how we solve it, therefore teams should frame problems in a customer-centric way.
• Mapping the opportunity space and selecting which opportunities to pursue are high leverage points in a product team’s system. They are crucial to a team’s success.
• The Opportunity Solution Tree helps teams select the most impactful thing to do next, even if that activity is more discovery/exploration. "What should we build next?" becomes "What should we do next?"

• Prioritizing opportunities, not solutions, will save you time. Product strategy doesn't occur in the solution space; it occurs in the opportunity space.
• Teams should be assigned outcomes and given the freedom to explore and select opportunities. It's unfair to assign an outcome to a team they can't influence. It's important to help teams find tractable metrics that will ultimately impact the business outcome.
• Visualize what you know in a customer's experience map - and use it to guide your interviews. Interviews help discover and explore opportunities. Meet with customers regularly so you don't have to cover so much ground. Instead, you can gain insight into the opportunity area that you're working on now - just in time to influence what potential solutions could look like.
• You and your team can take a step towards this way of working today. Pick your moment to advocate for more discovery. To maintain autonomy, teams must show their work to stakeholders - opportunities explored, customers spoken to, metrics identified, etc.

Full Book Summary · Amazon

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How to pass your UX research through time · Video

The New Lanchester Strategy for entering a market · Article

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Large Lanaguage Models. A Survey · Paper

How to become an AI PM · Paid Article

Why Ai Is Harder than We Think · Melanie Mitchell · 2021

Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself.

The Paper outlines 4 reasons why AI is harder than we think - areas where researchers are overconfident in their predictions .

• It's possible that narrow intelligence does not fall on the same continuum as general intelligence.
• The concept that "easy things are hard, and hard things are hard" is a spin on the AI saying "easy things are hard and hard things are easy," also known as Moravec’s paradox. We can make computers perform at adult levels in checkers, but giving them the perception and mobility skills of a one-year-old is challenging or impossible.
• Be aware of the "Lure of Wishful Mnemonics". Terms like "Machine Learning / Deep Learning" suggest more learning than actually occurs. People often say the model 'understands' that the image should have that label, but the word 'understands' is quite generous.
• Consider the possibility that intelligence isn't solely located in the brain. We're unsure if "pure rationality" can be separated from other closely integrated and interconnected human attributes, such as emotions, desires, a strong sense of selfhood and autonomy, and a basic understanding of the world. The ability to separate these attributes is uncertain.

View the Paper

Book Highlights

No one has an advantage at everything. Teams, organizations, and even nations have advantages in certain kinds of rivalry under particular conditions. The secret to using advantage is understanding this particularity. You must press where you have advantages and side-step situations in which you do not. You must exploit your rivals’ weaknesses and avoid leading with your own. Richard Rumelt · Good Strategy / Bad Strategy

Why, exactly, do people sometimes ignore the evidence of their own senses? We have already sketched out the two main answers. The first involves the information that seems to be conveyed by other people’s answers; the second involves peer pressure and the desire not to face the disapproval of the group. Cass R Sunstein and Richard H Thaler · Nudge

Moments of truth can be thought of as a special type of touchpoint. They are critical, emotionally charged interactions, and usually occur when someone has invested a high degree of energy in a desired outcome. Moments of truth either make or break the relationship. James Kalbach · Mapping Experiences

A modular approach has many known advantages: • It’s agile, because multiple teams can design and build modules in parallel. • It’s cost-effective, because those modules can be reused. • It’s relatively easy to maintain, since you can fix a problem in one module without affecting the others. • It’s adaptable, because modules can be assembled in the ways that meet different user needs. • It can have a generative quality, which means that you can create entirely new outcomes by introducing new patterns or combining them in new ways. Alla Kholmatova · Design Systems

Best of X

Semi-controversial opinion: There’s no such thing as working too hard. There’s just being under rested.

Even if the opinion is literally false --it's an example of a not true but useful belief.

Rather than focusing on decreasing work rate, the focus always goes towards increasing energy production. George Mack

The mobile S-curve ends, and the AI S-curve begins There’s never been a bigger contrast between mobile and AI — it’s the end of one technology curve, and the start of the other. It’s been 15 years since the App Store was launched; while the generative AI revolution started… Full Post · Andrew Chen

Tiago Forte

Tiago has devoted his life to personal knowledge management and organizational methods, becoming a modern-day David Allen. I adopted his Second Brain approach years ago to organize and retain digital information. This method allows you to pass information through time to your future self, a modern-day superpower. It also simplifies the creative process, making it easier for you to operate at a higher level.

Link

What I’m Listening to.

Left: Never

Right: Bomb Intro