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Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted.

Traditionally, product development followed a linear path.

A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent.

However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior.

Situational Management is a framework for deciding how to manage a report depending on their skill level & motivation. 

Where does AI fit today?

The answer is easy. It’s in the micromanage category where the motivation is high but the skill is low.

The computer is relentless in wanting to users’ questions or complete their code. But the skill level is relatively low - about the same as a high-school student.

The average American attention span has fallen from 150 seconds in 2004 to 75 seconds in 2012 to 47 seconds in 2023 - a 5-6% annual rate of decline.

How does this compare to these blog posts?

In 2013, the average reader dwelled on this site for 47 seconds.

Today, it’s 33 seconds, a 3.6% decline - which is a bit better ! but probably within the realm of statistical noise ¡

Rubrik, a Palo Alto-based data security company, filed their S-1 yesterday. At $784m in ARR, growing 47% with 130% net revenue retention across 6100 customers, the company should be one of top 10 fastest growing software companies alongside Klaviyo, ZScaler, & Crowdstrike - in ARR terms.

Half of new customers are over $100,000 in size & contract values have grown 19% from $101k to $120k in a year. 41% of new bookings derives from those new customers.

AI will transform software sales. Most of the discourse so far has focused on how AI upends the sellers’ worldview. But the buyers’ process will also evolve. When researching software, operational buyers & procurement teams alike will use AI to research different offerings.

Typing “Compare Salesforce & Hubspot for a 10 person sales team. which is better?” into Gemini produces this result & most importantly, a recommendation :

Selling software will evolve to selling agents, AI that acts on behalf of users.

The efficiencies for rote work are too massive to ignore for many uses. As the technology rapidly evolves, so too will the sales strategies.

Sellers & the startups they represent will need to re-imagine roles. In a sense, selling AI agents is analogous to category creation.

About a decade ago, Nick Mehta & the Gainsight team created the customer success category. In 2014, I remember joining a panel at the Gainsight annual conference Pulse, then in its second year.

Should software assist humans or act on their behalf?

In 2016, the question was easy to answer : sell Ironman not Robocop. Technology hadn’t reached the level of sophistication we have attained today where AI is 90% as capable as a high-school student, the MMLU benchmark for AI is precisely this.

The next generation of software startups have a strategic question with different terminology & potentially a different conclusion.

To be or not to be an agent, acting on behalf of workers?

Will AI sofware companies operate with better or worse profitability than a classic SaaS company?

Initially, I thought worse since the expense of serving AI as a product is signficantly higher.

But now I’m not so sure. AI SaaS may be much more profitable than the -10% average net income margins of the current crop of public businesses.

Yes, AI inflates the cost to serve the product. Google queries may be 10x more expensive than standard search results. That’s an unfair comparison since Google has focused on classic query cost optimization for more than 20 years.

Within data teams, a tension exists. Centralize the data analysis to ensure accuracy or enable end-users to analyze their own data directly which is faster & more direct.

The pendulum between these two states started with centralization during the 2000s with BI products from Microstrategy, Cognos, BusinessObjects, & Hyperion. In 2004, Tableau emerged from the Stanford campus to deliver their application to the users.

If you were to watch three videos on YouTube Shorts - one on Italian cooking, one on chess openings, & a third on crypto trading, YouTube Shorts’ recommendation algorithm combines the video descriptions with your dwell time.

Watching the osso bucco video to its end would trigger more Italian cooking specialty videos in your feed.

We believe every LLM-based application will need this capability.

Combining text & structured data in an LLM workflow the right way is difficult. It requires a new software infrastructure layer: a vector computer.