A new framework for AI adoption
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A new framework for AI adoption
"The challenge is not to act automatically. It's to find an action that is not automatic." - Nathan, Ex Machina
I’ve picked up quite a few new subscribers this past month, so to get you all up to speed, I decided to reissue one of my favorite editions of the newsletter. It’s definitely one of my most actionable pieces and I still follow the processes laid out here every time I’m on the look out for a new AI tool. I hope you enjoy!
Become more than human
Last week we talked about how AI tools can be used to bolster our humanity and make us more empathetic. This week, we’re changing tracks slightly to take a look at how product makers can best use AI tools to transcend their existing skillsets and level up. In this week’s podcast, Matt Kasner (head of product @ PlayerZero) and I discussed what it will take to avoid being replaced by AI - and reached the conclusion that in order to make yourself irreplaceable, you have to see yourself as an ever-evolving project (click here to listen to the full podcast):
"Are you consistently disrupting? Are you in that mindset of disrupting yourself to stay ahead? And I like to see myself as this project that will always be a project, and with technology it's more important than ever to be constantly redefining and innovating on the way that I go about my job, and the way I think about problems, and AI is a way to kind of help feed into that."
What Matt’s getting at here is that for high-level roles like product management, there’s no line between human and machine - only a set of jobs to be done. Therefore, whatever combination of technology and human effort completes those jobs at the lowest possible cost, and the highest possible quality, will win out in the end. In this paradigm, it’s up to you to invent and constantly reinvent a playbook for delivering value.
In the pod, Matt introduces a fantastic framework for augmenting your skills and becoming a driving force for AI adoption in your organization. It starts with vulnerability... before you can optimize, you need to know what you’re optimizing, and that requires looking at yourself with objectivity to identify your strengths and weaknesses.
Actionably, I recommend starting with a blank piece of paper and drawing a line down the middle of it. Label the piece of paper with one of your most common ‘jobs to be done’. On the left side, list out your strengths with regard to the job, and on the right side list out your weaknesses. You’ll end up with something that looks like this:
Now that I’ve taken stock of my strengths and weaknesses when it comes to creating SEO content, I can begin creating a framework for optimizing my time spent on this particular task. I know that topic & keyword identification and making topics actionable are strengths of mine, so for now, I don’t need to waste any time trying to optimize them. Instead, I’ll start by looking into tools that can help augment my weaknesses. Let’s start with the first weakness I identified: time spent writing simple content.
I know off the top of my head that there are a ton of tools for generating simple written content, so I can combine this weakness with another one to find a tool that will fit my specific needs. In this case, I’m Googling: “copywriting AI tools that can create SEO-optimized content”. Following this process helped me find the AI SEO-writing tool Byword, which not only writes high-quality long-form content, but also optimizes keyword density based upon the keywords/titles provided to it. With two weaknesses down, I simply repeat the process for my remaining weaknesses... and boom, I have a list of tools to try out that will actually make me more efficient, as well as pre-established expectations of my own performance with which to judge their effectiveness.
All you have to do to ensure you’re bringing in tools that actually move the needle is follow this process to a t. Simply repeat this for each of the most important jobs in your role until you’ve accounted for 100% of your time spent working each week. This framework has been great for me as it eliminates the “ooh look, shiny cool thing” effect that AI tends to have and cuts straight to the elements of my workflow that actually need augmentation.
Here’s another example: say I’m a product lead for a small startup with 7 employees. In such a business, bandwidth is incredibly limited and it’s crucial to ensure that work is delegated according to each individual’s unique skills. All too often though, work is actually delegated according to convenience.
For example, let’s say I need to take some production data and find the insights in it that are relevant to how I’ll plan out our next feature. I’m not a data scientist by trade, and I don’t understand code. But... I have 4 engineers on my team, all of whom are capable of visualizing data. So, I grab the one with the least amount of work currently on their plate and ask them to make some visualizations for me. Now, they may be competent with visualizing data, but by handing off this task to someone who doesn’t have my particular skillset, I guarantee that the learnings I’m actually able to take from the analysis will be limited to those my engineer can produce. What I actually need is an AI tool that can think about the data from a product owner’s perspective, and produce insights in a language that I can understand - in other words, I need to create an extension of myself that has the skills I lack.
If I would have done a strengths and weaknesses analysis prior to handing this work off to my engineer, I could have found the following Chat GPT use case from Jason Calacanis:
The way I see it, AI is coming for the minutia first - the pieces of our work that we wish we didn’t have to do and that contribute to a feeling of drudgery and inefficiency in the workplace. Jerry Cuomo (IBM Fellow and CTO of automation @ IBM) put it this way:
“Automation powered by artificial intelligence is changing our working lives by increasingly freeing people to apply their talent and effort to high-value work. Enterprises still spend billions of hours each year on mundane repetitive work, and AI can remove this burden so their people can focus on things that really matter. We call it making time for brilliance. We can use AI to transform people into superhumans.”
That’s not to say that AI won’t replace entire roles, but that those roles will specifically be around the “back office” tasks that white collar workers spend so much time on when they could be spending time finding unique solutions and innovating. So, how can you make yourself superhuman with AI? Take it one step at a time... keep open tabs for each and every “weakness” you’ve identified that doesn’t yet have a solution. See yourself as an ongoing project, and never stop optimizing.
Some of my favorite pieces on the subject of how AI can make us superhuman:
How AI-powered automation is giving people superhuman abilities by Jerry Cuomo, for IBM
Getting back to the core of a Product Manager in the face of AI by Christine Itwaru, for Mind the Product
Superhuman artificial intelligence can improve human decision-making by increasing novelty by Minkyu Shin, Jin Kim, Bas van Opheusden, and Thomas L. Griffiths, for the National Academy of Sciencesc
3 unique AI tools to check out
🔎 Research Studio
The product - Research Studio turns your data into actionable insights with its AI-powered research analysis. Designed for UX, Marketing, and Product professionals, simply upload your research files and allow the AI model to do the rest. Summarize documents, ask natural language questions about metrics and pain points, identify competitors, analyze user feedback, and export comprehensive reports—all within the app.
The use case - if you’re on a small team striving to understand your market, users, and competitors without spending hours on manual data extraction, Research Studio could be a game-changer. Its ability to synthesize and present complex data can drive informed decisions, providing a real competitive edge.
📹 Munch
The product - it’s becoming more and more common for the AI gods to anticipate my wants and deliver me a new AI tool that suits a very specific need of mine. In this case, Munch fills the need for a fast and efficient way to repurpose long-form content (namely the podcast) into interesting short form clips, without having to rewatch the entire podcast and cut clips myself.
The use case - Munch is for anyone looking to leverage video content to reach wider audiences. By distilling long-form videos into impactful, shareable clips, it can enhance your brand's social media presence, save time, and drive engagement. With Munch, you can efficiently harness video content, ensuring alignment with current trends, and optimizing performance across various platforms.
💼 aiApply
The product - aiApply automates job applications and cover letters with state-of-the-art AI. By simply uploading your current Resumé and navigating to the job application form, aiApply crafts the perfect cover letter, analyzing your Resumé and job specifications to create a tailored application.
The use case - I’ve been expecting a tool like this to drop any day now. Something like this could quickly become an essential tool for out-of-work product professionals looking to streamline their job search. The AI-driven analysis takes the guesswork out of crafting cover letters, ensuring that applications are targeted and compelling. For product leaders in transition, aiApply could offer a significant edge in a competitive job market, saving time and enhancing the quality of each application.
Chronicles of the circuit circus
XQ-58A 'Valkyrie' drone successfully flown by AI pilot - by Christopher McFadden for Interesting Engineering. The big pull quote:
“An experimental XQ-58A "Valkyrie" drone has officially been flown under artificial intelligence control, the Air Force Research Laboratory (ARFL) announced. Conducted on July 25 at the Eglin Test and Training Complex in Florida, the test flight saw the drone entirely AI-controlled for around three hours.
This test follows around two years of research and development between a partnership with Skyborg Vanguard, a team made up of personnel from the Air Force Research Laboratory, and the Air Force Life Cycle Management Center with the intent of creating unmanned fighter aircraft.”
Apps Are Rushing to Add AI. Is Any of It Useful?- by Justin Pot for Wired. The big pull quote:
“The American philosopher Homer Simpson once called alcohol “the cause of, and solution to, all life's problems.” AI, in this context, serves a similar function: It creates a problem (the emails are too long) and then solves them (summarizing the emails). It's an ouroboros, a snake eating its own tail, a technology that exists in part to solve the problems it is creating.
It's better, in my opinion, to look at the cultural assumptions instead of reaching for unnecessarily complicated technological ones. What cultural forces are making me think I can't just write a one-sentence email? Can I ignore that, if it makes communication better?”
The Ghost of Privacy Past Haunts the Senate’s AI Future - by Matt Laslo for Wired. The big pull quote:
“Rubio is far from an outlier. Ted Cruz of Texas, the top Republican on the Senate Commerce Committee, agrees. “I think if the Democrats push through restrictions on innovation and AI, it would be disastrous for America,” Cruz says. If the United States doesn’t lead, goes the GOP’s stock argument, an adversarial nation (read: China) will.
Still, there’s fear about AI’s potential to dramatically alter the world, which has kept senators mostly united over the need to do something. But with lawmakers beginning to write AI-related legislation, old and unresolved privacy debates are proving a major impediment—and there’s little room for error on the tightrope of bipartisanship in today’s Washington.”
Signoff
That's all from me this week. If you have any insights, feedback, or AI tools you'd like me to explore, don't hesitate to reach out. I look forward to bringing you more insights in the next edition.
See you next week!