The trifecta of AI strategy (and a ✨FREE✨ template)
A simple framework to go beyond "Can't we use ChatGPT for that?"
The rapid development of AI has been a source of anxiety for a lot of people. CEOs and founders are under pressure to incorporate AI into every aspect — from the website’s headline, sales or investor decks, user-facing features, to their company strategy. Executives and managerial levels are pressured for more efficiency by utilizing AI tools, whilst they often haven’t caught up with the latest developments of AI beyond using ChatGPT every now and then. Team members are anxious about losing their jobs to AI — the constant stream of layoffs doesn’t help. CEO memos like Shopify’s and Duolingo’s are flooding people’s timelines, some more controversial than others.
It’s an unsettling time in tech.
Some people would say that the hype will pass, that it’s just another fad like blockchain and web3. I fully disagree though — AI is truly changing the way we work, create, and develop products, and the sooner you embrace that, the better.
As the CPO of Zero Gravity, it’s my job to stay ahead of the game (and my CEO nagging me helps too), so I crystallized my thinking into this AI strategy. It’s nothing fancy — not the kind of strategy you’d get from McKinsey or Bain. This is a simple 3-prong approach to AI strategy that ensures we leverage AI in different aspects of the company. The nice thing about this framework? You don't need to be an AI expert or have a massive budget to get started. Let me walk you through each part.
Prong #1: Craft Optimization
This is the entry-level AI usage that you've probably been doing since the launch of ChatGPT. ChatGPT, Perplexity, and other general AI assistants are definitely super helpful for research, brainstorming, and writing. But don’t let yourself get stuck there.
The advancement of LLMs means that, more likely than not, there’s already a specialized AI tool out there that focuses on helping you do your job better and faster. Just some examples:
Gong for the Sales team: It captures and analyses every interaction with the customers and provides insights to improve sales performance.
Jasper for the Marketing team: An AI content generation tool that assists marketing teams in creating high-quality copy for blogs, ads, and emails.
ChatPRD, Cursor, and Magic Patterns: These tools assist product and engineering teams in drafting product requirement documents, coding, and designing UI components efficiently.
I understand the hesitation about adopting these tools. I know in some cases AI takes away the fun part. But hey, I personally believe that resisting it only harms your own career. The now-cliché adage that “You won’t be replaced by AI but you will be replaced by somebody who knows how to use AI” is true.
As a leader, I argue that our job is to create a safe environment for your team to try out the tools. Make it fun — create a ‘show and tell’, and award prizes for the most innovative use of AI. Give them the budget and freedom to try out new tools. Bottom-up adoption will work much better than top-down — they are more likely to utilize the tool if they choose it themselves. Your team are the subject matter experts anyway, so you should trust them to evaluate the tool as such. If you put too many procurement and security red tapes before allowing them to try out new tools, you’re killing innovation in your company.
Prong #2: Product Innovation
This prong is probably the one the investors are most keen to see. You probably already have one or two AI-powered features in your product now.
The flood of AI-native tools out there might make you think that it’s too late, or the market is too saturated to incorporate yet another AI feature into your product. But the game has just started, and the competitive landscape is still being shaped. If you already have access to a certain group of users and proprietary data about their goals and behaviours, you’re already miles ahead. As long as it still aligns with your users’ needs and problems, an AI-based solution is definitely worth considering.
I used to hesitate — do we really need AI for this? Is it too gimmicky? Now I’m using this heuristic: Could I see my biggest competitor launch this AI feature? Would I be seething if I were reading it on TechCrunch? If the answer is yes, I might as well be the one building this feature.
Prong #3: Internal Operations
This is the one I would argue is relatively unexplored compared to the other two. This prong is about streamlining your company’s day-to-day collaboration and communication. I’m sure you’re already implementing tools like Zapier, Asana, and Notion in your company that do a decent job of holding the company operations together. (If you haven’t, then you have a bigger problem, so maybe start there first.)
The tools above are mainly still operated by humans. Your team did a 1-hour meeting, documented the notes on Notion, and wrote some action items on Asana. A few days later, a team member would take a ticket from the Asana board and execute it.
AI agents can now be utilized to run many operational steps automatically. However, the critical prerequisite for building effective AI agents is high-quality input data. While the inner workings of AI may remain a black box (though it's my mission through this newsletter to demystify it), one thing is non-negotiable: you must feed it high-quality input data.
In a company context, much of this vital input data exists only in your team's heads, shared primarily through meetings and discussions. The first step to capturing this knowledge pool is consistent meeting transcription. Not utilizing AI to transcribe, summarize, and tag every meeting is a missed opportunity. Fortunately, you have many AI transcription tool options – even Notion now has this built into their product, making it seamless if you're already using it for company knowledge management.
With rich meeting notes as input, you can then create AI workflows and agents to automatically handle next steps – whether that's sending follow-up emails, drafting social posts, or even fixing simple code bugs. While AI agents are still in their early stages, we can fully expect their capabilities to advance dramatically even within the next year. Regardless of current limitations, starting to capture high-quality input data now will position you to take full advantage as the technology matures.
Now, how are we going to operationalize this strategy?
A strategy is only as good as its execution. And I believe that buy-in from every layer is the key here — otherwise, you’re perceived as either the bad guy who’s trying to replace workers with AI, or the deluded leader who’s too optimistic about AI. This is how I operationalize AI strategy in my company:
Set up the principles and the definition of success for each prong. I don’t dictate what people should do; I give them the guardrails.
Appoint a champion (or ask people to volunteer as a champion) for each prong. If you have a big team, you might need one champion from each sub-team. The champion’s job is to lead the exploration of new AI tools and get their teammates to use them, collect feedback, and iterate. That way, it’s not “Debbie from the top” who insisted that the developers should use Cursor for coding; it’s “Alex the Principal Engineer” who advocated for this tool.
Set up a meeting cadence with your champion — more frequent during the setup period, and less frequent for check-ins. Evaluate against the principles and definition of success you have created in the beginning.
Above is an example for one prong. There are three elements of each strategy:
Principle: It acts as a heuristic for the champions. Whenever they want to suggest implementing a new tool, they should ask themselves, “Is this removing admin, or adding admin?”
Areas: To give the champions a starting ground and some ideas. Not meant to be exhaustive.
Success definition: To allow us to evaluate whether the implementation has been successful. Note that I added some control metrics there as well, e.g. “without losing relationships or context”. The success metrics aren’t easily measurable, but many important things in life aren’t.
You can download this editable template if you want to adjust it for your company. (No catch, no email address required, I’m just nice like that.)
Hope this brings you a clear, actionable framework to implement AI strategy in your organization. As I said, it’s nothing fancy, but it works!
How are you approaching AI strategy in your company? I’d love to hear — drop me a note in the comments!