Accelerated AI Adoption

A Three-Level Maturity Model and How to Make the Shift From Single-Player to Multiplayer Mode

A GSD at Work LLC AAA Playbook Primer

Generative AI is under-hyped as a core productivity driver; however, achieving those outsized org-wide gains c-levels have been promised for ~2 years now can be challenging.

That said, when things go right, the technology can help you achieve your existing goals dramatically faster—sometimes by an order of magnitude—and unlock new product/market frontiers that help you stay ahead of your competition.

Yet, as we head into 2025, over two years since ChatGPT was originally launched, many organizations struggle to move beyond a handful of early adopters tinkering in private. How can you scale these capabilities so that entire teams, departments, and eventually your whole enterprise benefit?

To answer this, I’ve developed a "scale invariant*" three-level Accelerated AI Adoption Maturity Model. This framework shows you how to evolve from individual experimentation (single-player mode) to organization-wide transformation (multiplayer mode). By progressing through these levels, you’ll cultivate a more adaptive, open, and continuously improving culture—one that thrives on user-led innovation and seizes new value streams at every turn.

*it works for organizations of any size: from bootstrapped indie founders to massive enterprises

Level 1: Individual Productivity & Data Asset Creation

Goal: Maximize personal efficiency with AI, while generating rich data assets that set the stage for more complex workflows later.

At this initial stage, the focus is on you—your own productivity, learning curve, and personal data workflow. Core capabilities include:

1. Speech-to-Text Dictation:

Stop typing, start talking. Use tools like superwhisper (or, if you must, OS built-in dictation) to get your thoughts into the machine rapidly, providing high-context input to AI systems. This accelerates your brainstorming and helps you think in real-time with your AI “thought partner”; unlike Google Search, the key to getting the most out of these systems is, "more is more," meaning get as much salient context out of your head as quickly as you can and then into the AI system (vs. the three-keyword web search queries we've become accustomed to carefully crafting over the past two decades).

2. General-Purpose AI Chat Interfaces:

Tools like ChatGPT (especially the new Pro / Teams offerings) or Anthropic’s Claude equivalents serve as your personal AI assistant. They can help you rewrite text, draft memos, generate new ideas, or parse complex documents; this technology is truly one of those rare (maybe once in a century?) general purpose technologies that unlock qualitative changes in both what's feasible and how work gets done. Start with a basic subscription and level up as you become a heavier user.

3. Automated Meeting Note-Takers:

Capture all your discussions with a tool like Fireflies.ai . Recording conversations and making them searchable transforms transient meetings into long-lasting data assets; as I mentioned before, more is more, so the more salient data you have (and these raw primary sources are absolute gold, especially as the models get smarter, allowing them to perceive meaningful insight contained therein, not unlike a digital microscope), the more you'll get out of the general purpose tools à la Chat. Cultivate a habit of feeding this trove of transcripts into AI models for analysis, insights, and acceleration.

A great way to get started is to pipe a transcript into the most powerful you have access to after a meeting, and ask it to 1) act as your trusted advisor and give feedback, 2) draft a follow-up email, and 3) suggest an action item that no-one explicitly mentioned that you can perform in 60 seconds or less.

Outcome:

Personal productivity skyrockets, but more importantly, you accumulate a “data asset”—a record of your processes, problem-solving logic, and decision-making. This data pool becomes the raw material for more complex forms of AI adoption down the line.

Level 2: Asynchronous, Remote & Fractional Collaboration

Goal: Move from single-player efficiency to multiplayer mode, where small teams, fractional talent, and global collaborators work together asynchronously, mediated by AI.

Now that you’ve mastered personal productivity, it’s time to extend these benefits to a broader network. Think of Level 2 as building a distributed team of humans and AIs working together seamlessly; it can happen internally b/w FTEs or externally, via semi-porous, AI-mediated interactions with hyper-specialized contractors (like me) and, e.g., global talent you can spin up for ad hoc tasks.

Key Steps & Tools:

1. Asynchronous Communication:

Use Loom to record quick video updates and Slack (or Teams) for distributed collaboration. Asynchronous communication allows people to work across time zones and reduces the drag of status meetings.

2. Fractional Labor & Expert Networks:

Tap platforms like Upwork to find on-demand experts who can handle specialized tasks that AI can’t fully automate yet. Rather than hiring another full-time team member, you assemble a flexible constellation of talent as needed.

3. Building Your Digital Presence:

I highly recommend taking right-sized steps toward cultivating a rich professional footprint on LinkedIn: share insights, build credibility, and attract collaborators. For more informal, niche interests, platforms like X (formerly Twitter) can help you connect with other intrinsically motivated internet explorers; I personally get a lot of value from using X like a notebook, rather than a marketing channel per se. While this doesn't replace traditional networking or the deep, trust-based relationships that are the foundation of a scalable and business, they do complement those activities by allowing the people in your network to engage with you and your ideas in a low-cost, async, remote, and incremental way.

Outcome:

You transcend individual productivity. AI plus human collaborators form a fluid, adaptive team. Communication flows across continents and time zones (and cultures!). Your public presence and credibility grow, adding intangible assets—reputation, a global network—that feed back into your efficiency and opportunities.

Level 3: Bespoke Software Development & Capital Creation

Goal: Convert your accumulated data and expertise into digital capital assets—custom software tools that encapsulate your know-how and solve high-value problems.

At Level 3, you’re not just using AI-powered tools; you’re creating them (and maybe selling access!).

Key Steps & Tools:

1. AI-Augmented Software Development:

With tools like o1 Pro (just released last week; believe me, the $200/mo price tag is still a steal) and/or Replit , you can prototype, build, and host custom applications quickly. AI co-pilots assist with coding, testing, and refining features, dramatically reducing development cycles.

2. Custom Integrations & APIs:

Connect different APIs and services to build tailored workflows that solve specific, high-value challenges. You’re now orchestrating technology at a higher level of abstraction, stitching components together on-the-fly.

3. Monetization & Capital Asset Creation:

Your proprietary data, codified into software, becomes a revenue-generating asset. You can target a niche of premium clients or go broad-market, but either way, you’re turning your unique expertise into digital capital that scales — Stripe is easier than ever to set up, and you'd do yourself (and your existing customers) a huge service by encoding your unique expertise into always-on SaaS.

Outcome:

You’ve fully embraced the 21st-century digital economy. AI isn’t just a tool for crafting emails and summarizing documents—it’s a multiplier for your personal and organizational value. You own software assets that produce ongoing returns, raising your leverage and financial upside.

From Single-Player to Multiplayer: Cultivating Organizational Transformation

While the maturity model traces a clear vertical path—personal productivity, a new kind of "liquid team"–based coordination, and asset creation—another dimension is the shift from single-player to multiplayer mode across the organization. The real transformative leap happens when entire departments (or new emergent structures that smash through silos) and whole enterprises adopt AI-assisted workflows.

This shift often defies expectation. At Level 1, you’re flying solo. By Level 2, you’ve integrated AI with human collaborators, freelancers, and global talent. By Level 3, you’re building proprietary tools. Across these stages, you evolve from isolated innovation to collective progress—and that collective process (by which true collective learning can be achieved) is the essence of multiplayer mode: a culture where everyone is empowered by and with AI, continuously learning from each other’s breakthroughs.

Why This Matters for Organizations of All Sizes

The beauty of this model is that it’s scale-invariant. Whether you’re a solo entrepreneur or leading a Fortune 500 giant, the underlying principles remain constant; that said, AI integration into an existing enterprise with legacy IT, formal processes, career progression tracks, compensation bands, norms, artifacts, etc. comes with a slew of challenges (making change management up to 3x more expensive vs. previous IT innovation waves). Here's what you should do if you want to integrate the technology and achieve actually transformative results:

1. Adopt the Tools, Don’t Ban Them:

Give everyone meaningful access to AI tools (some variation of those listed above). Prohibiting these tools only pushes usage underground and risks losing control over data flows. By providing secure, approved, enterprise-grade access, you encourage responsible experimentation.

2. From Shadow IT to Show-and-Tell:

Many “power users” already exist in your organization. They’re the 1-5% who have quietly figured out how to leverage AI to multiply their productivity. Bring them into the spotlight. Host weekly show-and-tell sessions where they share breakthroughs. Record these sessions, build a knowledge repository, and turn private expertise into a shared asset.

3. Embrace Remote-First, Asynchronous Collaboration:

AI thrives in distributed networks; the companies that are doubling down on RTO initiatives will miss out on the upside, since the constraints that make AI use make more sense (and thus stimulate learning—necessity is the mother of invention after all) won't be in place to generate new know-how. Time zones matter less. Expertise is a Slack message or Loom video away. Outsource tasks on-demand to fractional talent (e.g., via Upwork; Replit bounties are another good option). This mode of operation accelerates learning and creates a culture of continuous improvement.

4. Rethink Roles, Responsibilities, and Incentives:

High-performing AI adopters won’t just be more productive; they’ll integrate strategy, execution, and quality control into a fluid workflow. Traditional role definitions may become obsolete. Embrace this tension and use it as a catalyst to redesign teams, create new career paths, and redefine incentives that reward innovation and knowledge sharing.

5. Marry Bottom-Up Innovation with Top-Down Ambition:

Over time, grassroots user-led innovation and leadership-driven strategic goals should converge. Top-down targets (e.g., double qualified leads, halve project timelines) become achievable as AI-fluent teams experiment, iterate, and learn rapidly. The result is not just incremental improvements—it’s a fundamental shift in how work gets done.

If you really want to go deep on this, check out my "One Objective to Rule Them All" approach to driving transformational change with cross-functional teams; it's a perfect complement to AI adoption, and formalizing the pursuit of a highly visible (and somewhat uncertain) strategic initiative will further catalyze the rate at which you reap the benefits of the AAA approach.

A New Kind of Organizational Capital

As the maturity model and show-and-tell culture scale, you accumulate a new form of “organizational capital.” It’s not just intellectual property or customer relationships—though those matter. It’s the collective know-how, artifacts, transcripts, and demos your people produce as they learn in public. New hires onboard faster because they have a rich tapestry of best practices to reference. Employees feel more confident trying new workflows because they see peers succeeding.

Over time, these processes, recordings, and shared insights become a durable competitive advantage. You’re not just “doing AI” in a corner—you’re building an organization that learns constantly, adapts easily, and redeploys its expertise at will.

In Summary

1. Level 1: Supercharge personal productivity and accumulate data assets.

2. Level 2: Expand your capabilities across asynchronous, global teams.

3. Level 3: Build custom software solutions that transform your expertise into digital capital.

Overlay this vertical maturity model with the horizontal shift from single-player to multiplayer mode. Start with a few AI power users, embrace show-and-tell sessions to share their breakthroughs, then formalize cross-functional “tiger teams” to tackle ambitious organizational goals. The result? A culture that doesn’t just keep pace with technological change—it sets the pace.

Generative AI is the biggest work paradigm shift since the internet. Those who master these levels and embrace the multiplayer mindset won’t just boost productivity; they’ll redefine the rules of competition.

And now is the time to act.

If you’d like to learn more about implementing this model or discuss private working sessions to jumpstart your journey, feel free to connect with me on LinkedIn and/or send me a DM.