Many organizations start by focusing on efficiency gains. That’s a natural entry point, but it only scratches the surface. Recent studies [1][2] show that these quick wins often don’t translate into lasting impact whether using AI in products or workflows — underscoring the importance of looking beyond only time savings and hype to AI’s potential as a growth driver.

Reframing the opportunity

The real leverage of AI is not in shaving a few hours off document writing or development. It is in helping us identify the right problems to solve more quickly, so that the time and resources we invest generate outsized impact for customers and the business.

  • AI’s strength is surfacing connections across vast amounts of information at a speed we cannot match.
  • Creativity is not replaced by AI. It is accelerated. Humans set the context and constraints, and AI broadens the possibilities.
  • The real power is in collaboration: using AI to create shared context quickly, align teams faster, and act as a collective brain across functions.

So far, most use of AI has been isolated: individuals asking for a quick draft of text or code autocompletion. That proves the technology and tools are useful, but the real leverage comes when we embed it into our workflows and systems.

A systemic approach ensures we are not just going faster in isolated moments, but consistently moving faster in the right direction across the whole Product lifecycle, GTM and beyond.

Beyond the SDLC

The SDLC is where the opportunity feels most obvious today, but AI can accelerate beyond the SDLC, it can enhance the product lifecycle — analyze data, draft PRDs, user stories, test cases, and even create initial mockups, prototypes and even some code.

The bigger play is not just AI in the SDLC. It is AI embedded across the our systems like the product lifecycle, for example:

  • Discovery: Connect insights from QBRs, sales calls, feature requests, and support tickets, making connections faster.
  • Design and development: Accelerate artifacts, align faster.
  • Iteration: Feed learnings back into discovery continuously, surface and implement enhancements sooner.

This creates a loop where insights flow seamlessly through discovery, delivery, and iteration that moves us in the right direction faster.

Why this matters

If we treat AI purely as a way to accelerate execution, we will miss its larger potential. The highest return comes when AI enables us to:

  • Identify problems earlier by connecting signals across the business, ensuring we focus on what matters most to customers and the market.
  • Free up engineering, product, and design time from repetitive tasks, so teams spend more time solving meaningful problems and refining usability.
  • Focus people, our most valuable resource, on critical thinking, creativity, strategy and innovation. AI generates breadth and speed. Humans apply judgment, context and stakes to ensure we are building the right things.

Moving faster in the wrong direction compounds risk and waste. The opportunity is to combine velocity with sharper judgment about which problems we choose to solve for the market and our customer’s, and reinforce GTM.

Where to start

Growth Through AI Fluency

The success of any systemic approach depends on how effectively people use the tools. Right now, effectiveness is uneven. Some treat AI like Google search while others use it as a real thought partner.

We need to build shared practices around AI, an AI playbook so to speak, so that everyone shares a baseline and can learn from and teach one another.

Framed as structured learning initiatives, AI fluency can become both a growth lever for our teams [3] and a playbook for the business overall:

  • How to frame prompts with context and constraints.
  • How to review outputs critically and refine them.
  • How to decide when AI is useful and when it is not.
  • Which models are best for what tasks.

The stakes

AI has no stakes. It does not live with the consequences of choosing the wrong problem, or making the wrong tradeoff. We do. Our creativity, judgment, and understanding of the market are what turn AI’s speed into business impact.

If we use AI only to go a little faster here and there, we’ll achieve sporadic efficiency but miss exponential growth. Many pilots fail to deliver lasting value [1], which makes it even more critical to embed AI thoughtfully into our systems.

If we embed AI thoughtfully, and focus on where the technology delivers on its promise — we can capture not just efficiency, but outsized impact and growth.

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