What if redefining one simple phrase could change how your entire organization delivers value?

Build your own CTO Playbook at www.theCTOplaybook.com — the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

Why does the definition of ‘done quietly’ determine whether engineering effort turns into real business impact? In this episode, I share a coaching story from a CTO leading a busy organization where motion looked like momentum, but nearly everything stalled just before completion. The teams were working hard, yet features lingered in limbo, ownership blurred, and frustration built across engineering and product.

There is a mental model that reframes software delivery using a familiar sports analogy, showing why writing code or merging branches doesn’t move the scoreboard. Impact only happens when work reaches production, is absorbed by the organization, and enables the next move. This lens exposes how excessive work in progress stretches timelines, fragments focus, and erodes fulfillment for senior engineers.

I talk about what changes when leaders stop tracking activity and start insisting on outcomes. For anyone responsible for CTO leadership, engineering productivity, or scaling teams without burning them out, this conversation challenges how you measure progress and where you apply pressure.

You’ll Learn:

[00:00] Introduction

[01:12] How teams stay busy yet fail to move the business forward when finishing is unclear

[02:08] What happens when too much work in progress creates motion without results

[03:07] Why writing code and merging branches do not equal business impact

[03:56] How the basketball scoreboard analogy reshapes what done really means

[05:14] The leadership question that exposes activity over outcomes

[06:41] What changes when nothing new starts until something is fully done

[08:27] How redefining done restores ownership, focus, and team satisfaction

Find more from Adam on LinkedIn and YouTube, and explore coaching, cohorts, and how you can stay up to date at theCTOplaybook.com, helping you build your own playbook for your path at your pace.

Build your own CTO Playbook at www.theCTOplaybook.com — the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

What if the most dangerous thing we build as leaders is certainty?

Today, I sit down with fellow technology leader Joe Thompson, to examine how ethical technology leadership shows up in the smallest day-to-day decisions, not the mission statements. A CTO mindset shifts once it becomes clear that the products being shipped shape how people work, think, and feel long after the roadmap is finished. Responsibility enters the work through a design-led lens that starts with user research and carries through product strategy grounded in usability, accessibility, and cognitive load.

Earlier in my career, optimizing metrics felt sufficient. That belief changed after seeing software become a primary work tool for thousands of people who had little choice but to live inside it every day. Tech for good emerges here as a leadership posture rather than a side initiative, rooted in intention, influence, and awareness. Digital transformation sharpens that responsibility further, with the power to narrow or expand who technology truly serves as analog channels steadily disappear.

If you’re navigating scale, pressure, and trade-offs as a technical leader, this episode is an invitation to slow down, ask better questions, and lead with impact rather than assumption.

You’ll Learn:

[00:00] Introduction

[03:01] Why design-led products start with user research, not features

[04:27] How optimizing for one metric creates invisible usability and accessibility debt

[07:12] When you realize your software becomes someone’s full-time work environment

[23:04] Why teams ship products without thinking through real-world user impact

[29:18] How engagement algorithms shape behavior and quietly reward harmful patterns

[31:07] What ethical leadership looks like without lecturing or moral grandstanding

[35:02] Why AI feels revolutionary while productivity barely moves

[42:21] Where tech leaders should start when thinking about impact and responsibility

[47:00] How values, influence, and intent guide better technology decisions

Resources Mentioned:

No Silver Bullet Essence - Accident in Software Engineering by Brooks F. | Article

You can connect with Joe and his work through his LinkedIn here.

Find more from Adam on LinkedIn and YouTube, and check out Adam's CTO coaching company here.

Build your own CTO Playbook at www.theCTOplaybook.com — the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

Are you running faster with AI, or just running blind?

Most teams don’t lack data, they lack understanding. Today, I sit down with CTO Jason McGhee, who has spent years inside analytics, machine learning, and product teams asking the hard question: why is the data changing?

AI in analytics works best when it supports human judgment instead of replacing it. A hybrid approach keeps people involved while AI assists with complex tasks, making decisions clearer and systems easier to reason about. Moving faster with AI increases risk when teams cannot explain why the data is changing.

Both recurring reports and one-off investigations break down without context. Dashboards often fail as real deliverables because they separate numbers from explanation. Insight becomes more actionable when it is shared alongside the data itself. Screenshots, slide decks, and disconnected tools add friction, making validation harder and discouraging deeper questions from leaders.

If you care about data-driven decision making, want a more honest relationship with machine learning outputs, or are figuring out how generative AI fits into real-world business analytics, this conversation sharpens how you think about data, trust, and momentum as a technology leader.

You’ll Learn:

[00:00] Introduction

[02:45] Why more data doesn’t help if you can’t explain what changed

[05:12] How keeping humans in the loop changes AI analytics failure modes

[09:48] Why dashboards break down once they leave the builder’s hands

[14:32] How AI turns big analytical questions into auditable steps

[20:41] Why one-off and recurring reports need shared intuition to work

[27:18] How screenshots and slide decks quietly block data validation

[34:55] Why faster AI increases the risk of running in the wrong direction

[43:07] How mixing structured data with Slack adds missing business context

[57:26] The leadership cost of treating analytics as outputs, not understanding

For more information you can also visit writ.so.

You can connect with Jason on his personal LinkedIn or his business LinkedIn.

Find more from Adam on LinkedIn and YouTube, and check out Adam's CTO coaching company here.