employed for good

good work, built on better systems.

Latest Posts

  • The Tech Conversation We Need to Have (not AI)

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    There’s a lot happening in the tech space right now that we could be discussing. And I’m well aware that it’s a mixed bag of positive, scary, productive, wrong, misguided and hopeful. (How much of each is still TBD. Thank you, current state of AI development.)

    But as someone who’s worked in the technology space for a LONG time, there are few tech topics that truly light up my insides and make me come alive.  So I was jazzed to see multiple people in my networks recently touch on one such idea: tech debt.

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  • On Resisting the Urge to Boil the Ocean (with AI)

    Dolphin leaping from the ocean with splash and sun

    Completely, 100% written by a human.

    In my last post, I broke down the technique that separates ‘sounds good’ AI implementations from high-value, ROI-seeking ones. That technique was this: prioritize the problems you want to solve for, over chasing the best models (or tech) available to get there.

    See my post with the cheeky title ‘Chase problems, not models‘.

    But there is a downside to chasing problems too vigorously, if you’re not careful. In your quest to feed up all your org’s problems & bottlenecks to AI, you may find yourself overwhelmed for another, contrarian reason.

    You want AI to solve for everything, right now.

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  • You don’t need the best model. You need the right problem.

    Two adjacent narrow streets paved with red and gray bricks, flanked by stone cottages and hanging flower baskets

    Completely, 100% written by a human.

    Whether your org is chasing the latest highly ranked models (anyone lamenting the current loss of Fable 5?) OR if your org is still watching AI from the sidelines, then I’ve got a reality check for you. It’s good news.

    You don’t need the latest and greatest models to transform your organization with AI.

    Particularly in the impact space, where so much fantastic well-intentioned work is bottlenecked by bureaucracy, data siloes, administrative tasks or other things that take an extraordinary amount of time…you have ample opportunity to make lasting and impactful changes, specific models aside.

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  • How is AI really going for everyone?

    Two male northern cardinals, one female cardinal, and one eastern bluebird on a wooden bird feeder in winter

    Completely, 100% written by a human.

    The jury’s still out on the real costs & returns of AI. But some of the verdicts have finally started to come back, and you know what gang? It’s not all roses.

    We’ve heard companies cite cost-savings by AI as a driver for layoffs and restructuring. The implication is that efficiency happens when AI can be used to do more things, and more quickly, with fewer people at the helm.

    (Friendly reminder that AI doesn’t displace people. People displace people.)

    But more recently, we’ve started to see companies dial that back. Some have rehired people they’ve laid off OR they’ve come out publicly to grapple with one of AI’s many awkward, current realities: it costs us more $$$ to use it than we thought.

    It also costs environmentally, but I digress.

    The consumption challenges that major enterprises are facing may feel far off. After all, many of us are only just starting to wrap our heads around AI and how to responsibly bring it to our orgs.

    But given how notoriously difficult it is to qualify whose AI implementations are actually going well/ driving growth / making an impact, this latest discourse begs an important, perhaps un-fun question.

    How will we know that our AI efforts are actually working?

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  • Why We All Need to Get More Technical (& 5 Places to Start)

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    Completely, 100% written by a human.

    As people and organizations sprint to use AI – whether they’re doing it well or not – there’s one point that’s become crystal clear.

    Technology is no longer reserved for the technologists. It’s time we all got a little more technical.

    This is true regardless of which side of the AI debate you fall. So if you’re someone that’s eager to jump in, a strong technical understanding will help you cut through the noise, be more effective in leveraging AI’s capabilities, and know its limitations.

    And on the flip side, if you’re someone who is reticent to even touch an AI chatbot – having a base will clarify the technical underpinnings driving your concerns. But it will also carve space for you to start to be able to mitigate those risks – assuming you’re not in a position to ignore AI completely, that is.

    Now when I say “get more technical”, I don’t mean to suggest that everyone become a machine learning engineer overnight. That’s not a good use of your time or mine (unless, of course, you are in fact a current or aspiring ML engineer).

    However, part of the responsibility in using or championing any disruptive technology is having the baseline understanding of how it works, and knowing where your own knowledge is limited.

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  • How to Get Your Org Started with AI (even if you don’t want to)

    Completely, 100% written by a human.

    Short version: An intentional approach to AI will help your org introduce it effectively and with less stress. Jump ahead to:
    – The Prep Work
    – The Policy Work
    – The AI Planning Work

    If you’re feeling the pressure of needing to make AI happen at your organization — along with the dread of actually putting those gears in motion — know that you’re not alone.

    The tension is normal. AI is advancing faster than most of us can catch up, technologists included. And when you can’t even wrap your head around how the underlying tech really works, or what it means for your organization’s data, it can feel like you’re staring at the top of an impossible mountain.

    But just like with any big task, you want to break this down into smaller, manageable chunks. An AI implementation need not be any different, no matter what anyone else tells you about how fast you need to be moving.

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  • The Case for Discernment in AI

    Clear glass filled with sparkling water and bubbles

    Completely, 100% written by a human.

    Short version: Discernment in AI is a critical skill to be practiced on 3 levels: personally, professionally, and as an organization. Jump ahead to:
    – The Case for Discernment in AI Personally
    – The Case for Discernment in AI Professionally
    – The Case for Discernment in AI Organizationally

    As I sit here writing this, it feels like the entire world is sprinting towards AI. Individuals and organizations of every size seem sold on this promise, that artificial intelligence is going to drastically change the ways we work and live.

    And to some extent that’s real. For better, and for worse.

    But regardless of where you sit on the zoomer/boomer(?) -to-doomer scale, the AI “revolution” is here. And while I don’t see a world where we all come out of this unscathed, I do believe there’s one specific attribute that’s guaranteed to separate the technology victors from the rest.

    Well, aside from access to resources & capital I mean.

    That thing? Discernment. Our ability to perceive, judge and distinguish the role AI plays in this world is the boldest and most impactful skill we can all possibly exercise right now.

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