Software Sourcing in the Age of AI - More B2B slop. // “The average software sourcing process involves 28 stakeholders and takes six months. That’s six months of manual research, vendor meetings, demos, internal debates, and ultimately, a decision that still may not be fully informed."
Lack of AI-Ready Data Puts AI Projects at Risk - If you’ve let your data lakes turn into data swamps your AI projects are going to go poorly. // “Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data."
What AI is good at, or, please don't fuck up my job and ETFs
I’m clearly a big fan of AI and believe it’s helpful in many ways.
I feel comfortable with that because I’ve used it for over two years now and rely on it daily for a wide variety of tasks, both work- and personal-related. That means I know exactly what it’s capable of, what it’s good at, and what it’s not good at. Me and the robot have a good relationship: we know how to work with each other.
Why I think AI take-off is relatively slow - My summary: humans resisting change is a bottleneck; also, humans not knowing what to do with AI; current economic models don’t can’t model an AI-driven paradigm shift, so we can’t measure the change; in general, technology adoption takes decades, 20 for the internet, 40 for electricity. // AI is a technology and is prey to the usual barriers and bottlenecks to mass-adoption.
The reality of long-term software maintenance - Ashley’s blog - “In the long run maintenance is a majority of the work for any given feature, and responsibility for maintenance defaults to the project maintainers”
How’s that open source licensing coming along? - ”The takeaway is that forks from relicensing tend to have more organizational diversity than the original projects. In addition, projects that lean on a community of contributors run the risk of that community going elsewhere when relicensing occurs.”
Top EDI Processes You Should Automate With API - Tech never dies. Helpful consequence: take care of it before it takes care of you.
Key insights on analytical AI for streamlined enterprise operations - ”The big issue, whether it’s generative or analytical AI, has always been how to we get to production deployments. It’s easy to do a proof of concept, a pilot or a little experiment — but putting something into production means you have to train the people who will be using this system. You have to integrate it with your existing technology architecture; you have to change the business process into which it fits. It’s getting better, I think, with analytical AI.”
GenAI Possibilities Become Reality When Leaders Tackle The Hard Work First - Like any other tool, people have to learn how to use it: “Whatever communication, enablement, or change management efforts you think you’ll need, plan on tripling them.” // Also, garbage in, garbage out: “GenAI can’t deliver real business value if a foundation is broken. Too many B2B organizations are trying to layer genAI on top of scattered, siloed, and outdated technologies, data, and processes. As a result, they can’t connect the right insights, automations stall, and teams are unsure of how to apply genAI beyond basic tasks."
A.I. Is Changing How Silicon Valley Builds Start-Ups - ”Before this A.I. boom, start-ups generally burned $1 million to get to $1 million in revenue, Mr. Jain said. Now getting to $1 million in revenue costs one-fifth as much and could eventually drop to one-tenth, according to an analysis of 200 start-ups conducted by Afore.” // Smoke ‘em if you got ‘em…