Method

We don't want to date ourselves, but for over 25 years, Method has been helping companies define what comes next. No matter which industry it's in, there are common success factors and pitfalls that occur. Michael Lewandowski dives into them and how they're shaping reinvention in financial services. https://bit.ly/3TK60S4

Most companies use a single AI tool to observe, but as the technology evolves, it requires systems that can observe with nuance, rehearse with fidelity, and act with accountability. With more data available and complexity climbing, we look at the technological shift from observe to act that is taking place with automation. https://bit.ly/4gI3GV7

AI is making code easier to produce, but the cost of building the wrong thing has definitely gotten louder. As teams move toward AI-native delivery and a lifecycle filled with coding agents and agentic workflows, how can companies succeed in this new workflow? In this episode of Build What's Next, Jason Rome and Cory Voglesonger discuss the importance of outcome and lean thinking in order to prevent AI-driven sprawl. Give it a listen here 👇, Spotify: https://lnkd.in/e65iH-uF Apple: https://lnkd.in/eWEwfwQ6 Or wherever you get your podcasts. 🎧

Traditional deployment sequences force companies to wait years accumulating structured data before deploying AI. By placing modern vision systems directly at the source, AI creates structured records from physical signals on day one without prior logging protocols. See how it can go beyond just analyzing existing processes > https://bit.ly/4fpKQBc

AI engineering is about making models operational, not just "intelligent". At the end of the day, product, data, platform, legal, security, and finance all have to interact with the AI team. If those relationships are poorly designed, costs increase while the model usage stays flat. We explain why teams should manage AI like a complex socio-technical system. https://bit.ly/4mI7Hda

One of the biggest challenges for financial institutions that are rethinking their go-to-market strategy is the heavily siloed nature of these organizations. How can this be overcome, and what are the success factors and pitfalls that help realize success with tech like AI? Michael Lewandowski explains in his latest blog > https://bit.ly/3TK60S4

AI introduces tools like agents and copilots into a complex ecosystem where you only know if something works after you try it. However, building software has always meant operating under uncertainty. We've compiled 2-3 experiments for each PDLC stage that your team can run to improve its workflow. https://bit.ly/3OIdIdg

"When you think about using AI, I always say one third (of tools) advise you, one third assist you, and one third be an adversary to you." - Jason Rome, SVP of Digital Strategy & Customer Experience. AI is everywhere. But which practical strategies should you use to ensure you're working better? In our latest podcast, Jason and CPP Investments | Investissements RPC's Jon Webster dig into the economics behind AI, the human risk, and strategies to help ensure you're maximizing opportunities. Give it a listen here 👇, Spotify: https://bit.ly/4fqA1yy Apple: https://bit.ly/4b4hhmf Or wherever you get your podcasts. 🎧

Deploying new software or AI into a broken process won’t fix the generator interconnection backlog. We explain how a system-first approach allows teams to effectively transform grid planning by addressing upstream process friction, fragmented data, and manual workflow disconnects. https://bit.ly/4eSnnsh

AI models now influence credit decisions, fraud detection, customer interactions, pricing, and operational automation. And while more companies are seeing tangible returns, critical gaps remain for many organizations. We dive into what it takes to put the operating capability in place to manage AI initiatives at scale. https://bit.ly/4wheM8M