AnswerRocket

The gap between AI winners and everyone else isn't compute power or budget. It's how they build. Our team gets into the details on the latest episode of AI, Actually. Watch it here: https://lnkd.in/e2sp9Gy6 #EnterpriseAI #AIStrategy #AIActually

The organizations getting real results from AI aren't rolling out agents like they're software deployments. They're onboarding them carefully like they're new employees. Our Managing Partner Jim Johnson wrote about this for Forbes Business Council, and it's a framing we think will stick. Link below. 👇 #EnterpriseAI #AIImplementation

Document integrity is one of the less-discussed failure modes in agentic AI, and our Head of Data Science and AI Innovation, Shanti Greene, breaks down exactly why it deserves engineering attention. If your organization is using AI to move documents through multi-step workflows, this is worth a read. https://lnkd.in/eErzZvh8 #EnterpriseAI #AgenticAI #AIAgents

Our May 2026 edition of the Rocket Report is available now! 🚀 Key Highlights Include: • Our resident AI expert Shanti Greene shares how he handles AI Usage Limits in an article from IT Brew. • Jim Johnson hosts Andrew Sweet, Nicole Kosky and Benjamin Titmus to discuss the topic plaguing so many enterprises right now, "Is our data AI-ready?" • We take a look at the role that enterprises tackling AI need right now - the Forward Deployed Consultant. • Learn why developers should lean into multiple models when coding with AI. And so much more! 💡

Your AI is only as smart as what you've taught it about your business. That's the insight behind this clip from Episode 20 of AI, Actually, featuring Benjamin Titmus on the semantic layer. Think of it this way: you wouldn't hire a talented new employee and just say "good luck." You'd onboard them. You'd explain what revenue means in your department, which decisions need escalation, how different teams interact with data differently. Your AI deserves the same treatment. The semantic layer is how you give AI that foundation. It's the language your AI needs to actually understand your business, not just process data. Watch the clip and catch the full episode on YouTube. 🎥 Link in Comments. #AIActually #EnterpriseAI #SemanticLayer #AIAdoption #DataStrategy

Episode 20 of AI, Actually is live. We're talking about the thing that underpins all of AI: data. Not whether your data is "perfect." Not whether you can throw everything at an LLM and let it sort things out. But the real conversation in between, what data readiness actually means, why the semantic layer is the missing piece most enterprises overlook, and how the companies getting ROI from AI are thinking about it differently. Jim Johnson, Andrew Sweet, Nicole Kosky, and Benjamin Titmus dig into all of it. 🎧 Watch/listen here: https://lnkd.in/ePDnrFqB #AIActually #EnterpriseAI #DataStrategy #SemanticLayer #AIPodcast

The conversation around AI adoption usually starts in the boardroom. Michelle Hamilton, our AI Adoption & Change Management Practice Leader, thinks it should start somewhere much more personal. In her episode of Lunch with Leaders, Michelle shares a perspective on AI that's equal parts practical and deeply human -- one that challenges how organizations think about who AI is really for, and how to actually bring people along. It's worth the listen. 🎧 on Spotify: https://lnkd.in/gBqgvWBa 🎧 on Apple Podcasts: https://lnkd.in/g8C8TzBr #EnterpriseAI #AIAdoption #AI

Choosing an AI coding model isn't a one-time decision. It's an ongoing discipline. Our team has been running frontier coding models through real client work for months now, and the verdict is clear: there's no universal winner. The right model depends on your priorities, your budget, and what you're actually trying to accomplish. A few things that hold up across the board: • Pick one model and get genuinely good at it. Depth beats breadth every time. The teams handling the most complex work aren't the ones with the most tools, they're the ones who've learned a single stack inside and out. • Cost tier matters more than people admit. Premium plans sound steep until you lose a week to throttling on a cheaper one. • First-attempt correctness is underrated. Speed metrics look impressive until you're debugging code that doesn't actually work. Our latest blog breaks down exactly how we think about this, with real benchmark comparisons across the major models. Worth a read if your team is making (or revisiting) these decisions. 👉🏼 https://lnkd.in/eNm6v3h9 #EnterpriseAI #AIStrategy #AITools #DataScience

AI token costs are becoming a real budget pressure inside enterprise organizations. IT Brew spoke with our Head of Data Science and AI Innovation, Shanti Greene, who shared practical guidance on how teams can use AI more efficiently without sacrificing output. His recommendations: 👉 Avoid sending unnecessary context in your inputs 👉 Limit output length where possible 👉 Use prompt caching to prevent repeated context from eating up tokens As AI usage scales across the enterprise, efficiency isn't just a nice-to-have. It's becoming a core part of responsible AI strategy. Read the full IT Brew piece here: https://lnkd.in/dkXsYEQY #EnterpriseAI #AIStrategy #AIAdoption #GenerativeAI

The "forward deployed engineer" title is creating a talent mismatch. Our colleague Nicole Kosky makes a sharp argument: AI didn't change what the front lines need most. It just made the gap more visible. The scarce resource isn't the person who can build. It's the person who can tell you what's worth building. Three things she says actually matter when hiring for this role right now: business depth, AI fluency, and synthesis. Worth a read: https://lnkd.in/eaCGnVMR #EnterpriseAI #AIStrategy #FutureOfWork