Digital FastForward

I got one of the strangest AI-generated spam emails I’ve seen in a while. It felt like I had accidentally been pulled into something I wasn’t supposed to see—an argument 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻 controlling it. The AI was actually pushing back: “This isn’t a good fit.” “This outreach doesn’t make sense.” And the human… overrode it and sent it anyway. I had to read the email multiple times just to make sure it wasn’t a scam. I even searched to see if anyone else had seen something like this. Nothing. It felt like I got a glimpse into something deeper: What happens when the agent is more context-aware than the human directing it? And it raised a bigger question: Who is actually creating the slop? Is it the AI? Or is it the human insisting on using AI in ways that don’t make sense? In this case, it didn’t feel like the AI was the problem. It felt like the human was. This brought me right back to a conversation that Steph Louis and I had with Stephen Bixby on the podcast around orchestration, guardrails, and auditability. YouTube → https://lnkd.in/dZvkDRev Spotify → https://lnkd.in/dEEUYhdv As AI agents become more capable, trust and oversight become the real challenge. Not just what gets generated… …but why it was generated in the first place. I’ll drop the email in the comments—curious how others interpret it. Or if anyone else has had a similar “wait… what is going on here?” moment with AI-generated emails or other agentic interactions. #AI #EnterpriseAI #AgenticAI #Automation #AIGovernance #Pega

AI makes it possible to build almost anything now. But the real question is: 𝗪𝗵𝘆 𝗯𝘂𝗶𝗹𝗱 𝗶𝘁 𝗶𝗳 𝗶𝘁 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘃𝗮𝗹𝘂𝗲? Recently, a Pega field leader asked me an interesting question: “Are you actually seeing teams use AI to design complex enterprise solutions yet?” My response sparked a pretty heated discussion. I shared that I’m seeing most teams use AI to design simple, low-hanging-fruit applications — small workflow automations, internal tools, and quick prototypes. Only a handful of teams are starting to use AI to design more complex enterprise solutions. The kinds of problems that cut across: • systems • teams • and workflows. That tension between 𝗔𝗜 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 and 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 came up again in a deep-dive conversation Steph Louis and I had with Stephen Bixby (SVP of Product Engineering at Pega) on the Designed to Accelerate podcast. In the episode, we wrestle with what it actually takes to move from AI experiments to enterprise solutions that deliver real value. Four challenges surfaced in the conversation: 1️⃣ Teams naturally gravitate toward low-hanging fruit 2️⃣ Replicating the current system vs. redesigning the process 3️⃣ Integration is still where AI design hits the wall of enterprise reality 4️⃣ AI makes solution design 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 — 𝗻𝗼𝘁 𝗹𝗲𝘀𝘀 I’m curious what others are seeing. What challenges are you encountering when trying to move from AI experiments to enterprise solutions? Is it: • integration • governance • architecture • organizational alignment • all of the above, or • something else entirely? Full conversation with Steve Bixby: YouTube → https://lnkd.in/dZvkDRev Spotify → https://lnkd.in/dEEUYhdv #AI #EnterpriseSoftware #SolutionDesign #IntelligentAutomation #Pega #DesignThinking

I think I just experienced "𝘁𝗵𝗲 𝗔𝗜 𝗳𝗹𝗶𝗽." For the last few years, I’ve worked with AI tools primarily through the lens of them being a net negative — always wondering in the back of my mind: Which jobs are going to disappear because of this new capability? But recently a switch flipped for me. After wasting hours wrestling with my email inbox, I asked the vibe-coding platform v0 to build me a new email client with a New York Times–style interface. (I still don’t understand why this type of email client doesn’t already exist.) I expected v0 to struggle with the prompt. Instead, it produced a working application and structure that went way beyond what I imagined. That moment shifted how I saw AI. I went from thinking about AI as a net negative to seeing it as a net positive. Not because the app was production-ready — it wasn’t. But because it suddenly became clear that AI gave me the ability to do things I simply couldn’t do before. At least not at speed or scale. And almost immediately, a second set of questions surfaced: How would something like this integrate with enterprise systems? What about security, governance, and architecture? Was I really willing to load my email into this vibe-coded app? That intersection — between what AI can suddenly generate and what organizations actually need — is exactly what we explored in my recent conversation with Kerim Akgonul, Chief Product Officer at Pega, on the 𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘀𝗶𝗴𝗻 podcast. 👇 In the clip below, Kerim shares a powerful analogy about architects and builders — and why a new kind of design role and process is needed for AI-Driven Design. Curious — when did your AI flip moment happen? #ArtificialIntelligence #SolutionDesign #LowCode #Pega #VibeCoding

A few years ago, my co-founder, Cornelius Pone, decided to build a home deep in the woods of Poolesville, Maryland. He didn’t start by pouring concrete. He hired an architect. They walked the land and talked through the vision — where the kitchen would go, how the house would sit, how light would move through the space. The architect translated that vision into a plan. Only after they agreed on the plan did the builder begin construction. The roles were clear. ➡️ The architect defined the house. ➡️ The builder made sure it could stand. In our latest podcast conversation, Pega’s Chief Product Officer, Kerim Akgonul, used this house analogy to describe what’s happening with Pega Blueprint and AI-driven design. AI can now generate structure — workflows, screens, prototypes — in seconds. Design starts to feel like build. Build starts earlier. And somewhere in between, the need for a new role emerges — someone who understands both the vision and the structural implications. A solution designer. As AI blurs the lines between roles and stages, clarity matters even more. Without guardrails, speed amplifies complexity — leading to technical debt and “zombie” solutions that take on a life of their own and become expensive to unwind. In this episode, Steph Louis and I explore this tension with Kerim — and what it means for enterprise teams adopting AI-driven design. YouTube → https://lnkd.in/gKuVKeNX Spotify → https://lnkd.in/gPRbtWsG #AI #EnterpriseSoftware #SolutionDesign #IntelligentAutomation #Pega #DesignThinking

Back in 2009, as a newly minted industry analyst at Forrester, Alan Trefler was one of the first tech CEOs I met and had dinner with. During that dinner, Alan grabbed a crayon and started drawing on the tablecloth — explaining why “The Layer Cake” was the most important invention in human existence. In that moment, I knew I was talking to someone willing to drop the executive veneer and go deep on design and architecture. During my time at Forrester, I quickly learned how unusual it is for a CEO to want to spend dinner debating architecture and design. So for our first podcast guest , Steph Louis and I wanted to revisit that same depth — this time focused on how AI is transforming the way solutions get defined, designed, and accelerated. In this week’s episode, we talk with Alan about: • What his four decades leading Pega reveals about AI hype vs. reality • How AI democratizes solution design, making design accessible to all • Why AI accelerates execution but doesn’t always clarify intent • Why solution design becomes more important — not less — in the age of AI • What the future of AI-Driven Design really looks like inside the enterprise Check out this week's episode, "The Future of AI-Driven Design" with Alan Trefler: • YouTube → https://lnkd.in/gMmDhje9 • Spotify → https://lnkd.in/gMFKqmy9 Episode available now on all podcast apps (search “Accelerated Design”). The future of AI-Driven Design won’t be defined by tools alone. It will be shaped by leaders willing to rethink how solutions get defined — and how value gets created. If that’s you, welcome to the conversation! #AIDrivenDesign #EnterpriseAI #SolutionDesign #Pega #DigitalTransformation #Leadership

For years, I’ve watched smart teams invest heavily in enterprise automation platforms like Pega—and still struggle to turn those investments into real outcomes. Not because the technology wasn’t powerful. But because solution design was treated as something that happens after the most important decisions are already made. When Pega first introduced Blueprint, I’ll be honest: I was skeptical. I assumed it was primarily a sales enablement tool—useful, sure, but not fundamentally changing how solutions get designed or how value gets realized. So I stayed on the sidelines. What changed my perspective wasn’t a roadmap update or a pitch. It was spending real time working hands-on with large language models, and seeing how dramatically they compress early-stage design work—shaping intent, surfacing tradeoffs, and forcing better decisions much earlier in the process. That shift led to a joint white paper we published with Pega on AI-Driven Design. The response confirmed we were naming something real—but it also surfaced a limitation. This isn’t a one-time idea that fits neatly into a static artifact. It’s the beginning of a much bigger conversation. So we started a podcast. I’m co-hosting it with Steph Louis from Pega, and together we’re creating space for honest, practitioner-level conversations about how AI is moving upstream into solution design—and what that means for roles, decision-making, and value acceleration. The first episode is called “The New Solution Design Playbook.” Over the first season, we’ll go deep with leaders and practitioners who are actively shaping how this work gets done. Upcoming episodes include conversations with Alan Trefler, Kerim Akgonul, Benjamin Baril, and Stephen Bixby. If you’re on the hook for designing AI and automation solutions that consistently deliver real outcomes, this conversation is for you. The first episode goes live tomorrow on all podcast platforms. I’ll share the link then—but if you want a personal nudge from me when it’s live, just leave a comment below or drop me a DM. #SolutionDesign #Pega #AIDrivenDesign #EnterpriseAI #ValueAcceleration

It’s finally here! What started a few months ago with an unexpected LinkedIn DM has turned into a full-blown white paper that puts a clear stake in the ground on how AI is reshaping solution design. Read The Future of AI-Driven Design report → https://lnkd.in/g5Z9iCW2 Before Steph Louis at Pegasystems reached out, I had already formed a strong point of view from working with dozens of teams using Pega Blueprint. One thing became increasingly clear: most of the challenges teams were running into weren’t really about AI — or Blueprint — at all. Most of the challenges were about: ✱ What happens before AI gets involved ✱ How teams frame problems and think about value ✱ How teams make tradeoffs and align on outcomes AI tools — including Pega Blueprint — have a way of accelerating whatever’s already there, good, bad, and ugly. When Steph and I compared notes, it was obvious we were seeing the same patterns from different angles. We walked away with the same conclusion: if AI is going to play a real role in solution design, then we — Digital FastForward and Pega — needed to put a stake in the ground around best practices, roles, and methodology. The paper challenges leaders to rethink the role of AI in solution design and defines a new discipline we call AI-Driven Design—the core practices required to accelerate value without losing alignment or judgment. We’d genuinely love to hear what resonates for you, what feels incomplete, or where your experience diverges from ours. And if you’re using AI in interesting or unexpected ways in your design work, we want to learn from that too. Steph and I want to thank a small group of leaders and practitioners who directly shaped the thinking behind this paper, including Eric Musser, Alan Trefler, Jackie McKinley, Stephen Bixby, Benjamin Baril, Kerim Akgonul, and Cornelius Pone. And a big thank you to my partner in AI-Driven Design [Crime], Steph Louis. Phew, we stepped onto a springboard - and just getting started! #AIDrivenDesign #SolutionDesign #Pega

It’s budget season, and I keep seeing the same frustrating pattern. Leaders are racing to finish their business cases — but all the energy goes into costs. The value side gets pushed off. Here’s the reality: if you don’t define value at the beginning, it rarely ever gets defined. Once the cost is approved, most teams never circle back to ask: Did we actually realize the value we set out for? I’ve seen this movie too many times in large-scale intelligent automation and AI programs. Teams ground their plans in cost, not value. And what happens? ❌ Costs spiral well past the original plan. ❌ Timelines stretch 2–3x longer. ❌ The risk of not delivering real outcomes skyrockets. When you’re not grounded in value, you end up making decisions based on reaction, not outcomes. It’s like investing without a financial advisor — you know the money is going out, but you’ve lost sight of what return you’re working toward. That’s why courage is required. It’s easy to sign off on cost — write the check, get the approval. It takes real courage to define value, put numbers behind it, and be accountable for delivering it. So here’s my challenge to leaders this budget season: 👉 Put as much effort into the value side of your business case as you do the cost side. 👉 Define it. Quantify it. Simplify it enough that you can share it and get feedback. 👉 Use it as your North Star. Because at the end of the day, no one remembers what you spent. They remember whether it delivered value. #BudgetSeason #ValueRealization #IntelligentAutomation #Pega #LeadershipCourage