Steve Lucas, CEO and Chairman of Boomi, is the creator of Digital Impact and a multi-time CEO with nearly 30 years of leadership experience in enterprise software. He has held CEO and senior executive roles at a number of the world’s leading cloud organizations, including Marketo, iCIMS, Adobe, SAP, Salesforce, and BusinessObjects.
Boomi is a number one provider of cloud-based integration platform as a service (iPaaS), helping organizations connect applications, data, and systems across hybrid IT environments. Its low-code platform enables rapid integration, automation, API management, and data synchronization to support digital transformation and streamline operations for businesses of all sizes.
As a multi-time CEO, how has your leadership approach evolved within the face of AI-driven disruption? What’s different about leading now vs. a decade ago?
Leading today is fundamentally different from even three years ago, let alone a decade. Back then, digital transformation was a strategic advantage. Today, it’s a survival imperative. AI-driven disruption has completely reset expectations around speed, adaptability, and data-driven decision-making. As a CEO, which means I not have the luxurious of linear planning or incremental improvement. The pace of change, particularly in my industry, demands daring, system-level pondering and execution.
Should you’re pondering that AI is just one other tool in your stack, you’re incorrect. It’s a force multiplier. Or no less than it could possibly be in the event you architect your organization with AI at the middle of all the pieces you do. In every discussion with my team, I at all times ask: “Have we considered how we will use AI on this initiative?” It’s literally a part of every discussion. That’s modified how I lead. I’ve at all times been hyper-focused on integration, data transparency, and breaking down silos. But now, all of that’s in service of constructing AI higher. Leadership continues to be about aligning teams around goals. But now AI is at the guts of achieving those goals.
Above all, today’s CEOs should be deeply human in how they lead. AI is accelerating all the pieces, and that may worry people. It’s why the human element (our values, our judgment, our empathy) must guide how we deploy it. It’s not nearly digital transformation. It’s about human transformation.
Your book argues that AI will fail without fixing digital infrastructure. Are you able to explain what you mean by “digital fragmentation” and why it’s such a critical issue at once?
Digital fragmentation is the silent killer of enterprise AI efforts. Over the past twenty years, organizations have raced to digitize their workplaces, adding more systems, apps, clouds, and platforms. But in that rush, few paused to construct meaningful integration between them. The result’s a tangled web of disconnected technologies and data silos that may’t confer with one another. The sum was lower than all of those parts.
Now, AI is forcing corporations to finally confront that fragmentation. AI systems require clean, connected, real-time data to operate well. But most businesses are attempting to scale AI across an unstable data foundation. That’s why, based on industry data, greater than 70% of enterprise AI projects fail. It’s not because AI doesn’t work, but since the digital environment around it is just too fragmented for it to succeed.
In , I argue that before any leader invests one other dollar in AI, they have to first fix the muse. Which means creating an integrated, AI-ready architecture that connects systems, harmonizes data, and enables intelligent automation. Otherwise, AI will only amplify the chaos.
In “Digital Impact,” you highlight real-world examples where integrated tech is making a difference — from disaster relief to sustainable farming. What case study surprised or inspired you probably the most while writing the book?
The instance that stuck with me most was the work done during a series of natural disasters to offer rapid emergency relief through integrated systems. In a single case, multiple disconnected government and aid organizations needed to collaborate in real-time, sharing data on all the pieces from infrastructure damage to the situation of vulnerable populations.
Historically, that type of coordination would’ve taken days if not weeks. But with integrated digital infrastructure and automation, they were in a position to respond in hours. Emergency supplies were rerouted, housing was secured for displaced families, and aid was delivered with a level of speed and precision that saved lives.
That case showed to me what’s possible after we stop treating integration as an IT problem and begin seeing it as a human imperative. Technology is at its best when it disappears into the background and just works seamlessly, intelligently, and in service of real people.
The subtitle of your book references “The Human Element” of AI-driven transformation. How can we ensure people remain at the middle of this technological shift?
That’s an important query of all. In , I argue that probably the most powerful AI strategy is a human strategy. We’re not constructing AI for machines. We’re constructing it to serve people. But it surely’s easy to lose sight of that in the frenzy to automate, scale, and optimize.
To maintain people at the middle, we must design AI systems that human capability, not replace it. Which means creating tools that reduce digital friction, support higher decision-making, and release time for more meaningful human work. It also means being deliberate about transparency, fairness, and ethics when AI makes decisions that affect people’s lives.
Most significantly, we want to equip every worker with the talents, access, and confidence to work alongside AI. It’s about melding the perfect of human and machine intelligence. This task isn’t relegated to simply data scientists or engineers. It is a moment for inclusive transformation, not exclusive innovation. If the human element is ignored, AI will turn into just one other tech fad. But when we get it right, it could possibly be probably the most humanizing force within the digital age.
You mention that organizations are constructing skyscrapers on sand. What are a number of the most typical architectural mistakes corporations make when adopting AI?
Probably the most common mistake is treating AI as a plug-and-play solution moderately than an ecosystem evolution. Leaders are sometimes dazzled by the promise of AI and jump straight into implementation without addressing the digital sprawl beneath it. That’s like constructing a penthouse suite on top of a collapsing constructing.
One major architectural issue is siloed systems. Most enterprises run dozens, even a whole bunch, of disconnected applications. Their data is locked in proprietary formats, spread across clouds, departments, and platforms. AI can’t thrive in that environment. It needs clean, consistent, real-time, interconnected data.
One other big mistake is underestimating the importance of integration and automation. Corporations implement AI pilots that work in isolation — but they don’t scale since the underlying workflows aren’t automated or integrated across systems. It’s like putting a rocket engine on a bicycle.
lays out what I call “AI-readiness” architecture, which is a set of principles for constructing modular, connected, secure, and scalable systems. Without that, AI is just window dressing.
Many leaders consider throwing more AI at problems will drive results. What’s the danger in that mindset, and the way can your book help reset expectations?
The most important risk is mistaking activity for progress. More AI doesn’t routinely mean higher outcomes in the event you apply it to broken, fragmented systems. Should you don’t fix the underlying process, AI will just amplify the present flaws. You’ll automate inefficiency, scale bias, and speed up chaos.
We’ve seen organizations spend hundreds of thousands deploying AI models only to hit a wall because they lacked clean data, integrated workflows, or change management strategies. In , I call this the “shiny object trap.” Leaders chase the newest model or tool, but they forget to ask an important query:
The book is a wake-up call. It helps reset expectations by grounding AI transformation in business reality. It’s not about how much AI you deploy but how thoughtfully you apply it, how well it integrates along with your ecosystem, and the way it serves your people.
That is the moment for clarity over hype, architecture over acceleration, and folks over platforms.
You’ve said, “SaaS as we comprehend it is dead.” Are you able to elaborate on what replaces it in an AI-first world — and the way agents will transform our interaction with software?
Absolutely. SaaS as we comprehend it – tabs, logins, dashboards, manual workflows – is already on life support. The following era is about : AI-powered copilots that autonomously take actions in your behalf based on the parameters you set and the information you provide.
In an AI-first world, software becomes invisible. You won’t “use” apps in the normal sense. As an alternative, you’ll tell agents what you wish, and so they’ll execute those tasks by accessing apps and systems. Wish to onboard a brand new worker? An agent will spin up the proper tickets in IT, provision access, update your HRIS, and send the welcome email – all with out a human clicking through five systems. It’s fascinating!
Agents are replacing interfaces. They’re redefining productivity. SaaS isn’t going away, but how we interact with it’s fundamentally shifting. The businesses recognizing this now will outpace those still optimizing for clicks and dashboards.
Boomi is pioneering AI agents that may work across apps. In practical terms, what sorts of tasks are these agents taking on today — and what’s next?
Our Boomi Enterprise Platform automates time-consuming tasks humans hate, and systems can’t handle alone. It’s the messy middle. Take into consideration syncing customer data between Salesforce and NetSuite, resolving supply chain discrepancies, or validating invoices across finance platforms.
These aren’t flashy use cases. They’re foundational. And that’s the purpose. We’re not talking about replacing humans. We’re talking about augmenting teams by removing digital friction and connecting data across systems so people can deal with high-impact work.
What’s next? Context-aware agents that don’t just follow rules but . Agents that understand business intent and adapt to alter. We’re constructing toward a world where every worker has an AI partner that works across apps, learns preferences, and proactively solves problems before they escalate.
What role do platforms like Boomi play in helping organizations shift from traditional software use to intelligent automation powered by agents?
Boomi is the connective tissue. You’ll be able to’t deploy agents effectively in a fragmented, disconnected ecosystem. Without integration, automation, and clean data, agents are like sensible minds stuck in a digital traffic jam.
Boomi clears the road. We unify apps, automate workflows, and expose data in ways agents can actually use. Consider us because the infrastructure layer for agentic AI. We’re plugging into a whole bunch of systems, enabling automation across them, and delivering real-time intelligence to agents in order that they can act with context.
We’re not only enabling AI. We’re empowering it to be . That’s the difference between cool tech demos and scalable transformation. With Boomi, organizations could make the leap from software as a destination to AI as an motion engine.
What inspired you to jot down this book now, and the way do you hope it’s going to change how tech and business leaders take into consideration transformation?
I wrote because we’re standing at a pivotal moment within the history of technology. I consider most leaders are focused on the incorrect thing.
At once, everyone’s talking about AI. But few are talking about AI actually works in the true world. The reality is you may have probably the most powerful AI on the planet, but in case your systems are fragmented, your data is stale, and your infrastructure is brittle, that AI is useless.
I’ve seen too many digital transformation efforts fail because they ignored the plumbing: the connections, the automation, the information readiness. I wanted to reveal that tough truth, but in addition offer a way forward. This book is a blueprint for methods to make AI and transformation actually , not only theoretically, but practically, system by system, team by team.
Is there a core message or call to motion you would like every reader of Digital Impact to walk away with?
Yes! Fix the muse.
We are able to’t keep constructing tech empires in digital quicksand. Before you chase the subsequent AI headline, ask: Are our systems connected? Is our data flowing freely? Are our teams aligned around outcomes, not tools?
is a call to return to first principles. Integration. Automation. Human-centered design. These are usually not “back office” concerns; they’re the front lines of transformation.
The leaders who achieve this era will probably be those who construct infrastructure that’s intelligent, agile, and invisible. My hope is that this book helps more leaders deal with what matters most, so we will all deliver on the promise of AI create a greater digital future for everybody.
