Expert Mode: AI’s Role in De-Risking the Enterprise and Unlocking True Agility
This article was based on the interview with Alan Mosca, Co-Founder and CTO at nPlan by Greg Kihlström, AI and Marketing Technology keynote speaker for the B2B Agility with Greg Kihlström podcast. Listen to the original episode here:
In the modern enterprise, “agility” is a term that has been discussed to the point of exhaustion. We all strive for it, build teams around it, and invest in technology to enable it. Yet, for many in leadership, particularly those overseeing vast, multi-year initiatives, true agility remains elusive. It’s one thing to pivot a digital campaign based on weekly performance data; it’s another entirely to steer a multi-billion dollar, decade-long project that has more moving parts than a Swiss watch. The sheer complexity can lead to a state of perpetual reactivity, where planning cycles are outpaced by the very problems they are meant to solve.
This is where the conversation around Artificial Intelligence needs to elevate beyond simple automation and productivity hacks. While the ability to generate copy or summarize a meeting is useful, the real enterprise value of AI lies in its capacity to function as a strategic co-pilot, navigating complexity at a scale no human team could ever hope to manage. In industries like large-scale construction, where delays are measured in millions of dollars per day, AI is not just a “nice-to-have” efficiency tool; it is becoming a foundational capability for forecasting, risk mitigation, and strategic decision-making. By examining how AI is being applied in these high-stakes environments, we as marketing leaders can draw powerful parallels and gain a clearer vision for its transformative potential within our own organizations.
Moving Beyond the Efficiency Trap
The initial allure of any new technology is often its promise of efficiency. “Do more with less,” “save time,” “automate manual tasks”—these are the familiar refrains. While not untrue, focusing solely on this narrow lens of value can cause us to miss the forest for the trees. Saving a team 50 hours a month on reporting is a commendable gain, but for a billion-dollar enterprise, it’s a rounding error. The true paradigm shift occurs when technology doesn’t just make an old process faster, but enables an entirely new, more powerful one.
Alan Mosca points out that the real value isn’t in saving a few hours, but in fundamentally changing the scale and scope of what’s possible. By compressing a forecasting process that once took months into mere minutes, you don’t just get one forecast faster; you get the ability to run hundreds of forecasts, exploring a multiverse of potential scenarios. This moves the organization from a reactive stance of trying to stay on track to a proactive one of choosing the best possible track from countless alternatives.
“If I’m taking a forecasting procedure that used to take six months and I can do it in 10 minutes, which is our core value proposition, what does that mean? Well, it means that I can do hundreds of them every day, which means I’m now entertaining this entire space of scenarios for different plans… That’s how you create the value rather than, ‘I’m gonna save 50 hours a month of manual work.’ And like, sure, that’s cool, but you know, organizations that spend billions of dollars, 50 hours a month is not registering on any scale.”
For marketing leaders, the parallel is clear. Imagine being able to simulate the outcome of a year-long global product launch not once, but across hundreds of different budget allocations, channel mixes, and creative strategies. The value isn’t just in building the marketing plan faster; it’s in de-risking the entire initiative by identifying potential points of failure and optimizing for success before a single dollar is spent. This shift from simple automation to strategic simulation is where AI delivers exponential, rather than incremental, returns.
AI is More Than a Chatbot
With the Cambrian explosion of Large Language Models (LLMs), it’s understandable that for many, “AI” has become synonymous with “ChatGPT.” We see it in the proliferation of tools designed for content creation, summarization, and basic Q&A. While these applications have their place, this narrow focus creates a significant blind spot for enterprise leaders. The most powerful AI systems are not simply generalist chatbots, but a sophisticated orchestra of specialized models working in concert.
Mosca highlights this common misconception. True enterprise-grade AI involves a complex stack of technologies: bespoke machine learning algorithms for forecasting, proprietary generative models trained on specific industry data, and LLM-powered agents for automation and analysis. To believe that mastering a few prompts is the extent of leveraging AI is to fundamentally misunderstand its potential.
“The biggest misconception that I find now is that everybody just thinks that when you mention AI, you actually mean ChatGPT… I see this a lot in like the thought leadership circles…where people are giving webinars, ‘This is how you use AI,’ and it’s just a series of prompts. And I’m like, that’s like 0.1% of the things that you can do. We’ve developed an army of different machine learning algorithms, plus our own LLM agents, plus our own LLM models and generative models for plans and everything else. And those are the things that to me are exciting.”
This is a critical distinction for marketing leaders navigating a crowded MarTech landscape. Many vendors are hastily slapping an “AI-powered” label on their products, which often amounts to little more than a thin wrapper around a public API. A truly intelligent system, however, is one that leverages fine-tuned models trained on your specific business context—your customer data, your campaign history, your market dynamics. The future of marketing AI isn’t just about asking a chatbot to write an email; it’s about deploying specialized models that can predict customer churn with 95% accuracy, optimize media mix in real-time across 50 markets, and identify the next emerging trend before it appears on any trend report.
Leadership’s Role: Fostering Hunger, Not Mandates
Technology, no matter how powerful, is only as effective as the culture it’s deployed into. In risk-averse, highly regulated industries, the natural inclination is to move cautiously. Yet, the pace of change with AI demands a different mindset. It requires leadership that is not just open to innovation but actively “hungry” for it—a leadership that sees data not as a liability to be guarded, but as a strategic asset to be leveraged.
Mosca explains that the most successful deployments happen when there is a top-down mandate to explore and improve, coupled with a bottom-up excitement from the teams who will use the tools. The roadblocks often appear when this alignment is missing, personified by the proverbial data gatekeeper who stifles progress in the name of security or asset valuation, failing to grasp the opportunity cost of inaction.
“Leadership needs to be hungry for opportunities to improve their organization. And that’s usually the spark that ignites everything else… Conversely, you have the opposite. And chief data officers are usually very good. But every once in a while, there’ll be a chief data officer that appears out of the blue in the middle of an engagement and says, ‘Why are you using our data? Our data is worth $2 trillion. So you have to pay us or you’re not getting it.’ And usually then we walk away when that happens.”
This anecdote, while specific to a data officer, represents a broader cultural challenge familiar to every marketing leader who has tried to launch a new CDP, attribution model, or personalization engine. Success depends less on the technology itself and more on securing executive sponsorship and fostering a collaborative, data-forward culture. Furthermore, when it comes to preparing teams, the answer isn’t top-down mandatory training, which often feels like homework and breeds resentment. Instead, it’s about identifying the internal champions—the naturally curious and excited individuals—and giving them the space and permission to experiment, to learn, and to lead the charge. They will become the internal advocates who demonstrate the value far more effectively than any corporate-wide training module.
Summary
The journey of integrating AI into the enterprise is a marathon, not a sprint. As Alan Mosca’s experiences in the high-stakes world of construction demonstrate, the path is fraught with challenges, from data accessibility and cultural inertia to the very human difficulty of thinking in probabilistic terms. Yet, the rewards for navigating this complexity are immense. The ability to look at a ten-year, ten-thousand-activity project and not just manage its execution but accurately forecast its risks and opportunities represents a fundamental leap in strategic capability.
For us as marketing leaders, the lessons are directly applicable. We must guide our organizations to look beyond the superficial applications of AI. The goal isn’t simply to write more blog posts or analyze sentiment faster. The goal is to build a predictive, agile marketing engine that can simulate outcomes, de-risk major investments, and create more meaningful connections with customers at scale. This requires a commitment to building a sophisticated data and technology stack, but more importantly, it requires a leadership mindset that is relentlessly curious, strategically hungry, and willing to empower its best people to explore the frontier of what’s possible.
