For Adam M. Root, an AI Product Management instructor, meaningful AI impact comes from asking the right questions, applying structured strategic frameworks, and keeping human expertise at the center. Without that foundation, even the most advanced models tend to produce shallow or unreliable results. "If you start with garbage in, you're going to get garbage out," Root says. At Maven, Root designs and teaches live cohort-based programs for product managers, designers, and founders who want to build AI-native products, while also serving as a fractional Chief Product Officer - AI Product Strategy. His flagship courses cover AI-first product strategy, prompt-driven prototyping for designers, agile AI development, and go-to-market strategy for AI products—all created to help teams move from curiosity to capability in fast-moving AI environments. Many enterprises are now pushing to scale agentic AI, yet most struggle to understand why their initiatives rarely move beyond pilot stages. Instead of anchoring their work in a clear business problem, teams often chase tools, models, or vendors, hoping technology alone will unlock impact. While it can produce impressive demos, the real enterprise value is minimal. It's a gap that raises the critical question of what truly drives scalable AI success. Why Enterprises Struggle to Scale Agentic AI Enterprises are under intense pressure from boards to show measurable AI outcomes, and this urgency often drives them into execution before understanding what actually needs solving. Root sees the same misstep play out repeatedly: teams race toward outcomes without grounding their efforts in a clear grasp of the underlying problem. "The technology is not advanced enough to do that on its own," he explains. "People think they can plug in more compute or hire another vendor and solve it. It won’t work." Human expertise must exist at the core of agentic workflows. Companies that rely on AI to replicate tribal knowledge lost during layoffs risk stumbling into operational blind spots. Root predicts many enterprises will eventually need to rehire the same subject matter experts at a premium because their institutional understanding cannot be reconstructed by an LLM. Architecting Scalable Workflows: The PURSUIT Framework He leverages the PURSUIT framework which gives enterprises a practical path forward, offering clear guidance on how to avoid these pitfalls. At its core, the framework guides teams through thoughtful problem definition long before they touch a model or dataset. Problem: What is the real human problem we are trying to solve, beneath the surface symptoms? Problem: User: Who is this really for, and what do they value, fear, and prioritize? User: Rationale: Why is solving this problem strategically important and relevant right now? Rationale: Solution: What is the smallest, most transformative product experience that solves the problem meaningfully? Solution: Understanding: What evidence do we need to collect to validate or invalidate our assumptions? Understanding: Insight: What patterns, truths, and behavioral drivers emerge from the evidence, and how should they shape the product? Insight: Thesis: What is the strategic product direction that ties everything together into a single, defensible narrative? Thesis: Technology isn’t in the framework…intentionally. "Most people make their tech the thesis. They start with the tool, and then they work backward," Root says. For him, the right questions matter even more than the right models. Teams often rush into research, parallel model runs, and search aggregation without clarifying what they need to learn. "Ask better questions and you get better results." Root builds human review into the beginning of every agentic workflow, and often prompts the LLM to refine its understanding of the problem before starting tasks. He supplements this with structured interviews from subject matter experts through tools like Strella, creating clean, contextualized knowledge inputs. The Coming Wave: Multimodal and Parallel Systems Root believes the next big advances in AI will come from systems that can use different types of inputs at once, such as text, images, video, audio, and real-world sensor data, from multiple AI models working together at the same time. These models will then be able to compare what they each find and combine their reasoning to produce better answers. AI will be able to look at many kinds of information at once and let several models think through the problem together to produce a clearer, stronger result. Wearable technologies like Meta’s smart glasses are one example of this future, showing how enterprises may soon capture and integrate real-world signals at scale. However, novel capabilities won't solve foundational issues. Without well-designed questions, validated inputs, and early-stage human guidance, multimodal systems simply accelerate flawed reasoning. "If you still start without the right questions, you're not going to get the right solution," Root says. He expects a market correction in 12 to 18 months as this novelty gives way to operational reality. Companies that removed subject matter experts in hopes of AI-driven efficiency may find themselves struggling to recover lost context. Competitors who retain tribal knowledge will have an edge that software alone cannot replicate. The Overlooked Risk and the Undervalued Hire Despite rapid AI-driven automation of entry-level roles, seasoned experts remain irreplaceable. "Your subject matter experts are not replaceable by AI," he says, stressing that enterprises prioritize hiring principal product managers who can translate messy organizational challenges into reliable, scalable workflows. For early stage companies selling into enterprise markets, he suggests recruiting recently laid-off PMs who bring years of insider perspective. "You'll get tribal knowledge that would have taken you years to learn," he says, arguing it can define a company’s strategic advantage. AI's potential is enormous, but only when rooted in strong strategy and thoughtful execution. Enterprises that begin with clarity, human expertise, and better questions will be the ones capable of architecting agentic AI workflows that not only function, but scale. Follow Adam Root on LinkedIn or visit his website for more insights. LinkedIn website

This story contains new, firsthand information uncovered by the writer.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

Adam M. Root: How to Architect Agentic AI Workflows That Scale Across the Enterprise

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