Navigating the AI Hype Cycle: When to Build or Wait

Dissects the boom-bust pattern of emerging tech adoption, giving actionable guidance on when to build, partner, or wait.

Part I: The Mirage and the Momentum

The AI economy is moving at a pace that forces every executive to ask: Are we too early? Are we too late? Are we getting distracted? The narrative is seductive. Founders are building faster. Boards are buying the story. Markets are rewarding the illusion of inevitability. But as someone who has operated across the full breadth of organizational complexity—from Series A startups to enterprise-grade digital transformation programs—I have learned that what moves quickly in the press often moves messily in practice.

My background across finance, operations, analytics, and business intelligence has taught me that adoption curves do not follow clean S-curves. They spike. They stall. They recalibrate. Nowhere is this truer than in generative AI. We are in a period marked not by maturity, but by volatility. And that requires something many companies are not great at: pacing. In this first of three parts, I lay the groundwork for a structured lens on hype, grounded in capital stewardship, execution, and competitive timing.

Let’s call it what it is: the AI hype cycle is not a market condition. It is a psychological weather system. And those who lead well during it do not bet on where the clouds move—they plan for visibility through fog.

Understanding the Phases of Hype

Gartner coined the term “hype cycle” to describe the trajectory of excitement around emerging technologies. It comprises five stages: Innovation Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, and Plateau of Productivity. In real markets, these stages are nonlinear. They often overlap. They don’t signal hard stops but operating moods.

The current phase of GenAI adoption shows all the signs of peaking. Investment is flowing faster than infrastructure. Pilots are being greenlit without purpose-built governance. Agent-based systems are being layered into workflows faster than integration allows. And beneath it all lies a mounting uncertainty about what is real, what is repeatable, and what will compound into strategic advantage.

Founders must resist the urge to chase narrative velocity. Boards must question not just what is being built, but why now, and for whom. This is not conservatism. It is timing discipline.

A Personal Calibration

In my own career, I’ve watched similar cycles across analytics, cloud computing, and data science. When analytics dashboards first became mainstream in SaaS companies, everyone rushed to build visualizations. But few invested in clean data or metric governance. The result? Beautifully useless charts. The same happened when machine learning became fashionable in forecasting. Everyone wanted models. Few had the rigor to question drift, seasonality, or human override.

What I learned—and what I teach the teams I work with—is that tools only add value when matched with infrastructure, incentives, and institutional patience. If you deploy AI before you understand how it learns, it will regress to the mean. If you implement agent workflows before redesigning decision rights, they will mirror your organizational dysfunction. Hype is not dangerous because it overpromises. It is dangerous because it reallocates focus away from fundamentals.

Strategic Timing: The Cost of Being Early

Many founders are told that being early is a competitive edge. It is not. Being early without leverage creates strategic debt. You burn resources, expose customers to immaturity, and accumulate tech clutter that will be rewritten later. In AI, the cost of being early can be especially high because:

• Model behavior evolves rapidly, creating maintenance overhead
• Vendors lock you into immature tooling ecosystems
• Lack of regulatory clarity raises compliance risk
• Early use cases often lack cost-benefit discipline

I advise founders to draw three timing thresholds:

1. Market readiness: Is the customer problem acute, budgeted, and acknowledged?
2. Product readiness: Can you deliver a reliable, testable output that improves over time?
3. Org readiness: Does your internal team know how to govern, iterate, and de-risk the deployment?

If the answer to any of these is no, wait. Or better, partner with a firm that already has infrastructure and exposure. In the GenAI age, learning by osmosis through intelligent alliances is smarter than blazing trails with broken shovels.

Signals of Substance vs. Signals of Hype

Boards must equip themselves with the literacy to distinguish between market momentum and maturity. A few critical indicators to look for:

• Signal of Hype: The team mentions LLMs but can’t articulate the training data provenance or model constraints.
• Signal of Substance: The team has instrumented feedback loops, documented performance thresholds, and a data privacy roadmap.
• Signal of Hype: Press releases outpace pilot results.
• Signal of Substance: The AI roadmap is tied to quarterly learning goals, not static feature releases.

In board meetings, the question must not be “are we using AI?” but “are we designing around its constraints and iterating into its capabilities?”

The Role of the CFO: Capital Allocation Under Uncertainty

As a finance leader, I view hype cycles not through the lens of novelty, but through the prism of capital efficiency. Every dollar deployed into AI experimentation must be accountable to some future compound return: better decisions, faster learning, reduced friction, enhanced defensibility. If it does not fit those arcs, it is not investment. It is hobbyism.

CFOs should build a separate ROI framework for AI investments:

• Time-to-feedback: How quickly do we learn from each iteration?
• Model confidence: What’s the margin of error or override rate?
• Data leverage: Are we making proprietary data work harder?
• Elasticity: Can this system scale without human headcount scaling linearly?

These are not standard metrics. But then again, AI is not a standard asset class. Boards must get used to new vocabulary of judgment. That means designing for exploration, while enforcing discipline.

Part I Closing Thoughts

Founders and boards must learn to surf hype without drowning in it. The role of leadership in an AI hype cycle is not to block experimentation, nor is it to greenlight every idea. It is to ask the questions that time-box uncertainty and allocate capital toward compound learning.

Part II – From Deployment to Discipline: What Happens After the AI Honeymoon Ends

The honeymoon always ends. This truth holds for relationships and startups alike—and most especially for technology cycles. What begins with euphoria and media saturation eventually moves into something far more sobering: operational complexity, cost scrutiny, leadership skepticism, and rising user expectations. This is the post-hype plateau where AI must prove it deserves a permanent seat at the strategic table. The Board no longer asks “Why aren’t we using GenAI?” but instead, “Why is this still not driving margin?” or “Why do we need to hire three new engineers just to keep this model alive?”

As someone who has led finance, analytics, operations, and systems strategy across multiple technology-driven sectors—ranging from AdTech to SaaS to medical devices—I’ve come to appreciate the rhythm of tech adoption. There’s always a moment when exuberance gives way to realism, when the glossy demo must be reconciled with backend constraints, and when Boards begin asking: Did we overbuild? Undergovern? Or worse, misdiagnose what AI could realistically deliver?

If Part I of this essay focused on identifying the signs of the hype phase and choosing when to build, partner, or wait, Part II concerns itself with what to do once AI has been deployed, expectations are mounting, and the systems are no longer shiny. This is where strategic capital allocation meets execution discipline. Where AI leaves the PowerPoint and enters the P&L.

The Post-Hype Drop: Recalibrating Expectations

By the time the dust begins to settle, most AI initiatives look less like magic and more like middleware. The early wins—automated summaries, faster financial closes, agent-based sales forecasts—start blending into business-as-usual. Meanwhile, hidden costs creep in: model maintenance, data labeling, user retraining, latency issues, and constant prompt refinement.

At this stage, founders face a different challenge: transforming tactical wins into systemic value. The initial pilots may have delivered productivity gains, but few early-stage companies sustain velocity without revisiting what they’ve built and why. The AI hype cycle tends to compress timelines in unrealistic ways. What should have been a two-year investment is often crammed into a quarter. The result: patchy deployments, overly broad toolkits, and insufficient governance.

It is here that Boards and CFOs must begin asking better questions:

• Is the AI deployment self-sustaining, or does it require permanent intervention?
• Are our KPIs aligned to business outcomes, or just to technical output?
• What organizational capabilities have we actually built—or did we simply buy another tool and wrap it in our logo?

AI is Not a Cost Saver—It’s a Learning Engine

A common mistake companies make post-hype is to expect AI to function as a perpetual cost-cutting engine. This expectation is rooted in outdated automation logic. Traditional RPA or scripting tools removed redundancy. GenAI, by contrast, requires human feedback to remain useful. It is a learning engine—not an end-state system. When Boards assume GenAI is plug-and-play, disappointment is inevitable.

Consider the finance team. After deploying agents to accelerate revenue recognition and automate accrual commentary, it becomes clear that variance narratives are still inconsistent. The agents summarize data but do not yet understand context. What’s needed is a new role—part controller, part prompt engineer, part business partner—who can oversee, refine, and continuously train the system. This costs money. But when done right, it creates durable leverage.

The right question at this phase is not “How much can we save?” but rather, “How much faster and more accurately can we learn?” Enterprises that frame GenAI around learning loops rather than cost lines will unlock greater organizational returns. Why? Because the agent’s value compounds over time as it ingests feedback, whereas the cost-savings plateau quickly without reinvestment.

In my experience, the highest-performing teams are those that integrate AI performance into the management cadence. They track intervention rates, prompt churn, and feedback lag just as they would track sales funnel velocity or CAC payback. These are not vanity metrics. They reveal whether the system is improving, static, or in decline. And they give CFOs a real basis for determining whether AI is a value center or an experimental cost.

Building the Operating Layer: From Tools to Systems

The shift from pilot to platform requires not just governance, but architecture. AI becomes sustainable only when it is connected to the enterprise fabric—not just as a bolt-on, but as a layer that communicates with source systems, human workflows, and compliance logic.

This is where many startups falter. They deploy AI agents into Slack or Notion or Chrome extensions, expecting consistent ROI. But these interfaces often lack context integrity. The agent can’t see the full data pipeline, can’t infer user intent, and can’t enforce policy constraints. What’s needed is a middle layer—a business logic interpreter that sits between the agent and the enterprise systems.

I’ve seen companies solve this elegantly by building or adopting thin orchestration layers that mediate agent access to ERP, CRM, and file repositories. These layers act as real-time data governors, ensuring that context windows are clean, document access is appropriate, and agent responses are scoped. Without this, agents hallucinate or leak. With it, agents become trustworthy copilots.

Boards should insist on understanding this middle layer. Not just because of technical curiosity, but because this layer determines enterprise reliability, compliance posture, and scaling potential. Without it, every agent becomes a liability.

Metrics that Matter: From Latency to Leverage

Founders must also develop a new muscle: tracking the right metrics. In the hype phase, AI metrics tend to skew toward usage (queries per day, prompts submitted) or output (tokens generated, summaries delivered). These are vanity indicators. In the post-hype discipline phase, what matters are business-adjacent metrics:

• Agent-accelerated decisions per week
• Human override rates on AI suggestions
• Cross-functional latency improvement (e.g., time to contract, time to variance explanation)
• Forecast deviation with vs. without agent participation

These metrics link AI effort directly to business velocity and accuracy. In one portfolio company I advised, the finance team tracked the difference in forecast error between human-only models and agent-augmented ones. Over two quarters, they showed a 23% reduction in error with fewer hours worked. That’s not a model stat. That’s a Board stat.

Similarly, legal teams deploying contract-drafting agents should track negotiation cycle times, deviation from playbook language, and escalation frequency. These data points justify or invalidate the investment—without emotional bias.

Boards should push for this discipline. If AI is real, it must show up in the way work flows, decisions accelerate, and errors decrease. If not, it’s theater.

When to Pause, Pivot, or Sunset

Not every AI deployment survives the discipline phase. And that’s okay. What separates high-functioning companies from laggards is the willingness to sunset or re-scope with clarity and speed. Here’s how I typically advise founders to make the call:

Pause if: Feedback loops are inconsistent, but model behavior is improving. Often seen in early-stage agent deployments where users are just beginning to trust the tool. A pause allows retraining, not abandonment.

Pivot if: The initial use case was poorly scoped. For example, trying to use GenAI for precision forecasting instead of variance commentary. Reframe the problem, redefine the agent’s role, and relaunch.

Sunset if: Agent output creates more errors than it resolves, user adoption is stagnant, and system overhead (data, compute, support) exceeds downstream value. At this point, it’s likely the organization wasn’t ready—or the problem didn’t need solving by AI.

In my own work across organizations, I’ve had to sunset several early AI initiatives. One involved a GenAI agent designed to produce investor FAQs ahead of board meetings. In theory, it saved time. In practice, it hallucinated sources, over-summarized key metrics, and couldn’t interpret footnotes. We pulled the plug and shifted instead to human-drafted prompts with GenAI summaries reviewed by FP&A. The model became a helper, not the author.

Boards should encourage these hard calls. AI, like any other capability, must compete for capital. If it’s not earning its place, the answer is not more investment—it’s refactoring or removal.

Partnering vs. Building Redux: Round Two Decisions

As AI transitions into the post-hype phase, the initial decision of whether to build or partner often resurfaces. Early partnerships may prove inflexible. Internal builds may lack resilience. This is a good time to re-audit the original AI footprint.

Partner again if:

• The original build lacks core infrastructure
• Market solutions have matured with robust APIs
• You want to reduce agent overhead and focus on orchestration

Build again if:

• Your data edge has improved
• You now have internal learning capacity
• You want model behavior tightly aligned with internal logic

Don’t let sunk cost bias drive your second-round decisions. The best companies treat every AI integration as a hypothesis—not a marriage.

A Word on Burn: AI’s Invisible Cost Structure

One under-discussed reality of AI post-hype is the cost structure it imposes—often invisibly. Fine-tuning models, retraining on updated data, prompt engineering, latency debugging, and compliance testing all consume human and compute resources. These are rarely visible on the P&L line item. They appear as productivity drag, scope creep, or operational noise.

Founders should work closely with their CFO to track and forecast these costs. Break down your AI cost stack:

• Vendor API usage
• Agent training hours
• QA or feedback loops
• Prompt library maintenance
• Incident response (for model drift or misbehavior)

This cost stack is essential to determining true ROI and should be updated quarterly. Boards must push for visibility here, especially as budgets tighten and AI’s shine begins to fade.

Closing Thought: Discipline is Not the End of Innovation

It’s tempting to view this post-hype period as deflationary. In reality, this is where real innovation happens. Discipline forces clarity. It eliminates cargo cult behavior. It pushes teams to define what’s real, what’s working, and what’s worth scaling.

In my view, this is the most exciting phase of the hype cycle. It is when talent crystallizes, systems stabilize, and strategies evolve. Companies that navigate this phase well emerge with an AI advantage that is operationally real, not just visually compelling.

As I often tell founders and Boards alike: It’s not enough to ride the hype wave. You must row through the trough.

Part III: Compounding Through Intelligence — Turning AI into Strategic Differentiation

The first wave of AI integration often looks like a controlled experiment. Success is defined by minimal viable products, isolated pilots, and narrowly scoped wins. But once the hype has faded and the initial deployments mature, the companies that truly capitalize on the AI opportunity are not those that build the most features. They are the ones who build the most resilient learning loops, refine intelligence at scale, and anchor AI into the core of their strategic model.

This final part focuses on compounding—how boards and founders should now think beyond tools and tactics to build defensibility and strategic advantage with AI. At its core, this is a question of design. Are you building a business that gets smarter over time? And is AI simply assisting your operations—or actually becoming a force multiplier in how you model, plan, and compete?

In over three decades of building financial systems, managing strategic capital, and implementing analytics platforms across verticals—from logistics and SaaS to AdTech and healthcare—I’ve observed that true business intelligence comes not from tooling, but from embedding decision-making frameworks deeply into the fabric of how companies learn. That same thinking now needs to guide the adoption of AI agents.

AI as an Asset, Not a Tool

Treating AI as capital rather than code means measuring its returns like any other asset on the balance sheet. In a past life as a CFO and consultant across venture-backed firms, I encountered the same story repeatedly: new technology gets purchased to solve old problems, but it is not maintained, trained, or governed. The systems decay, the workflows splinter, and the investment becomes shelfware.

This pattern is deadly with AI because agents degrade without structured input. They become less accurate, less aligned, and more fragile. The antidote is to treat your AI infrastructure as amortizable intellectual capital. It must be nurtured, updated, retrained—and, most critically, aligned to strategy.

From a financial perspective, we are entering an era where AI systems should sit alongside human capital and R&D as long-term assets. The ability of your AI systems to generate decision-ready insights, to reduce reaction time, and to model uncertainty becomes a strategic capability. That means you must evaluate:

• Knowledge accumulation rate: Are your agents learning faster than your competitors’?
• Organizational dependency: How many decisions rely on AI-augmented pathways?
• Model attribution: Can you link AI recommendations to outcome improvements—higher margins, faster conversions, reduced working capital?

Boards need to start asking for these analytics, not just usage dashboards.

From Linear Workflows to Nonlinear Agents

Having architected multiple business models and analytics backbones over the years, one thing I consistently preach is the danger of linearity. Most companies treat their processes as linear workflows: input X goes through steps Y and Z to produce output. But AI doesn’t think linearly. It reasons probabilistically. It maps relationships. It generates hypotheses.

Organizations that cling to linear thinking will find AI agents more disruptive than helpful. But those who embrace nonlinearity—especially in strategic planning, product iteration, and forecasting—will discover leverage. Consider:

• In revenue operations, agents don’t just predict pipeline conversion—they surface counterfactuals: what if marketing touchpoints changed?
• In procurement, agents do not just reorder materials—they optimize vendor portfolios based on cost, lead time, and ESG impact.
• In FP&A, agents go beyond variance analysis—they simulate fiscal cliffs and test budget elasticity under real-time stress conditions.

These are nonlinear advantages. They depend on orchestration, not execution. And they demand a shift in mindset: from building workflows to designing systems of learning.

Architecting the Intelligence Stack

In several of the companies I’ve advised or led, one thing has always distinguished the fast scalers from the laggards: their data architecture wasn’t just robust—it was usable by design. In the age of AI agents, this is mission critical.

The modern enterprise intelligence stack must include:

• A clean, governed semantic layer: agents need context, not just access.
• A feedback infrastructure: every agent decision must be reviewable and improvable.
• Clear lineage between data, models, and decisions: especially in regulated sectors like fintech, healthtech, or identity.

Without this foundation, AI becomes a black box. With it, AI becomes explainable, composable, and trustworthy.

I’ve seen this firsthand in revenue automation platforms, where the absence of context (such as one-time revenue adjustments or non-standard terms) led to bad recommendations. Once we layered agents with a semantic translator and business rules engine, both accuracy and adoption soared.

Talent Strategy: Training the AI-Adjacent Organization

Boards often assume that AI talent means hiring PhDs or ML engineers. In truth, the most critical roles emerging now are “AI-adjacent.” These are finance analysts who prompt well, product managers who understand token limits, controllers who can debug prompt drift, and legal teams who can red-team AI behavior.

My teams across finance, product, and operations have been strongest when cross-pollinated. I often paired data scientists with financial modelers, business analysts with compliance officers. The result was always the same: richer hypotheses, faster validation, fewer surprises.

As founders think about building AI-native companies, the org chart must evolve. Not everyone needs to build models—but everyone must learn to interrogate them.

The Board’s Role: Stewarding Strategic Intelligence

AI maturity is no longer a tech strategy. It is a board-level responsibility. Directors must now move beyond curiosity and into accountability. That means:

• Asking how AI is embedded into planning, not just execution.
• Requiring AI risk disclosures: data drift, prompt injection, security holes.
• Demanding clear audit trails of decisions influenced by agents.
• Enforcing oversight for hallucination risk, model degradation, and external data exposure.

Having worked with boards through M&A diligence, capital raises, and strategic turnarounds, I can say with certainty: AI now belongs in the audit committee. It is not just a toolset. It is a system of influence.

Boards must help shape the long-term strategy for AI governance just as they do for financial reporting and compliance. And they must ensure the company’s AI footprint is not just secure—but valuable, explainable, and adaptable.

AI Compounding: The Final Test of Differentiation

Ultimately, companies will not be valued on whether they use AI—but on how their AI improves over time. Compounding intelligence is the final differentiator. This requires:

• Feedback loops that shorten with scale.
• Models that fine-tune to proprietary data signals.
• Users who become co-teachers, not just consumers.

The companies that dominate the next era of business will be those who learn faster than others. Not just human learning—but organizational intelligence that accumulates, adapts, and anticipates.

I’ve had the privilege to witness this transformation in several industries. In AdTech, where agents learned audience response curves in days, not months. In logistics, where dispatch models adjusted dynamically to weather and labor constraints. In SaaS, where GTM agents optimized renewal language based on ICP nuance.

None of these wins came from simply deploying AI. They came from investing in intelligence loops. That is the new moat.

Final Word: Build with Conviction, Govern with Clarity

The AI hype cycle will continue. New models, new demos, new metaphors. But the companies that win will do one thing right: they will stop treating AI as magic and start treating it as a discipline. That discipline will look different depending on your stage, your sector, your size.

But the principles hold. Build what you can refine. Partner where you can’t scale. Wait when infrastructure lags. And above all: design for learning. Because in a world of agents, the companies who learn fastest win.

As someone who has spent thirty years building the scaffolding of great companies—from accounting systems and BI platforms to capital strategy and due diligence—I offer you this: AI is just the latest ingredient. Your culture of precision, iteration, and strategic patience is the real differentiator.

The future isn’t just automated. It’s intelligent. Build accordingly.