AI Value Creation: Build Systems Around What Actually Moves the Business

AI value creation is not the same as doing more work with AI. That distinction is becoming increasingly important as AI compresses the time required to build, market, analyze, and deliver products. Faster execution can create remarkable leverage, but it can also accelerate activity that was never particularly valuable in the first place. In the second part of Rob Broadhead's conversation with Scott Shagory, that tension becomes a central issue. AI is changing delivery, sales, marketing, engineering, product ownership, and financial planning simultaneously. When everything can move faster, leaders face a more difficult question than "Where can we use AI?" They must determine where value is actually created.

Speed becomes leverage only when the work being accelerated contributes to an outcome that matters.

About Scott Shagory

Scott Shagory is the founder and CEO of the Purple Finch Group, where he helps technology CEOs navigate growth when organizational complexity begins to outpace the clarity that originally drove the business. His work focuses on identifying underlying growth constraints, clarifying where organizations create value, making implicit founder knowledge visible, and helping leaders adapt their strategy and operations as technologies such as AI reshape the business environment. Outside his business work, Shagory is a senior martial arts instructor, an experience that complements his focus on teaching and breaking complex ideas into understandable frameworks.

AI Value Creation Begins With the Value Chain

Shagory points to delivery, sales, and marketing as areas experiencing significant disruption. Roles are morphing and overlapping, and responsibilities that once seemed clear are becoming harder to define. What does it mean to be an engineer when AI can accelerate portions of development? Where does product ownership begin and engineering end? What should sales and marketing teams prioritize when content, research, outreach, and analysis can all be accelerated? These questions become more difficult because organizations are not simply introducing a new tool. They are changing the speed and boundaries of work across multiple functions at once.

The natural response to that pressure is often to do more, but that is precisely where trouble begins. Shagory describes reacting as "the lowest form of action." When organizations feel pressure to keep up, they can launch projects without deciding what trade-offs those projects represent. Every AI initiative consumes something, whether that resource is money, attention, management capacity, employee energy, or opportunity. The systems question is therefore not simply, "Can AI perform this activity?" Instead, leaders need to ask where value is being created and whether accelerating a particular activity strengthens that value.

AI Value Creation Requires Clear Trade-Offs

The early AI rush encouraged experimentation at enormous speed. Teams pushed products, consumed tokens, worked weekends, and pursued the possibilities created by rapidly changing technology. Shagory describes attending conferences where leaders talked about pushing their teams to produce more and "token max," yet he rarely heard them clearly articulate what the company intended to do and, equally important, what it had deliberately decided not to do.

That second decision is where strategy becomes important. AI expands the number of things a company can attempt, but it does not expand attention, capital, leadership capacity, or customer demand at the same rate. Prioritization therefore becomes more important, not less. A company might use AI for generation, categorization, development, customer analysis, or internal automation. Each application may be technically possible, but technical possibility does not mean each one deserves investment.

For every proposed AI initiative, identify the customer or business outcome it should improve and what the organization is willing to deprioritize in exchange.

This approach changes AI adoption from experimentation for its own sake into a portfolio of deliberate business bets. It also forces leaders to acknowledge opportunity cost. A team spending months developing one AI capability is choosing not to direct those same people, resources, and attention somewhere else. Faster development does not eliminate that trade-off.

AI Value Creation Exposes Busy Work

Broadhead raises an organizational problem that predates AI: people can work extremely hard without moving customer or company value forward. Employees may be busy every day, completing tasks and generating output, while producing little that materially changes an outcome. AI makes this problem more visible because it dramatically increases the amount of output a team can generate.

A team can now produce more code, campaigns, prototypes, research, documents, and ideas in less time. Increasing the volume of work, however, does not prove that the organization has become more productive. It may simply mean that the organization is producing waste faster. Shagory therefore returns to what he calls the linchpins of value creation. Leaders need to understand what the organization does better than anyone else, which activities support that advantage, and where customers actually experience meaningful value. Once those elements become visible, teams have a clearer basis for deciding what deserves acceleration.

AI can make an unfocused organization look extraordinarily productive because output can rise long before anyone proves that outcomes have improved.

Without that clarity, teams can work at cross-purposes while every individual appears busy. Engineering can accelerate one direction while product moves toward another, and sales and marketing can generate more activity without improving the customer experience. AI does not automatically align those functions. In fact, increasing their speed can make misalignment more expensive.

From AI Hype to Organizational Discernment

The conversation also identifies an important change in how businesses are approaching AI. The initial phase was characterized by excitement and pressure to move. Organizations feared being left behind, and experimentation itself sometimes became evidence that a company was adapting. Shagory describes the beginning of the year as a period of "token maxing," when businesses were caught up in the excitement, power, and perceived magic of what the technology could accomplish.

He now sees signs of a transition toward greater discernment. Companies are beginning to confront harder questions about what their experiments actually produced, what customers gained, which initiatives deserve continued funding, and where automation may not be the right choice. That transition matters because moving quickly up the wrong ladder still leaves the company in the wrong place. As Shagory observes, "Everyone's chasing or moving up a ladder, but it's not necessarily the right ladder."

AI may allow an organization to unwind certain mistakes faster than before, but speed does not restore the months, attention, money, and employee energy consumed by a poorly chosen initiative. Shagory also points to the human cost of aggressive experimentation, describing teams working seven days a week as they attempted to keep pace with rapid technological change. That effort can eventually create exhaustion and burnout. For Shagory, employees remain an organization's most important resource, which means a strategy that accelerates technology while exhausting the people responsible for directing it is not a sustainable system.

AI Value Creation Must Show Up in the Financial Model

Eventually, the AI system reaches finance. As AI becomes embedded in products and operations, organizations need to understand its costs rather than treating the technology as an experimental budget with unlimited upside. Shagory discusses how companies are beginning to determine how inference costs should be measured. Leaders have to consider whether those costs should be understood at an aggregate level, individual product level, or product-channel level because each approach changes what they can see about the economics of the business.

These questions matter because unpredictable AI costs can undermine budgeting and forecasting. A product that appears attractive when AI usage is inexpensive can become considerably less attractive when inference grows with adoption. Shagory notes that even a substantial variance in inference costs can create serious problems for a company because finance leaders may no longer know how to forecast what a product or project will actually cost.

The underlying financial foundation matters just as much. Shagory describes AI as a spotlight that exposes areas where a business is weak, including fundamentals such as the general ledger and chart of accounts. Poor underlying data does not automatically become useful financial intelligence because an AI layer has been added. The business still needs coherent data if leadership expects technology to provide accurate insight.

The more sophisticated the AI system becomes, the more important seemingly boring foundations become. Coherent financial data, meaningful metrics, and clear cost attribution help leaders distinguish genuine growth from expensive activity.

Build the System Before You Accelerate It

AI creates a genuine opportunity because many established assumptions are being reset. Large companies and small founders alike are reconsidering how products are built, how work is organized, and what customers will pay for. Shagory sees that disruption as a potential leveling field because organizations of different sizes are facing many of the same fundamental questions. The advantage does not automatically belong to the company that uses the most AI. It can belong to the organization that develops greater clarity about where technology creates meaningful leverage.

Companies that understand what Shagory calls their "origin of genius," know where value is created, and can articulate their strategic trade-offs have something meaningful to accelerate. Companies without that clarity may simply move through confusion faster. The practical challenge is to connect the system from end to end: customer value informs strategy, strategy determines priorities, priorities shape AI investments, and those investments produce measurable costs and outcomes that inform the next decision. That creates a business system rather than a disconnected collection of AI experiments.

AI Value Creation Also Requires Leadership Leverage

Shagory's bonus recommendation provides a practical way for founders and executives to apply the same thinking personally: audit their time. He recommends recording where time is actually spent without immediately judging the results. A leader may intend to devote two hours to deep work and discover that the activity repeatedly consumes four or five hours. Another high-priority responsibility may continually receive less attention than expected because the executive is being pulled toward work that is interesting, familiar, or urgent.

The audit matters because Shagory describes a founder as the company's "very first professional investor." Money is not the founder's only investment; time and attention are capital as well. Looking carefully at how those resources are allocated can reveal the same kind of misalignment that an organization may discover when evaluating its AI investments. The question remains consistent: Where is the resource going, and is that where it creates the greatest leverage?

Conclusion: Faster Is Not the Same as Forward

The most important question in AI adoption is becoming less technical. Organizations already know that AI can produce extraordinary amounts of work. The harder challenge is deciding which work deserves to exist. AI value creation begins when leaders identify the few activities that genuinely move the business, make explicit trade-offs around them, measure their economic impact, and use technology to increase leverage where it matters. AI can accelerate the conveyor belt, but leadership still has to decide what belongs on it.

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Additional Resources

• Leveraging AI for Business: How Automation and AI Boost Efficiency and Growth