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Tag: ExecutiveLeadership

  • AI Revenue Is Growing Rapidly. So Why Isn’t the Entire Business Growing as Fast?

    Fast growth in an AI category can coexist with modest company-wide growth. The difference between those two measures matters.

    Infosys’s October 23 results offer a useful case study in how AI growth can appear inside a larger business. The company said AI revenue represented 10.5% of quarterly revenue and grew 29% quarter over quarter. Total revenue, measured in constant currency, grew 1.2% sequentially. Infosys also lowered its FY27 revenue growth guidance to 1%–2%, from 1.5%–3%.

    Those figures do not show that AI caused the slower overall growth. They do show why executives need to look beyond growth within a new product or service category and ask whether it is adding to the company’s total economic value.

    Category growth and company growth measure different things

    A business can build a fast-growing AI practice while its overall revenue grows slowly. That can happen when the new activity is only part of the business, when other areas are flat or declining, or when AI work replaces some existing services.

    The key distinction is whether AI revenue is incremental.

    If a client hires a provider for a new AI project that would not otherwise have existed, that may represent new demand. If the client shifts budget from a traditional service to an AI service, the provider may be changing the mix of its revenue without increasing the total. If AI helps the provider deliver existing work with fewer hours, productivity may rise even as labor-based billings come under pressure.

    These situations can look similar in an AI revenue headline. Their implications for growth and profitability are different.

    Productivity gains can change who captures the value

    Automation can help a services business complete work faster, reduce rework, or deliver more within the same timeframe. That may improve the provider’s cost structure. But if the contract is priced around hours or staffing levels, the client may also expect a lower price when fewer hours are needed.

    Reuters reported that Infosys cited pricing pressure and client requests to pass along AI-related productivity gains among the factors reflected in its revised guidance. That is useful context for understanding the business-model challenge. It does not mean AI alone explains the company’s overall results.

    When a provider saves time, the commercial question becomes: Who captures that productivity value?

    The provider may retain more of the savings through improved margins. The client may capture part of it through lower prices. Or the parties may agree on outcome-based pricing, where payment is tied more closely to results than to labor hours.

    Each approach changes the economics.

    What executives should measure

    AI revenue is worth tracking, but it should sit alongside measures that help explain its contribution to the whole business:

    • How much AI work comes from new customer demand versus existing budgets being redirected?
    • What revenue is won, retained, or replaced because of AI?
    • Does delivery productivity improve margins, reduce prices, or produce a mix of both?
    • Are customers buying additional outcomes, or receiving familiar outcomes through a new delivery method?
    • How are contracts and pricing adapting as the labor required for delivery changes?

    These questions help separate growth in an AI category from net-new growth for the enterprise.

    The executive question

    Infosys’s results are a reminder that a growing AI line item and modest company-wide growth can exist at the same time. That relationship deserves attention, but it is not proof of a single cause.

    The more useful question is whether AI is creating genuinely incremental value—or changing how existing value is delivered, priced, and shared.

    For an entrepreneur, that distinction is practical: Can AI make the business more productive while reducing revenue from the services that built it?

    The answer depends on what customers are paying for, how the work is priced, and whether the business can turn its productivity gains into new demand or stronger economics.

  • AI Infrastructure Is Moving From Maximum Capacity to Maximum Certainty

    The AI infrastructure conversation has been dominated by scale: more data centers, more generation, more chips, more gigawatts.

    Scale is still essential. AI workloads need enormous amounts of compute and power. But a large capacity announcement does not, by itself, mean a project can be financed, built, connected, and operated on a dependable schedule.

    That gap is moving to the center of the conversation.

    A project’s real value depends on several linked conditions: a site that can be developed, power that can be delivered, equipment that can be procured, financing that can support the timeline, permits that can be secured, and a community willing to live with the project. If one critical dependency remains uncertain, the announced capacity may be far from usable compute.

    The signals are converging

    Recent developments point toward a more coordinated approach to AI infrastructure.

    In the United States, utility and regulatory processes are placing greater emphasis on whether large-load projects are financially ready and accountable for the infrastructure costs they create. Texas has adopted financial security requirements for large-load interconnection requests, while federal regulators have also focused on cost recovery and protections for existing customers.

    In Japan, a project involving JERA, Dell, and RHAELM brings together power capabilities, a site, data-center development, financing, and AI infrastructure. The project illustrates the appeal of planning these components as an integrated deployment rather than treating them as separate problems to solve one by one.

    Developers are also pursuing onsite generation. Recent reporting describes growing interest in smaller gas turbines and other behind-the-meter options, partly in response to equipment lead times and the challenge of securing grid connections. Onsite power may offer a different schedule path, but it does not erase the trade-offs: fuel supply, permitting, emissions, cost, and the eventual relationship with the grid still matter.

    Community relationships are becoming a more visible part of the infrastructure strategy as well. AWS recently announced a multiyear investment in programs for communities near its U.S. data centers. These commitments do not guarantee public support, but they show that local impact and engagement are now part of the deployment conversation.

    From announced capacity to predictable dependencies

    These examples are different, but they point in the same direction: the challenge is no longer just assembling enough capacity. It is coordinating the conditions that allow capacity to become operational.

    That means executives may need to ask a broader set of questions:

    • Is the power timeline credible, and what assumptions does it depend on?
    • Who pays for the upgrades needed to serve the load?
    • Are land, permits, equipment, and financing progressing on compatible schedules?
    • If grid power is delayed, what alternatives are available—and what new risks do they create?
    • How are local costs, benefits, and concerns being addressed?

    These questions do not replace the traditional focus on capacity. They make the capacity claim more useful by connecting it to execution.

    Certainty as a competitive advantage

    “Certainty” here does not mean that every project outcome is guaranteed. Large infrastructure projects will always involve uncertainty.

    It means that fewer critical dependencies are left unresolved, that assumptions are visible, and that decision-makers have a credible plan for what happens when conditions change.

    As AI infrastructure scales, the strongest projects may not be the ones with the largest headline number. They may be the ones that can show how power, capital, land, equipment, compute, and community considerations fit together—and how the project can move from announcement to operation.

    The optimization target is shifting from “How many gigawatts can we announce?” to “How many dependencies can we make predictable?”

    In AI infrastructure, certainty is becoming part of the product.