The Economics of Building vs. Buying Software in an AI-First World

For decades, enterprise technology strategy revolved around one familiar exercise: optimizing license spend. CIOs negotiated harder, CFOs pushed for consolidation, and procurement teams hunted for volume discounts. It worked—until it didn’t.

AI changes the nature of the question entirely.

Today, the real strategic dilemma isn’t how much you’re paying for software. It’s why you’re paying recurring licenses for intelligence at all—especially when that intelligence can increasingly be built, owned, and evolved faster with AI.

This is where the idea of zero-license enters the conversation. Zero-license doesn’t mean eliminating software licenses across the enterprise. It means reducing license dependency in layers where AI-driven custom capability is economically and strategically superior to per-seat SaaS.

At the CXO level, this represents a shift in mindset. AI moves value away from access to features and toward ownership of intelligence—the decision logic, workflows, and reasoning that truly differentiate one enterprise from another. It’s quickly becoming a defining choice in enterprise software strategy and enterprise AI strategy.

In this blog, we’ll discover:

• Why traditional SaaS economics break down in an AI-driven enterprise
• How to reframe cost from total cost of ownership to cost of capability
• Where a zero-license strategy makes economic sense across horizontals like BI, CRM, and BPM
• How the economics play out in regulated verticals such as banking, healthcare, and insurance
• And what governance, controls, and operating discipline are required to make this approach sustainable

Let’s dive in.

Why License-Based Software Economics Break in an AI-Driven Enterprise

SaaS economics were designed for a very specific world. That world assumed human users, predictable usage patterns, and relatively fixed workflows. Licenses made sense when value scaled linearly with people logging into screens. This is the logic behind most software licensing models.

AI breaks every one of those assumptions.

Enterprises are now leveraging digital workers—agents, copilots, and autonomous workflows—that don’t behave like humans at all. Their usage is bursty, non-linear, and continuous. Decisions are made in milliseconds, not business hours. Workflows adapt dynamically based on context and data.

The result is a structural mismatch.

Organizations increasingly find themselves paying per user for work that machines are doing. According to Gartner, by 2026, more than 80% of enterprises will have used generative AI APIs or models in production environments. Yet most commercial software pricing still assumes named users and static access rights.

That creates friction. AI value gets challenged by license boundaries. Costs scale with access rather than outcomes. And the more AI you deploy, the faster those economics deteriorate.

The core insight is simple and increasingly unavoidable:
AI scales with data, compute, and logic—not licenses.

Reframing the Cost Model: From TCO to Cost of Capability

Traditional ROI and TCO models struggle in this new reality. They’re still anchored to license fees, implementation costs, and annual support contracts. That lens misses the bigger picture.

A more useful frame for CXOs is the Cost of Capability.

Instead of asking, “What does this software cost us per year?” enterprises are starting to ask:

• What does it cost to build and evolve this capability?
• What is the ongoing run cost in cloud and AI compute?
• How fast can we change it when the business changes?
• How locked-in are we to a single vendor’s roadmap?
• How adaptable is this system over five or ten years?

This leads to a powerful comparison: the cost of renting intelligence versus the cost of owning it.

CFOs recognize the long-term cost curves immediately. CIOs see the architectural implications. CEOs understand the strategic differentiation at stake. Renting intelligence may feel cheaper in year one, but ownership compounds in ways licenses never do.

Zero-License Zones: Where Eliminating SaaS Licenses Makes Economic Sense

Not every system should go zero-license. But many should—especially where the economics are already misaligned with how work actually happens.

The strongest candidates are systems where value comes from reasoning, orchestration, and insight, not from the UI itself. These are typically platforms where enterprises pay recurring license fees for users and screens, even though a growing share of the work is done by automation and AI.

Enterprise BI and Analytics Platforms

Enterprise BI tools aren’t cheap once you move beyond basic reporting. In large deployments, pricing typically lands in the $30–70 per user per month range after premium features and capacity add-ons are factored in. For an organization with around 3,000 users, that adds up to roughly $1.1–2.5 million a year in BI subscription costs alone—before you account for implementation services or the underlying data platform.

Now compare that with a different approach. For enterprises already standardized on a cloud data platform, analytics doesn’t have to be something you license seat by seat. It can be something you build once and own.

Modern AI coding tools have changed the economics here in a very real way. Published studies show developers completing about 26% more work with AI assistants, and controlled experiments with tools like GitHub Copilot show tasks being completed up to 55% faster, depending on the use case. When you apply those gains to an enterprise analytics program, many organizations can reasonably model a one-time, AI-accelerated build in the high six-figure to low seven-figure range—roughly $0.85–1.2 million.

After that initial build, ongoing costs look very different from traditional BI licensing. Instead of scaling with the number of users, run and enhancement costs scale with data volumes, workloads, and actual usage.

In a representative 3,000-user scenario, the three-year economics start to diverge clearly:

* Scenario Illustration: This example considers 3,000 BI users with a blended license fee ranging from $30 to $70 per user per month for the buy model. It excludes service and data platform expenses. The build model involves an AI-powered custom analytics layer on an existing cloud data platform, costing roughly USD 0.85–1.2 million upfront. Annual run and enhancement costs are estimated at 20–30%, increasing with usage rather than user numbers. These figures are scenario-based estimates, not industry standards, and depict the relative cost trends over three years at scale.

CRM Front Ends

CRM front ends provide a clear example, especially those built on core systems like Salesforce or Dynamics. While the core CRM acts as the system of record, companies often pay per-user fees for tailored sales and service interfaces, dashboards, and experience layers. For large organizations, advanced editions usually cost in the low to mid hundreds of USD per user each month, amounting to low to mid thousands annually before any discounts.

By comparison, custom front ends powered by lightweight interfaces and AI copilots can be built on top of CRM platforms using modern AI coding tools. Multiple studies and GitHub’s own experiments show developers completing coding tasks roughly 20–55% faster when using AI coding assistants, depending on task and context.
Using that productivity gain, a custom CRM experience often falls into a high six-figure one-time build range (~$600k–1.2M), with ongoing cloud-centric support costs that scale with usage rather than headcount.

At scale (e.g., thousands of CRM users), this shifts total economics decisively versus recurring per-seat software licensing models over a three-year horizon, as shown in the following table.

*Illustrative scenario: Assumes 3,000 power users at an effective blended price of $70–125/user/month after discounts for the buy model and assumes 9–12 FTE‑months of engineering at enterprise rates plus 15–25% annual run/extend for the build model. Figures are scenario-based estimates, not industry averages, and are intended to illustrate relative three-year total cost dynamics in stable, high-volume operating environments.

Business Process Management Platforms

A similar story plays out in workflow and BPM platforms. Large-scale deployments of enterprise workflow tools frequently reach high six to low seven figures per year when user tiers, modules, and integrations are factored in. Yet many of these workflows are fundamentally about routing, rules, and exceptions—areas where AI-led orchestration performs well without a heavy UI footprint.

Enterprises replacing these layers with custom orchestration services typically invest USD 1–2M upfront, then reuse that logic across multiple functions without expanding license footprints.

The economics are straightforward.
You stop paying recurring fees for access.
You start investing in intelligence that compounds over time.

Vertical Economics: Where Zero-License Delivers the Highest ROI

The zero-license case becomes even clearer when viewed through an industry lens.

Banking and Financial Services

At large Tier‑1 banking and financial services institutions operating multiple decisioning and fraud platforms at high volumes, combined annual license and subscription spend can reach into the low‑single‑digit millions of USD, especially when advanced compliance and customization are included.

Owning these intelligence layers changes the equation. Custom‑built decision engines for Tier‑1 banks typically start around 1.5M USD and can run to several million for a full enterprise scope; a 1.5–3M USD upfront range is a realistic working assumption for a large‑bank build, with higher outlays for global, multi‑line deployments.

Here’s how the costs could work materially out in a three-year period.

* Illustrative scenario: A large bank uses multiple licensed platforms for AI-driven credit decisioning, collections, and fraud detection, with combined annual fees in the low single-digit millions of USD. It also has a custom build program costing approximately 1.5–3 million USD upfront, with ongoing expenses of about 25–30% of the initial build annually for maintenance, model updates, and operations. These figures are scenario-based estimates, not industry averages, and are intended to demonstrate the relative total cost over three years in stable, high-volume environments.

Healthcare and Life Sciences

Life sciences companies and major healthcare providers often license tools for care pathway orchestration, clinical trial monitoring, patient engagement, and safety surveillance. These licenses are usually structured per study, per site, or per user. When organizations operate at scale—such as in global trials, multi-country studies, or across extensive provider networks—the annual costs for licenses and subscriptions can escalate rapidly.

In many cases, these expenses reach into the low millions of US dollars each year, especially if advanced analytics, compliance modules, or custom features are needed. However, the financial landscape shifts when organizations own the intelligence layer. Instead of depending on a patchwork of point solutions for monitoring, workflow management, alerts, and analytics, they can develop custom orchestration and intelligence layers that sit atop their existing EMRs and clinical systems.

This approach allows regulated platforms like EMRs and CTMS tools to remain as the stable backbone, while the new layer delivers flexibility and innovation. Building such a custom layer typically requires a significant upfront investment, often ranging from high six figures to low seven figures. For large healthcare systems or life sciences firms, budgeting between $800,000 and $2 million is a reasonable estimate for an enterprise-grade solution. This investment covers care pathway management, clinical trial visibility, analytics, and AI-powered alerts. Once the system is in place, ongoing costs are much lower. These recurring expenses scale with actual workload, data volume, and the frequency of model updates, rather than being tied to the number of users or sites.

In summary, while the initial outlay for a custom intelligence layer is substantial, it can lead to more predictable and scalable costs over time, especially for organizations managing complex, large-scale operations.

Here’s how the costs could work out materially in a three-year period.

* Illustrative scenario: Assumes a life sciences organization running multiple clinical trials or a large healthcare provider using licensed care orchestration, patient engagement, and analytics tools with recurring fees in the USD 1–3M range per year. The build scenario assumes a custom intelligence layer delivered on top of existing EMRs and clinical systems, with an upfront build of USD 0.8–2.0M and annual run costs of approximately 20–30% of the initial build for maintenance, compliance updates, analytics enhancements, and model tuning. Figures are scenario-based estimates, not industry averages, and are intended to illustrate relative three-year total cost dynamics in stable, high-volume operating environments.

Insurance

In Insurance, underwriting and claims platforms are often bought as large, bundled suites. For mid- to large-size carriers, it’s not unusual for the combined license, maintenance, and customization costs of these platforms to range from $1 to $ 4 million per year—especially when advanced rules, workflow customization, and regulatory support are included.

There’s another way to approach this. Many carriers are keeping their policy administration systems as the core system of record, while building custom underwriting intelligence and claims-triage layers around them. These layers handle decisioning, prioritization, automation, and insights—without replacing the core platform. In stable, high-volume environments, this model can translate into roughly 20–30% lower total cost of ownership over three years, while also making it easier to adapt as products evolve, regulations change, and risk profiles shift.

The 20–30% savings range isn’t an industry-wide average. It comes from straightforward scenario modeling. In a typical scenario, a mid- to large-sized carrier spends $1–4 million per year on licensed underwriting and claims suites, including vendor maintenance. The custom-intelligence path shifts much of that spend into a one-time build in the $1.5–3 million range, followed by ongoing run and enhancement costs of about 20–30% of the initial build per year, which is consistent with common enterprise software maintenance patterns.

When you compare three-year totals across these ranges, many realistic configurations show the custom path coming out 20–30% cheaper than a heavily licensed suite—particularly in high-usage, relatively stable operating environments where per-seat and per-transaction licensing costs tend to dominate.

* Illustrative scenario: Assumes a mid- to large-size insurance carrier using bundled, licensed platforms for underwriting decisioning and claims management, with combined license, maintenance, and customization costs in the USD 1–4M range per year. The build scenario assumes the carrier retains its policy administration system as the system of record, while implementing custom underwriting intelligence and claims-triage layers for decisioning, prioritization, automation, and insights. Upfront build costs are assumed at USD 1.5–3.0M, with ongoing annual run, enhancement, regulatory updates, and model-tuning costs of approximately 20–30% of the initial build. Figures are scenario-based estimates, not industry averages, and are intended to illustrate relative three-year total cost dynamics in stable, high-volume operating environments.

Across industries, the pattern is consistent.
Eliminate licenses where intelligence creates advantage.
Retain licenses where records must endure.

The Hybrid Model: Zero-License on Top, Licensed at the Core

For most enterprises, the future isn’t binary. It’s hybrid.

Licensed systems continue to anchor compliance, data integrity, and transactional consistency. On top of them sit zero-license intelligence layers that handle decisions, exceptions, orchestration, and AI reasoning.

Interfaces shift dramatically in this model. Screens give way to APIs and agents. Humans engage when judgment is required. Machines handle everything else.

This architecture delivers speed without sacrificing stability—and it aligns naturally with how AI actually works.

A Practical Decision Framework for CXOs

The buy-build decision doesn’t need to be philosophical. It needs to be repeatable.

For each system, leaders should ask a simple set of questions:

• Is this primarily a system of intelligence, not a system of record?
• Does AI materially improve outcomes here—speed, accuracy, or automation?
• Are we paying per user for work that is mostly done by machines?
• Does this capability need to change faster than vendor roadmaps allow?
• Would owning this logic differentiate us over the next few years?

If most of the answers are “yes,” the economics usually favor building.
You’re likely renting intelligence that could be owned, evolved, and scaled without compounding license costs.

If most of the answers are “no,” buying remains the right choice.
Stability, compliance, and standardization matter more than differentiation, and vendor platforms do the job well.

If the answers are mixed, a hybrid approach tends to deliver the best outcome.
Keep the core licensed, and build the intelligence and experience layers on top—where AI can create real leverage.

“We Have No Development Capability”: The Most Common Concern

This is the concern that stops many conversations before they start.

Zero-license does not mean becoming a software company. It doesn’t require massive engineering teams or unmanaged technical risk. What it means is owning decision logic, data flows, and intelligence layers that matter.

In practice, this shift is partner-led, not DIY. Enterprises define the outcomes they need. Partners design, build, and operate the intelligence responsibly.

Even in the height of the SaaS era, enterprises relied on partners for implementation, customization, integrations, and managed services. AI amplifies that dependence, not reduces it.

The partner role evolves—from system configurator to intelligence co-builder, from integration vendor to capability architect, from upgrade support to continuous AI evolution.

Service providers with AI expertise, like Hexaware, help enterprises build license-free AI platforms, custom decision systems, and enterprise-grade AI governance frameworks that accelerate time to value while lowering long-term cost.

There’s an economic truth here that CFOs understand immediately: services spend is finite; license spend compounds forever.

Risks, Controls, and Governance

Zero-license strategies only deliver value when they’re backed by discipline. Without the right controls, faster building can lead to fragmented architectures, shadow IT, and AI systems that are hard to govern or explain. The organizations that succeed take a platform-first approach—standardizing how services, data, and models are built and secured—while pairing it with strong partner-led delivery, centralized data and security controls, and clear ownership for every intelligence layer. Done right, zero-license isn’t about cutting corners. It’s about shifting from rented software to owned capability, with governance that scales as fast as innovation does.

Conclusion: Zero-License Is Not Cost-Cutting. It Is Strategic Control.

Zero-license is often misunderstood as a procurement tactic. It isn’t. At its core, it’s a strategic decision about where enterprises want to own capability versus rent it. As AI compresses development cycles and shifts value away from UI-heavy software, the economics of buying versus building are changing—especially at scale. For analytics, decisioning, orchestration, and experience layers, recurring per-seat licenses increasingly fund features enterprises don’t need, at a pace they don’t control.

Zero-license strategies flip that model. They concentrate investment into owned intelligence, designed around the business, and governed as a product—not a subscription. The result isn’t just lower long-term cost. It’s faster iteration, clearer accountability, and the ability to adapt as markets, regulations, and customer expectations change.

This doesn’t mean abandoning platforms. Systems of record still matter. But the future belongs to enterprises that buy the core, build the intelligence, and control the experience. That’s not cost-cutting. That’s control.

The strategic risk in an AI-first economy isn’t building software. It’s renting intelligence indefinitely.