Balancing Legacy and Innovation: The Budget Battleground in IT Planning

With constrained budgets, many IT leaders face a trade-off between maintaining existing systems and funding exploratory projects like edge computing, observability, and digital process reengineering. What frameworks or guardrails can technology executives use to manage that balance?
Dec. 7, 2025
6 min read

Key Highlights

  • Treat legacy as a strategic asset, not dead weight: Instead of defaulting to rip and replace, quantify the full cost of integration, workarounds, and process friction, then modernize by connecting what already works.
  • Make AI a data-first, board-level decision: AI should align with enterprise priorities, regulation, and customer outcomes, but it only creates value if you've invested in clean, governed and integrated data.
  • Redefine your ROI: Evaluate initiatives across operational efficiency, revenue and margin impact, risk reduction, employee effort, and time-to-benefit.
  • Win the cultural battle with investor-style discipline: Treat your roadmap like an investment fund and pair that with clear change narratives, co-ownership with the business, and empowerment so teams view modernization as an enabler, not a threat.
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Across every industry, CIOs and technology leaders are being asked to deliver the impossible: modernize decades-old systems, accelerate AI adoption, enhance the customer experience, strengthen cybersecurity, and reduce operational costs — all without expanding the budget. For many, the hardest part isn’t understanding what must be done. It’s knowing where to place the next dollar when both legacy stability and future innovation feel non-negotiable.

In conversation with Bala Thiru, a technology leader currently overseeing transformation, AI strategy, and enterprise architecture at American Airlines, one theme became clear: The battleground isn’t technology; it’s prioritization. And the organizations that win will be those that treat budget planning as both an analytical and a cultural exercise.

Legacy as a strategic asset, not a liability

Technology Executives often face pressure to “modernize the legacy,” a phrase that often implies wholesale replacement. But as Bala explains, legacy systems are more than outdated code or hardware. They are “decades of encoded business logic, customer nuance, regulatory insight, and operational workflows.” In the aviation environment Bala operates in, some of the core systems are more than 70 years old, yet they help orchestrate more than 3,000 flights a day with extraordinary reliability.

“Legacy is heritage,” he emphasizes. “Not a hindrance.”

The real challenge isn’t old technology; it’s the friction created when legacy must interact with dozens of newer systems. Integration overhead, manual reconciliation, and brittle connections often create hidden costs that go unmeasured. When Bala’s team conducted a data-driven total cost of ownership analysis, they discovered that only 50% of cost was technical. The rest came from process inefficiencies and workarounds that no one had previously quantified.

The takeaway for technology executives? Don’t assume legacy is the problem. Understand what it enables, what it constrains, and what value it still holds. Bala believes that modernization should focus on “containerize, complement, and connect” — not “rip and replace.”

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AI as a board-level imperative

While legacy demands protection, artificial intelligence (AI) demands acceleration. At Bala’s organization, AI strategy isn’t an IT-led project — it’s driven by the CEO and the board. Competitive pressure is intense, customer expectations shift overnight, and AI has become the differentiator for net promoter scores (NPS), operational speed, resiliency, and customer experience.

But AI doesn’t sit on top of a weak foundation. It magnifies it.

“You can only imagine the complexity,” he says. “Now you add AI strategy to it.”
For airlines — and many heavily regulated industries — the challenge isn’t just scaling AI but ensuring its models are safe, explainable, and fully compliant. Data authenticity, IP protection, and guardrails around model development are essential.

AI investments must align with enterprise priorities, regulatory requirements, and customer outcomes — not departmental experiments.

The data foundation comes first

One insight Bala repeats: “The data foundation is critical for AI. Without it, AI is useless.”

This reframes the innovation roadmap. The smartest AI pilots will not generate a return on investment (ROI) if the underlying data is fragmented, inconsistent, or poorly governed. Investments in data quality, lineage, integration, and architecture often deliver the highest long-term return, yet they are rarely the most visible or exciting line items.

For technology executives fighting for budget, Bala suggests positioning data as an accelerator of both legacy efficiency and future innovation. Investments in clean, orchestrated data help reduce operational friction today and unlock AI capabilities tomorrow.

A more realistic, multi-dimensional ROI

Many budget conversations hinge on the ROI, but Bala argues that leaders often measure only a fraction of the real value. When evaluating whether to invest in new projects or maintain legacy systems, technology executives should quantify multiple dimensions of ROI:

  • Operational Efficiency: What frictions or manual processes can be eliminated?
  • Revenue and Margin Impact: How will customer experience or reliability improve?
  • Risk Reduction: Cybersecurity, resilience, regulatory compliance.
  • Employee Cost: How many cycles are consumed by outdated workflows?
  • Time to Benefit: How quickly will outcomes be realized?

This expanded view allows technology executives to capture "value reclamation" — the small but cumulative improvements that come from modernizing workflows, not just systems. These “small pebbles” often add up to more measurable impact than the big rocks alone.

 

The mistake many organizations make is confusing "grow" with "transform." They assume extending existing systems is the same as innovation. It’s not.

Run, Grow, Transform: A framework for clarity

Bala categorizes every technology investment into one of three buckets:

  • Run: Maintain and stabilize core systems to keep the business operational. Reduce footprint through tech-debt retirement and automation.
  • Grow: Extend capabilities — cloud adoption, data integration, and enhancements that improve efficiency and preparedness.
  • Transform: Build new capabilities such as AI, edge computing, digital workflows, and next-gen business models.

The mistake many organizations make is confusing "grow" with "transform." They assume extending existing systems is the same as innovation. It’s not. And when these categories blur, budgets bloat, timelines slip, and transformation loses credibility.

Bala’s advice: Apply discipline. Define buckets clearly. Fund each intentionally.

Think like an investor, not a technologist

Perhaps the most powerful metaphor Bala offers is this: “Treat your portfolio like an investment fund, not a backlog.”

This means rebalancing quarterly, not annually. Doubling down on initiatives with proven value. Cutting or redesigning projects with unclear ROI. Making modernization self-funding by redirecting efficiency savings into innovation. Momentum earns political capital. Delivery earns trust. And trust earns future budget.

Culture: The invisible battleground

For all the technical challenges, Bala says the biggest hurdle is not architecture or AI. It’s culture.

Change management is the single largest barrier to modernization. Employees worry that automation will erase jobs. Technology teams optimize for “tech elegance,” while business units prioritize measurable outcomes. Leaders struggle to articulate the narrative behind modernization in a way that feels meaningful, not threatening.

Success requires clear, consistent communication about why modernization matters. Co-ownership across IT and business stakeholders. Transparency around decisions, priorities, and outcomes. And the reassurance that innovation is not a threat, but an extension of human capability.

“Empowerment,” Bala says, “is the key.”

Pragmatism, persistence, and measurable wins

After decades leading programs across global enterprises, Bala distills his advice for fellow technology executives into a simple formula:

Listen. Assess. Be pragmatic.
Iterate. Be persistent.

Big slogans and sweeping transformation promises rarely survive contact with reality. Instead, organizations win by delivering measurable wins, building trust across teams, and maintaining clarity about what matters most.

In a world where budgets will likely remain constrained, the leaders who succeed won’t be the ones who choose between legacy and innovation. They will be the ones who learn to orchestrate both. Leveraging heritage while advancing toward the horizon.

About the Author

Jess Mand

Jess Mand

Contributor

Jess Mand is an award-winning communications strategist and founder of INDEMAND Communications, where she helps organizations translate complex ideas into clear, compelling narratives that drive connection and action. She partners with Fortune 500 companies, growth-stage firms, and mission-driven organizations to design communication strategies, content programs, and experiential campaigns that engage employees and elevate leadership messages. Known for her creative storytelling and pragmatic approach, Jess brings a rare blend of strategic insight and human-centered perspective to every project she leads.

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