Barney Krishnan is a Data Executive at UniCredit with expertise in financial services, digital banking, AI and data modernization platforms.
Historically, IT organizations have treated business initiatives as large-scale technology builds—either stand-alone projects with direct ROI (e.g., a mobile-only application that fast-tracks the onboarding of credit card users) or shared platform capabilities (e.g., a customer onboarding platform that can assist customers in creating accounts across all products/channels), compounding value across multiple domains.
Financial planning, both companywide and in the IT department, has traditionally focused narrowly on upfront costs, overlooking the deeper levers of CapEx/OpEx structuring, tax incentives, time-to-market economics and emerging AI usage models. Platform strategies—delivering common IT capabilities at scale with efficiency by a team equipped with the necessary tools, skills and governance—introduced complexity, acting as force multipliers for agility and innovation but also exposing hidden challenges like legacy debt accumulation, FinOps visibility gaps and variable AI economics.
This tension sets the stage for modern transformation: moving from project accounting to product-centric, outcome-driven investment models that integrate financial structuring, modular architecture, strategic opportunities (such as M&As), tax credits and comprehensive risk governance to achieve sustainable competitive advantage.
From Projects To Outcomes
Enterprises are accelerating the shift from a project to a product mindset, redefining technology investments as continuously evolving capabilities rather than fixed deliverables with defined endpoints. For example, a project mindset treats software as a one-time deliverable with ongoing support costs, whereas a product mindset treats it as a long-lived asset that can be continuously improved and more favorably capitalized.
This fundamentally transforms funding models—ongoing enhancements, automation pipelines and AI model retraining increasingly qualify as capital investments with extended amortization periods, rather than recurring operational expenses.
Financial Structuring As Strategic Leverage
Beyond optimizing raw compute and resource costs, forward-thinking companies establish financial strategies that strategically leverage government incentives, tax credits and ESG-linked benefits to amplify returns.
This type of financial structuring can transform IT investments from mere cost centers into economic engines. Key financial levers include:
• CapEx/OpEx optimization through tailored depreciation schedules
• Regional incentives and R&D tax credits
• ESG-linked funding for sustainable technology initiatives
• Extended amortization for reusable platforms versus short-cycle project builds
For example, green computing initiatives and workforce development programs often secure both immediate tax advantages and long-term reputational benefits, delivering compounding value that extends far beyond traditional P&L metrics.
FinOps + Modular Architecture = Value Partner
Modern architectures such as APIs, microservices and data products have long enabled granular deployment. Yet, financial operations (FinOps) haven’t fully realized their potential in tracking and attributing them to outcomes.
FinOps maturity can help deliver real-time visibility into consumption, eliminating arbitrary top-down allocations. Paired with modular design, it enables precise cost-to-value attribution, directly linking spend to specific business services and outcomes. One example of this is enterprise data platforms measuring workloads at a business function level.
This transforms IT from an opaque cost center to a transparent value partner, with a clear line of sight connecting consumption patterns, business results and targeted reinvestment opportunities.
Time To Market: An Economic Driver
Prioritizing speed over perfection can create tangible benefits like earlier revenue realization combined with intangible advantages like first-mover positioning and optimal market timing.
Carefully modeling time-to-market economics separates immediate execution from ongoing optimization, helping ensure that IT teams prioritize what truly matters: delivering business value before polished perfection. An example of this is launching a peer-to-peer payment solution to avoid attrition from merchants having to pay huge commissions to payment networks.
Legacy Systems As Quantifiable Debt
Legacy dependency creates compounding costs that hide beneath short-term timeline pressures.
Beyond the obvious drags—slower delivery, higher support costs, integration complexity, stifled innovation and AI adoption barriers—legacy systems accrue exit costs that grow exponentially with each dependent, incremental service. For example, adding incremental solutions to maintain the legacy mainframe will make the eventual transition from that mainframe costlier, more complex and riskier. Treat legacy solutions as high-interest debt: Each delay compounds future cost, so make the remediation burden visible and choose modernization over deferral.
AI Agents: The New Economics Challenge
AI costs fundamentally differ from traditional software economics. Fixed or seat-based tools are characterized by predictable monthly spend and team-level allocation amortized over licenses. AI agents, on the other hand, operate on usage-based models driven by tokens, compute cycles and documents processed, creating highly variable costs that demand real-time attribution and cross-functional cost sharing.
“Bad AI” economics demand new governance. Expected losses from errors in regulated domains (e.g., credit, underwriting and compliance) can dwarf deployment costs.
Monitoring, explainability and rollback capabilities become essential investment criteria rather than optional overhead, as they help companies avoid high audit and regulatory costs.
M&As: Strategic Opportunities
Large transformation initiatives such as core modernization, API layers and data platforms are becoming harder and harder to fund, either due to cost or to stakeholders’ impatience as they wait to see the end results. In that regard, mergers and acquisitions force enterprise transformation—next-gen platform establishment and standardized integration models that reduce M&A costs and risks.
From a financial perspective, these rare opportunities must be leveraged to support bold initiatives. Transformation investments become justifiable when framed as building reusable foundations that deliver compounding value across multiple deals. For example, a company could buy niche SaaS firms and build a shared integration platform. This may be costly at first, but each new deal will be integrated faster and cheaper as the cost is spread across multiple acquisitions.
This isn’t to discount the complexity of combining a merger with a transformation initiative, but the benefits of the acquired advanced technology would ideally offset the challenge.
In Summary
Modern IT leadership demands integrated mastery of technical excellence, financial discipline, business alignment and risk governance.
Forward-thinking enterprises fund outcomes rather than outputs, tracking adoption and consumption metrics alongside unit economics like cost to serve and cost per decision, as well as business impact through customer metrics and revenue acceleration. Organizations embracing product-centric funding, real-time economics, modular architecture and outcome ownership can build technology foundations that deliver compounding returns for decades.
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