Technology Finance: A Guide to Sustaining AI’s Return on Investment

Conclusion

AI’s promised advantages rely not just on the technology strategy, but on governance velocity. This means executives and leaders should set an objective to control AI financial outcomes at the same speed as Cloud consumption and autonomous agent action.

The imperative for finance and business leaders is to move the discussion around AI spending from passive expenditure scrutiny to assertive value orchestration. Implementing a systemic approach, such as the TAONexus framework, can help ensure that every AI dollar is traceable, governed, understood, and linked to measurable strategic objectives.

This commitment underpins promised AI return on investment (ROI) and resilience.

The TAONexus (TBM-AI-OKR Nexus) framework¹ provides a platform-based approach to achieve timely visibility and control over AI costs and benefits. TAONexus² enables this control by combining the speed of FinOps telemetry (real-time data) with the rigour of technology business management (TBM) cost attribution, and the clarity of outcome-focused objectives and key results (OKRs).

Implementing this combination approach creates a dynamic control system where AI investment performance is continuously measured, AI value is quantified using unit economics, and scarce capital is proactively directed toward high-impact outcomes.

Observations

The Imperative for AI Financial Governance at the Right Speed

The central difficulty in governing AI investment is the timing mismatch between financial measurement speed (cadence) and technical (machine) speed. AI usage costs can fluctuate by the hour in multi-Cloud environments.

1. The Problem – Velocity Mismatch: Traditional TBM provides essential strategic context, including the standardised total cost of ownership (TCO) taxonomy. However, TBM’s traditional speed (monthly or quarterly) is too slow for volatile AI inference costs. FinOps provides the necessary granular, real-time consumption data, but lacks the strategic TCO context and outcome alignment of TBM and OKRs.
2. Blending Cadences: TAONexus addresses this timing challenge through proportional governance velocity i. e., speed and efficiency in decision-making and/or rule-making processes that is balanced to match the importance or risk of the issue. In essence, TAONexus enables the TBM TCO model to adopt the speed of FinOps data to capture consumption changes in near real-time, allowing the business, IT, and finance to intervene on cost issues before they become budget breaches.
3. Proportional Risk Governance: Governance must match the speed of autonomous action. Agentic AI systems operate independently, which increases the risk of unintended action or algorithmic bias. Effective AI governance excellence (AIGovX)³ demands financial safeguards against financial issues that could rapidly destroy ROI. This requires implementing technical controls (hard boundaries, immutable logging) and human oversight (human-in-the-loop checkpoints) that fit the AI system’s risk tier (low, medium, high).

Operationalising Accountability: The Power of AI Unit Economics

Effective AIGovX requires both rigorous input control and outcome measurement. Accountability must shift from only tracking the cost of inputs to also measuring the cost of outcomes i. e., instill a value driven culture.

1. Quantifying Value: Fusing FinOps and TBM data via TAONexus creates a solid platform for AI unit economics. This shifts the cost narrative from a total expense (e. g., “$100k was spent on GPU time”) to measurable cost-for-performance (e. g., “Cost per successful prediction was $0.02”). Unit economics make fragmented AI spending tangible, actionable, and directly accountable to the business owner.
2. Linking Cost to Strategic Outcome (OKRs): AI unit economics function as the key results (KRs) within a strategic OKRs framework. This ensures capital is aligned with aspirational business outcomes (the OKR mandate), not just optimised for efficiency (the FinOps goal).
3. The TAONexus Value Council (TVC): This model empowers a cross-functional TAONexus value council (C-suite, finance, technology, and business leadership) to intervene proactively. By continuously monitoring real-time dashboards showing AI unit economics against OKR progress, the TVC can quickly redirect resources to maximise strategic value realisation (SVR).

Sourcing as a Commercial Excellence Strategic Control Plane

In accelerated SVR cycles, sourcing risk severely undermines AI ROI.

1. The Need for Militant Contractual Flexibility: Traditional fixed-requirement contracts quickly fail due to rapid AI innovation. A contract with a relatively fixed scope is likely to be obsolete or suboptimal in the near- to medium-term as new models or techniques emerge. This creates a high risk of the contract locking the buyer into a solution that does not meet changed/emerging needs.
2. Shifting to Value Orchestration: Embedding commercial excellence (CommX) strategies in sourcing agreements⁴ helps to secure the buyer a fair share of the value created by AI initiatives and innovations that the buyer is paying for.
3. Performance-Based Contracting: To protect the ROI defined by OKRs, contracts must adopt outcome-linked payment models. A substantial portion of supplier fees should be contingent on the achievement of measurable buyer outcomes (KRs) that are established within the TAONexus construct. This requires both parties to share in the benefits, reflecting the supplier’s role in improving buyer outcomes (e. g., by the buyer gaining access to the supplier’s AI innovations). The goal is to properly assign financial risk to the supplier, which ultimately lowers the performance risk for the buyer.
4. Proactive Risk Mitigation: CommX also includes securing contractual mechanisms that protect proprietary value. KR’s can be leveraged to cement shared interests in protecting the buyer’s valuable IP by tying supplier payments and their opportunities to grow the account to their performance that advances the buyer’s OKRs.

Next Steps

TAONexus Implementation

Implementing TAONexus requires a structured, multi-phased approach to shift the organisation from reactive cost reporting to proactive value management.

Immediate Actions: Establishing Governance and Visibility

The initial focus must be on creating the necessary data foundation and setting executive governance structures.

1. Establish Executive AI Accountability and AIGovX Mandate:
   • Designate a single, non-delegable C-suite leader (e. g., CEO, COO, or CIO with CFO oversight) as the accountable owner for AI SVR.
   • Establish a cross-functional AI Governance Committee to approve AI deployments based on tiered risk categories (Low, Medium, High).
   • This committee defines the AIGovX guardrails (technical boundaries, human-in-the-loop checkpoints) to prevent catastrophic autonomous failure.
2. Mandate Unified Cost Attribution (TBM/FinOps Integration):
   • Balance a zero-tolerance policy for unallocated spend with speed and flexibility, e. g., implement a sandbox budget for R&D with lighter governance, reserving the zero-tolerance policy for production systems. Mandate the use of unified tagging aligned with the TBM taxonomy across all AI resource consumption.
   • Integrate FinOps tools with the TBM platform to automate mapping from granular Cloud bills to the standard TBM cost model.
   • Impact: Achieve near-complete attribution of volatile AI costs, eliminating the unknown/other category that causes financial distrust.
3. Define and Baseline AI Unit Economics:
   • Identify 3–5 critical business value streams accelerated by AI.
   • Define the initial set of AI Unit Economics (e. g., Cost per accurately routed ticket) and establish baseline targets (KRs) linked to strategic OKRs.
   • TAONexus Application: This shifts the organisation from tracking costs (inputs) to measuring value delivered (outputs), making investment decisions transparent.

Planning for Future Actions: Industrialisation and Orchestration

This work focuses on the actions needed to harden continuous feedback loops and scale the governance model.

1. Activate the TAONexus Value Orchestration Model:
   • Formalise the TAONexus Value Council’s operating cadence with weekly or bi-weekly meetings to review shared dashboards that show AI Unit Economics variance against OKR progress.
   • Empower the Council to make immediate, continuous, outcome-based resource reallocations.
   • Impact: This replaces traditional, siloed budgeting with a flexible resource-allocation model that directs capital toward value-generating activities.
2. Integrate Risk and Resilience Guardrails:
   • Implement continuous monitoring of critical AI systems under a model risk management (MRM) framework, tracking drift and the human override rate.
     • An MRM framework is the set of policies, procedures, and controls an organisation uses to identify, assess, manage, and monitor the risks associated with model use in its operations. For organisations using AI, ISO/IEC 42001:2023 for AI governance provides a management system framework for governing AI, including aspects of model risk.
   • Test operational resilience for all critical AI agents against the organisation’s governing board-approved disruption tolerances.
     • The requirement to test critical systems, including AI agents, against an organisation’s governing board-approved disruption tolerances is a key component of operational resilience regulations in highly regulated sectors like financial services. Other cross-industry voluntary standards, such as ISO 42001, provide a management system framework that facilitates compliance with regulations and helps organisations build AI governance and testing regimes.
   • Test critical AI agents’ operational resilience against the organisation’s governing board-approved disruption tolerances. While this testing is mandated by operational resilience regulations in sectors such as financial services, broader, cross-industry voluntary standards, such as ISO 42001, provide management system frameworks that facilitate compliance with these regulations and help organisations build the necessary governance and testing regimes.
   • TAONexus Application: These frameworks become non-negotiable guardrails, helping safeguard against autonomous speed that compromises organisational resilience, including the protection of liquidity as a core component of overall financial resilience.
3. Shift Workforce Capability via Augmentation:
   • Launch targeted training to equip staff with AI collaboration skills, focusing human capacity away from routine processing toward higher-order strategy tasks.
   • Define human-in-command roles for high-risk AI systems with the authority to intervene or shut down systems when AIGovX thresholds are breached.

Looking to the Horizon: Sustaining AI SVR and Competitive Advantage

This final phase cements the organisation’s long-term SVR through governance maturity and relentless commercial excellence.

1. Enforce CommX for Sourcing:
   • Implement performance-based contracting as the default model for acquiring AI products and services. Structure payment to be directly indexed to the achievement of defined AI unit economics (KRs).
   • Implement algorithmic escalation clauses that tie fees to performance standards (e. g., reduced fees if model accuracy falls below thresholds), providing a self-enforcing contract governance mechanism.
2. Achieve Real-Time Decision Superiority:
   • Mature the TAONexus model to integrate all strategic inputs, from real-time consumption signals (FinOps) to TCO context (TBM), and external commercial performance (CommX KPIs), into a single operational control plane.
   • Impact: This allows the c-suite to execute rapid, confident strategic pivots, using integrated data on cost, risk, and outcome faster than competitors.
3. Mandate Perpetual Governance Maturity:
   • Ensure the governing body continually evolves its competency regarding AIGovX and MRM.
   • Establish continuous improvement practices (like the FinOps maturity cycle) across the entire TAONexus platform.

Footnotes

1. See: ‘Strategic Value Realisation: AI Costs Management and Value Attribution’, IBRS, 2025.
2. FinOps is © The FinOps Foundation, 2025; Technology Business Management and TBM is © The TBM Council, 2025; OKR framework created by Andrew Grove at Intel, 1983, later formalised and popularised by John Doerr, 2018 – see ‘Set and achieve your goals with open source software for OKRs’; TAONexus, TBM-AI-OKR Nexus, TAONexus Value Council (TVC), and Strategic Value Realisation are © Barta Global Services, 2025.
3. See: ‘Agentic AI and Leadership’s Duty of Care: The Case for Excellence in AI Governance’, IBRS, 2025.
4. See: ‘B2B Value Capture in the Intangible Economy – Commercial Excellence in Procurement’, IBRS, 2025.