How TBM is evolving to power the AI era

The Technology Business Management (TBM) Council’s EMEA Summit in Amsterdam in late June delivered transformative announcements that signal a fundamental shift in how enterprises approach technology value management. With the launch of TBM Taxonomy 5.0, TBM Framework 2.0, and strategic partnerships expanding the discipline’s reach, the TBM Council is positioning TBM as the cornerstone for managing complex, AI-driven technology ecosystems. For CIOs and finance leaders grappling with questions about FinOps, GreenOps, and AI’s impact on technology governance, the answers are becoming clearer — and more strategic than ever. (See also: The 10 biggest issues facing CIOs today.)

TBM 5.0: A foundation for technology value

During the TBM Council’s Amsterdam summit, the TBM leadership team shared details on TBM Taxonomy 5.0 and Framework 2.0.  These aren’t incremental updates but a comprehensive progression of how organizations can model, measure, and manage technology investments in an era defined by artificial intelligence, hybrid cloud architectures, and distributed technology leadership. They explicitly model different AI categories (e.g., agentic, prescriptive, and predictive), based on practical input and practices from global TBM Council members.

The Framework 2.0 evolution uses new guiding principles that recognize organizational maturity levels. As Matthew Guarini, executive director of the TBM Council, explains: “TBM Framework 2.0 organizes the standards, value drivers, capabilities, and guiding principles organizations need at all levels of maturity, to succeed in identifying and achieving valued business outcomes.”

Adjacencies: FinOps, GreenOps, and Security

There are a variety of additional standards, frameworks, operating models, and technology advancements that impact or relate to TBM. During the summit, industry executives and practitioners addressed and presented case studies on the following:

• FinOps and TBM: FinOps and TBM have complementary strengths. FinOps excels at real-time cloud cost optimization, providing granular insights into cloud service provider spending and optimization opportunities. TBM provides the broader context, offering total-cost-of-ownership perspectives on cloud-related labor, SaaS, management tools, datacenters, and license spending. The key distinction lies in their operational focus. FinOps thrives on cloud’s variability, enabling real-time decisions about resource utilization, instance reservations, and vendor negotiations while TBM operates across all technology spend, at the speed that the rest of the tech stack moves — cloud computing, on-premises infrastructure, SaaS, labor, suppliers, and software licenses — focusing on strategic cost transparency and allocation across business capabilities.
• GreenOps: The summit also highlighted TBM’s expansion into sustainability through the GreenOps model — a development that reflects growing pressure for environmental accountability in technology decisions. Through the incorporation of GreenOps principles into TBM, organizations can manage environmental impact with the same discipline used to manage cost, performance, and value. 
• Cybersecurity: During the summit, the team and a global CISO speaker highlighted the deepening alignment of TBM and the FAIR (Factor Analysis of Information Risk) framework. This alignment addresses a critical gap that has long challenged CISOs: communicating cybersecurity risk and return in financial terms. TBM can be used to enhance FAIR cyber practices. As cybersecurity threats require the protection of the enterprise, CISOs, CIOs, and CFOs must demonstrate how cybersecurity supports resilience, innovation, and growth.

AI’s transformative impact

Artificial intelligence is fundamentally reshaping the landscape of technology leadership and financial management. As organizations accelerate AI adoption, leaders must navigate new complexities that challenge established practices. The following points highlight how AI is driving a shift in budgeting, organizational structure, and governance, requiring a rethinking of traditional approaches to remain competitive and effective.

• Traditional budgeting models will no longer suffice: For finance teams, AI introduces complexity in several dimensions. Traditional budgeting models struggle with AI’s variable consumption patterns, like early cloud adoption challenges but with greater unpredictability. AI investments often span multiple organizational boundaries — from infrastructure costs for GPU computing to specialized labor for data science teams to licensing fees for AI platforms and services.
• AI impacts the entire organization: The organizational impact extends beyond pure financial management. For example, as Guarini notes, “We’re seeing a fundamental shift where CISOs often don’t report to the CIO anymore and has access to dedicated AI cybersecurity funding.” This creates budget fragmentation where AI cybersecurity costs exist in a separate financial silo, making it more challenging to achieve the core TBM objective of providing comprehensive visibility into total technology spending and its business value.    Additionally, Guarini states, “There’s significant technology spend under AI officers and digital officers operating independently.” This dispersed technology leadership requires frameworks that transcend traditional organizational structures and organizations should recalibrate their financial management approach to account for a holistic approach. 
• New technology governance models must be established: Discussions and examples revolved around AI’s impact on top management teams on five primary areas of influence: hybrid decision-making processes, AI’s ethical implications, governance through AI, AI-driven competitive advantage, and pivoting toward productivity rather than labor displacement. For technology leaders, this means developing new capabilities in managing AI governance, measuring AI ROI, and integrating AI considerations into traditional TBM practices.

The strategic imperative: TBM as integration discipline

The Amsterdam summit revelations point to TBM’s multiyear evolution as more than a cost management framework — it’s becoming the integration discipline for modern technology governance and value management. This evolution addresses the fundamental challenge facing technology leaders: managing increasingly complex, distributed, and rapidly changing technology portfolios while demonstrating clear business value. (See also: TBM helps CIOs translate tech spending to business outcomes.)

The new TBM framework positions it as the dominant discipline that integrates context-aware insights from specialized practices like IT financial management, FinOps, FAIR, ITSM, and more, providing a holistic view of technology’s value to the business. Such integrations are critical as organizations grapple with AI governance, sustainability requirements, cybersecurity risk management, and multicloud complexity.

For CIOs and finance leaders, the message from Amsterdam is clear: TBM is no longer just about IT cost transparency. It’s about creating a unified framework for technology value management that spans organizational boundaries, integrates specialized disciplines, and provides the governance structure needed for AI-era technology leadership. As technology becomes more central to business strategy and more complex in its delivery models, TBM provides the standards, processes, and integration capabilities that modern enterprises need to succeed. The question isn’t whether to adopt the different disciplines and federate them; it is, rather, how quickly organizations can implement them to gain competitive advantage in the AI era. (See also: How to build an AI-ready organization: the Enterprise Intelligence Architecture.)

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Eveline Oehrlich, a director in IDC’s IT Executive Programs (IEP), is the former chief research officer at PeopleCert and DevOps Institute. She is considered a thought leader in the adoption of DevOps, value stream management, and IT and enterprise service management principles to improve IT services and products. She is passionate about empowering IT leaders with strategy, operational excellence, and creating a culture of continuous improvement and automation.