TBM for AI
Turning Innovation Into Measurable Value with the TBM Framework
AI represents one of the most powerful engines of innovation in modern enterprises. From generative copilots to intelligent automation, AI promises to transform how organizations operate, serve customers, and compete. But that promise is accompanied by unprecedented complexity: unpredictable scaling costs, opaque ROI models, fragmented toolchains, and resource bottlenecks.
Many organizations launch AI initiatives only to find themselves surprised by operational costs, stalled due to resource constraints, or unable to connect investments to measurable business outcomes.
Technology Business Management (TBM) offers a better way forward. As a value discipline, TBM provides a structured, transparent framework for understanding AI costs, forecasting resource requirements, allocating investment, and optimizing outcomes. With TBM Taxonomy 5.0, organizations can model the full lifecycle of AI—from early experiments to enterprise-scale deployments—and make the trade-offs necessary to drive innovation with accountability.
Taxonomy 5.0: Unlocking AI Transparency, Insight, and Optimization
At the core of TBM is a robust, evolving taxonomy. Version 5.0 introduces advanced modeling capabilities that support the full diversity of AI architectures and solution types:
- Support for Generative, Interpretive, Predictive, Prescriptive, and Agentic AI
- Technology Resource modeling for GPUs, specialized data platforms, open-source and commercial models, and AI-specific labor
- Scenario modeling for buy vs. build, hybrid inference placement, and model fine-tuning strategies
Organizations can now heatmap spend drivers, trace cost allocations by business unit, and integrate token-based or blockchain-enabled consumption models into showback and chargeback. These capabilities make it possible to not only optimize AI spend, but also align it with innovation goals across product, customer, and societal outcomes.
Learn more about how TBM can support your AI deployments and operations in our publication TBM for AI Value Realization: A Guide to Optimizing AI Investments by Aligning Business Fundamentals for Maximum Value and ROI.
Use Case: Fully Burdened AI TCO Across Architectures
From SaaS-based chatbots to custom-built foundation models, AI investments vary dramatically in complexity and cost structure. TBM provides a composable approach to modeling the Total Cost of Ownership (TCO) across this spectrum:
- Map cloud infrastructure, GPUs, licensing, and platform tools to Technology Resource Towers
- Include labor, consulting, data acquisition, and model fine-tuning in Headcount and Other Labor pools
- Associate AI investments with specific Solutions and Business Capabilities to track outcomes
With Taxonomy 5.0, AI cost models aren’t static—they support scenario planning so teams can evaluate the trade-offs of inferencing in public cloud vs. on-prem, or customizing a commercial model vs. building from scratch. This helps ensure AI investments are aligned to both performance and business value.
Use Case: Capacity Planning for AI Innovation
Innovation depends on access to the right capabilities at the right time. Yet many AI initiatives are delayed or derailed by unexpected infrastructure, data, or labor constraints. TBM enables:
- Forecasting of compute, GPU, storage, and licensing needs for each stage of AI maturity
- Prioritization of limited resources to AI initiatives that align with strategic innovation goals
- Visibility into AI resource consumption across products, teams, and business units
By modeling demand and capacity together, TBM supports smarter planning and resource shaping—avoiding waste while ensuring high-value innovations are not stalled by scarcity.
Use Case: AI Showback and Chargeback
As AI capabilities become embedded across the enterprise, it’s critical to track and allocate consumption transparently. TBM enables:
- Connection of token-based usage to cost models using the TBM Taxonomy
- Allocation of model usage, data access, and inferencing to departments or services
- Showback or chargeback mechanisms for both internal teams and business partners
This ensures accountability, creates a feedback loop for optimization, and supports equitable funding models for shared AI platforms.
Use Case: TBM for Responsible and Ethical AI
True innovation isn’t just about performance—it’s also about responsibility. TBM helps organizations embed ethical, environmental, and social impact considerations into AI investments:
- Tag and track AI investments aligned with DEI, accessibility, environmental sustainability, and regulatory compliance
- Include metadata and impact reporting for initiatives tied to non-revenue value
- Enable visibility into investments that support societal goals—alongside commercial ones
When organizations can see the cost and impact of their AI investments in support of broader values, they are more likely to sustain those efforts, rather than abandon them when budgets tighten.
TBM by Design: Proactively Protecting the Enterprise
Most organizations apply TBM after decisions have been made—tracking cost, performance, and outcomes after deployment. TBM by Design flips this model. It embeds TBM’s financial, consumption, and value insights directly into planning, architecture, scenario modeling, business case development, governance workflows, and decision checkpoints. Rather than reporting or reacting to value assessments after the fact, organizations proactively design for it—before investments are made, before infrastructure is provisioned, and before new features are launched.
Learn more about TBM By Design.
The principle of designing value from the beginning is critically important to AI solutions which require early, high-stakes decisions on architecture, infrastructure, training approaches, and operating models. Applying TBM only after AI is deployed limits its ability to influence strategy or justify spend. With TBM by Design, AI capacity, TCO, and ROI modeling become inputs to planning and architecture decisions—guiding innovation toward the highest-value outcomes.
How TBM by Design supports AI:
- Incorporates TCO and capacity forecasts into AI funding and business case approvals
- Supports build-vs-buy decisions based on projected costs, performance, and ROI
- Enables early identification of high-cost or low-yield solutions
- Aligns scarce resources (GPUs, data science time, etc.) to priority use-cases
Embedded TBM delivers:
- AI investment governance based on modeled cost and value tradeoffs
- Faster time-to-value with more confident go/no-go decisions
- Greater agility in adjusting AI roadmaps as real costs and results emerge
Getting Value Quickly from TBM for AI
AI doesn’t wait—and neither should your TBM practice. You don’t need perfect models to get started:
- Begin with SaaS-based AI solutions and map costs to basic Taxonomy structures
- Use directionally correct models to identify major cost drivers for more complex AI
- Leverage existing billing, infrastructure, and labor data—even if partial—to create early insights
In many cases, a rough TCO model delivered in the first 60–90 days is more valuable than a “perfect” model 12 months later—especially if it prevents waste or strengthens the case for further investment.
Take the Next Step
Innovation thrives on clarity, alignment, and momentum. TBM brings all three to AI initiatives. Whether you’re piloting chatbots or scaling agentic platforms, TBM gives you the tools to model costs, forecast needs, and prove value.
- Start where you are—model even partial data
- Engage with AI leaders through the TBM Council community
- Embed TBM into the foundations of every AI initiative
Explore more in the TBM for AI Community or dive into the TBM Taxonomy 5.0 to begin mapping AI to value.
Join the TBM community: where innovators and leaders converge
The TBM Council is your gateway to a treasure trove of knowledge: think cutting-edge research papers, insightful case studies, and vibrant community forums where you can exchange ideas, tackle challenges, and celebrate successes with fellow practitioners.
We’re calling on organizations and forward-thinking individuals to dive into the TBM community. Participate in our events, engage in our discussions, and tap into a vast reservoir of knowledge. This isn’t just about networking; it’s about contributing to and benefiting from the collective wisdom in navigating the dynamic world of cloud computing.
