Organizations around the world are racing to adopt artificial intelligence. Some are building advanced machine learning platforms, while others are experimenting with generative AI to improve customer service, automate support functions, or accelerate internal workflows. Yet beneath the pressure to “deploy AI everywhere,” a quieter reality is emerging: Organizations leveraging the Technology Business Management (TBM) discipline are achieving faster, more confident AI investment decisions and bringing AI value to the business more quickly. This advantage is measurable—80 percent of TBM practitioners report having a clear plan to manage AI costs, compared to just 33 percent of organizations operating without TBM.
TBM—through its modernized Framework, Conceptual Model, Taxonomy, and TBM by Design methodology—gives leaders a structured way to understand the cost, performance, and outcomes of AI initiatives. Increasingly, that insight is revealing a difficult truth: a significant number of AI deployments are not meeting expectations, either because they fail to produce measurable value, introduce operational risk, or degrade customer and employee experience.
As a result, a new phenomenon is gaining visibility: AI rationalization.
What Is AI Rationalization?
AI rationalization refers to the intentional scaling back, suspension, or discontinuation of AI initiatives that fail to deliver sufficient business value relative to their cost, risk, and operational complexity. Like cloud repatriation, AI rationalization reflects investment discipline, not rejection of the underlying technology.
AI rationalization occurs when organizations determine that a particular AI solution is not producing adequate return—whether measured in financial performance, customer satisfaction, employee experience, risk reduction, or strategic advantage. In these cases, continuing to invest in AI becomes less defensible than reallocating resources to higher-value alternatives.
AI rationalization may take several forms:
- Targeted rationalization: narrowing the scope of AI-supported tasks
- Partial rationalization: reducing automation depth or reintroducing oversight where AI underperforms
- Full rationalization: discontinuing AI systems that fail to justify ongoing investment
- Strategic pauses: suspending AI initiatives until foundational capabilities mature
The rise of AI rationalization is not evidence that AI is failing as a technology. Rather, it reinforces a core TBM principle: value does not come from technology adoption alone—value comes from disciplined governance, transparency, and informed decision-making.
AI Deployment Lessons: Real-World Failures Through a TBM Lens
The following examples span industries and use cases, yet they reveal a consistent pattern: AI initiatives often falter not because the technology is incapable, but because investment scales before economics, operational impact, and customer outcomes are fully validated. In each case, measurable value signals were incomplete, misaligned, or surfaced too late. A mature TBM discipline would not have guaranteed technical success—but it could have imposed clearer outcome thresholds, cost transparency, and governance controls before expansion.
1. McDonald’s Ends Its AI Drive-Thru Initiative
McDonald’s introduced AI-powered drive-thru ordering at more than 100 U.S. locations to automate order-taking and reduce labor costs. Instead, incorrect orders and inconsistent performance spread on social media, eroding customer trust and increasing frontline workload as employees corrected AI errors. In 2024, the company discontinued the pilot after determining it did not improve customer experience. McDonald’s did not abandon AI; it continues investing in forecasting, order verification, and operational optimization—areas with clearer economic visibility.
A mature TBM program could have constrained scale earlier. By modeling full cost-to-serve—including integration, exception handling, and frontline rework—and tying expansion to explicit customer satisfaction and accuracy thresholds, TBM governance could have identified that projected labor savings were being offset by operational friction and brand risk before broad rollout.
2. Commonwealth Bank Rehires Staff After AI Falls Short
Commonwealth Bank of Australia deployed AI voice bots in July 2025 to replace customer service roles in pursuit of efficiency. Instead, resolution times lengthened, escalations increased, and call volumes failed to decline as expected. Within weeks, the bank reversed the decision and reinstated affected staff—an unusually rapid public recalibration driven by operational realities. The episode did not signal a broader retreat from AI; CBA continues investing in fraud detection, customer engagement, and other measurable use cases.
From a TBM perspective, the outcome reflects scaling before validating service thresholds. A mature TBM program would have required defined targets for resolution time, escalation volume, and cost-to-serve before workforce reductions were tied to automation. Full economic modeling of downstream complexity could have revealed that projected savings were not materializing. Governance stage gates tied to realized outcomes—rather than forecast efficiencies—could have constrained rollout and avoided a public reversal.
3. Klarna Walks Back Claims About AI Replacing 700 Workers
Klarna announced that generative AI was performing work equivalent to hundreds of customer service employees. Over time, inconsistent responses and poor handling of complex issues affected customer trust. Human agents were reintroduced, while AI investment continued in personalization and fraud detection.
Here, TBM would have separated automation volume from realized value. Clear outcome metrics—customer satisfaction, resolution quality, and operational cost impact—would have been required before validating labor-equivalent savings. By enforcing evidence-based thresholds and portfolio comparison against alternative investments, TBM governance could have moderated expansion until value was consistently demonstrated.
Taken together, these cases illustrate AI rationalization: disciplined recalibration of specific use cases that failed to deliver net value, paired with continued investment in AI initiatives aligned to measurable business outcomes.
How TBM Supports Better Decisions About AI Scaling and AI Rationalization
TBM disciplines AI investment decisions. When performance falters, it provides the structure to decide whether to fix, narrow, or stop an initiative.
- Start with economics. Using TBM Taxonomy v5.0, organizations must model the full cost of AI—platform, integration, retraining, governance, monitoring, and operational rework. If AI does not reduce net cost-to-serve or improve value drivers after all costs are accounted for, it should not scale.
- Demand measurable outcomes. The modernized TBM Framework ensures investments and day-to-day technology delivery and operations are aligned to organizational value drivers like financial performance, risk and compliance, or sustainability goals. AI that degrades these value drivers should trigger review and recalibration—not expansion.
- Compare AI against alternatives. Through the Value Drivers layer, TBM forces capital allocation decisions based on relative return, not momentum or trend pressure.
- Institutionalize governance. TBM by Design embeds stage gates, accountability, and predefined decision rights so scaling occurs only when value is proven.
TBM accelerates time to value when AI works—and enables disciplined AI rationalization when it does not.
Conclusion: AI Adoption Is Not a One-Way Door
AI holds enormous promise, but not every AI initiative creates value. Some increase cost, degrade customer experience, frustrate employees, or introduce complexity without sufficient return. In these cases, AI rationalization is not retreat—it is responsible investment governance.
Organizations that apply TBM from the outset make better AI decisions and accelerate time to value. Those already deep into AI investments can still bring TBM discipline to clarify economics, measure outcomes, and correct course. When AI underperforms, TBM provides the transparency, comparative insight, and governance structure needed to recalibrate or discontinue initiatives with confidence.
In a world where AI will continue to evolve rapidly, TBM gives leaders a durable advantage: the ability to scale AI decisively—and rationalize it without hesitation when value fails to materialize. The TBM Council will continue delivering practical guidance and real-world perspectives to help organizations govern and optimize AI investments.
Leaders can explore dedicated AI resources in the TBM Council Knowledge Base and join the forthcoming AI Strategy Community, where practitioners will exchange proven approaches for managing the financial and operational realities of AI. Watch for additional AI-focused insights and implementation guidance in the months ahead.
