The 2025 Great AI Disappointment: Our Response to Why Companies Are Abandoning Their Generative AI Projects

The honeymoon phase is over. According to the Economist, after two years of breathless excitement about generative AI's transformative potential, the harsh reality of implementation has set in. According to recent S&P Global data, 42% of companies are now abandoning most of their generative AI pilot projects—a dramatic spike from just 17% last year. For CFOs watching millions of dollars disappear into failed AI initiatives, this trend represents both a warning and an opportunity. 

The Root of the Problem: Big Models, Bigger Disappointments 

The current wave of AI disillusionment stems from several fundamental mismatches between expectation and reality: 

Scale Mismatch: Large Language Models (LLMs) like GPT-4 are designed to be generalists, capable of handling virtually any task. But most business problems don't require this breadth—they need depth in specific, narrow domains. It's like buying a Ferrari to deliver pizza: impressive technology, but wildly inefficient for the task at hand. 

Cost Explosion: LLMs are expensive to run, requiring significant computational resources for every query. When companies deploy them across entire organizations, the operational costs quickly spiral beyond what most business cases can justify. The Klarna example—where the company had to rehire human customer service representatives after cutting too deep with AI—illustrates how cost savings can turn into cost disasters. 

Implementation Complexity: LLMs require sophisticated infrastructure, extensive fine-tuning, and constant monitoring to work effectively in enterprise environments. Many companies underestimated the engineering overhead required to move from impressive demos to production-ready systems. 

The "Black Box" Problem: Large models are notoriously difficult to interpret and control. For CFOs who need to understand and audit business processes, deploying systems that can't explain their reasoning creates compliance and risk management nightmares. 

The SLM Solution: Right-Sized AI for CFO Priorities 

Small Language Models (SLMs) represent a fundamentally different approach that aligns much better with CFO priorities around cost control, risk management, and measurable ROI. Here's why they're emerging as the smarter choice: 

1. Predictable Economics 

SLMs can run on standard business hardware or modest cloud instances, making their operational costs predictable and manageable. Instead of paying per API call to external providers, CFOs can budget for SLM infrastructure like any other IT resource. This shift from variable to fixed costs makes financial planning infinitely easier. 

Consider a financial close process: instead of sending thousands of journal entries to an expensive LLM for review, an SLM trained specifically on your company's accounting patterns can run locally, processing the same volume for pennies on the dollar. 

2. Focused Performance 

SLMs excel when trained on specific domains. A model designed solely for expense categorization will outperform a general-purpose LLM on that task while using a fraction of the resources. This focus translates directly into better business outcomes: higher accuracy rates, fewer errors requiring human intervention, and more reliable automation. 

3. Transparency and Control 

Smaller models are inherently more interpretable. CFOs can understand how decisions are made, audit the reasoning process, and maintain the control necessary for regulatory compliance. When an SLM flags a transaction as suspicious, it can explain its logic in terms that finance teams can verify and defend to auditors. 

4. Faster Time-to-Value 

SLMs can be deployed and customized quickly. Instead of months-long implementation projects requiring extensive infrastructure changes, focused SLMs can often be operational within weeks. This rapid deployment means CFOs can see concrete results—and positive ROI—much sooner. 

Practical SLM Applications for Finance Teams 

Smart CFOs are already identifying specific use cases where SLMs deliver immediate value: 

Automated Expense Coding: Train an SLM on your company's specific chart of accounts and expense patterns. The model learns to categorize transactions with accuracy that often exceeds human consistency, while explaining its reasoning for each decision. 

Contract Analysis: Deploy SLMs to review vendor contracts for specific risks or opportunities. Unlike general-purpose models that might flag everything as potentially important, focused SLMs can identify the clauses that actually matter to your business. 

Financial Close Automation: Use SLMs to identify unusual journal entries, flag potential errors, and streamline the month-end close process. The models learn your company's specific patterns and can spot anomalies that generic systems would miss. 

Budget Variance Analysis: Train SLMs to generate consistent, insightful explanations for budget variances, freeing analysts to focus on strategic responses rather than routine reporting. 

Implementation Strategy: Start Small, Scale Smart 

The key to successful SLM implementation lies in starting with narrow, hgh value use cases and expanding systematically: 

Phase 1: Proof of Concept - Choose one specific, measurable problem where human effort is repetitive and rule-based. Deploy a focused SLM and measure results against clear metrics. 

Phase 2: Process Integration - Once the model proves its value, integrate it into existing workflows. Focus on augmenting human capability rather than replacing it entirely. 

Phase 3: Strategic Expansion - Use lessons learned to identify additional use cases where the same approach can deliver value. Build internal expertise and confidence before tackling more complex challenges. 

(Talk to Assure about how you can get started for under $5K) 

The CFO Advantage: Leading Through Practical AI 

While other executives chase the latest AI headlines, CFOs have a unique opportunity to lead through practical implementation. By focusing on SLMs that deliver measurable value at predictable costs, finance leaders can demonstrate how AI should really work in business: quietly, efficiently, and profitably. 

The companies abandoning their grandiose AI projects aren't failing because AI doesn't work—they're failing because they chose the wrong tools for their specific problems. CFOs