Why Explainable AI is Key in RevOps

"Apple will get young people used to wearing a watch, and later maybe they will want to buy themselves a real watch." –Jean-Claude Biver, TAG Heuer CEO

As Jean-Claud Biver saw back in 2017, a promising, universal solution can create noise without delivering the precision or value required for the real job. Just like the smartwatch, AI is surrounded by hype — but RevOps leaders don’t need black-box AI systems. They need purpose-built, consistent, and understandable tools. 

By combining general capabilities with the specific business context of each user, explainable AI tools provide the focus that RevOps leaders need. 

Why “Universal” AI Can Fall Short in Revenue Operations

Much like a smartwatch that can do everything from measuring your pulse to sending text messages, today’s AI systems — especially large language models (LLMs) — are designed to be universal. As a result, they’re dazzling in their breadth but can be underwhelming in their specificity. While impressive in fields like creative generation, these models often fall short when applied to recurring business operations such as sales forecasting, simple data mining, or lead scoring.

At AlgOps, we’ve moved away from LLMs for forecasting. Why? Because operational decision-makers don’t want AI that “thinks” for them. After all, as businesses change day in and day out, LLMs don’t get the context — people do. Instead of really powerful LLMs, people want custom, consistent, and transparent tools that empower them to make informed decisions.

Let’s break down some of the biggest limitations of universal AI systems:

Lack of Consistency

“AI tools” are often black boxes. They may provide answers, but they do so inconsistently without clarifying the reasoning or logic behind them. In any business, trust and understanding are paramount. Decision-makers need to see the “why” behind every forecast or recommendation. Moreover, they need to know what they can expect day in and day out. 

Excess Complexity

No one needs a jackhammer to drive in three nails. Universal AI models often come with capabilities far beyond what’s necessary, creating inefficiencies in both implementation and maintenance. For focused use cases like sales forecasting or lead prioritization, simpler, bespoke models often outperform — with fewer resources.

Misaligned Data

Universal models typically rely on massive amounts of general data. But for sales forecasting and other RevOps needs, first-party data—data that’s specific to your business—is what drives accuracy. Training models on unrelated or generalized datasets often leads to noise, not insights.

The Case for Explainable, Purpose-Built AI

Explainable AI solves these problems by prioritizing transparency, simplicity, and specificity. In the context of RevOps, explainable AI offers:

• Clarity: It provides a clear understanding of how decisions are made so that stakeholders can trust the outcomes.
• Agility: Explainable AI models are simpler to train, fine-tune, and retrain, making them adaptable to changing business needs.
• Focus: By solving one problem exceptionally well, custom AI avoids the distractions and inefficiencies of one-size-fits-all solutions.
• Privacy: Unlike massive LLMs that often rely on public or third-party data, explainable models can be trained exclusively on your first-party data. This ensures security and relevance.

For example, when we build forecasting models at AlgOps, we use algorithms designed for single use cases. These models focus on what matters most — your data and your objectives — without introducing unnecessary complexity or risk.

Using AI as a Tool

Imagine relying on a universal LLM to predict quarterly sales. You’d risk basing decisions on a model trained on irrelevant data and delivering opaque outputs. Contrast that with an explainable model trained exclusively on your historical sales data, calibrated to understand your unique sales cycles, and capable of providing transparent insights into its predictions.

Because explainable models are sensitive to data quality, you can see exactly how each variable impacts the outcome. Then, you can modify your approach to improve your results. That simply isn’t the case with generalist LLMs. 

Why Explainable AI Works for AlgOps

At AlgOps, we specialize in creating autonomous, custom-built models for RevOps. These models are trained on first-party data, so they’re both private and secure. And instead of trying to be a jack-of-all-trades, they’re purpose-built for specific use cases.

We believe that business leaders don’t need a “smartwatch” that’s just okay at everything. They need tools that deliver clarity, precision, and results.

Looking for AI that works like a tool—not a black box? Let’s talk about how AlgOps can deliver explainable solutions tailored to your business.