A chatbot demo that impresses the board or a predictive model that shows exponential growth in a controlled lab environment—for many businesses, this initial triumph is followed by a long, frustrating silence.
This is the artificial intelligence (AI) production gap—the treacherous chasm between a promising prototype and a scalable, reliable system integrated into core business workflows. It’s a challenge that plagues organisations of all sizes, but for small and mid-sized enterprises (SMEs), the chasm can seem insurmountable, widened by budget constraints and a lack of specialist expertise.
The Production Gap: Why AI is Harder than it Looks
“Prototypes in a lab—models or chatbots—are fairly easy these days, almost deceptively so,” explains Maya Mikhailov, Founder and CEO of Savvi AI, a patented AI platform that enables data and product teams to build, deploy, scale, and manage AI apps. “So companies get lulled into the belief that production cannot be that much harder. But ask any enterprise AI practitioner and they will tell you that building a model or demo is only 10% of the effort—the real work is ensuring you have a system that works with high reliability at scale.”
Getting to that point requires model auditability, explainability, inferencing at scale, AIOps, DevOps, guardrails, secure endpoints, continuous retraining, and much more. This can take months and millions of pounds. Mikhailov wants to “level the playing field for mid-market businesses”, ensuring they can use AI to solve critical business problems.
Low-Code Platforms: A New Route to AI Adoption
The heavy investment required for AI development often creates a barrier to smaller players. Factors like expensive infrastructure, the need for highly skilled talent, and long development cycles are only some of the reasons. Furthermore, many AI solutions are built keeping enterprise-scale challenges in mind. While large enterprises traditionally had an advantage with their vast datasets, this is changing thanks to pre-trained models and APIs. Still, an unclear return on investment (ROI) could be the leading cause behind smaller firms being uncertain about AI investments.
This is where low-code platforms are changing the game. They aim to democratise AI experimentation by using visual workflows, drag-and-drop components, and pre-built models.
ALSO READ: The GenAI Paradox: Why Widespread Adoption Hasn’t Led to Widespread Value
Tackling the ‘Black Box’ Problem
But this approach isn’t without criticism. While low-code simplifies development, there are concerns that such platforms hide the underlying decision-making logic, creating a “black box”. Business stakeholders worry that while they may see what the model predicts, they don’t know why it made that prediction.
Mikhailov is deeply aware of this “perceived risk” with some low-code AI tools that appear to be trading off speed for transparency. “With Savvi, we’ve designed our platform so you don’t have to choose,” she says. “On the surface, it’s an easy-to-use tool for non-technical teams. Below the surface, we provide the same in-depth explainability you’d expect from code-driven builds from technical teams: error rates, feature weighting, hyperparameter definitions, and so on.”
To achieve this, Savvi built a private large language model (LLM) to allow any user to ‘ask’ the AI about how it was constructed and why it behaves the way it does. The goal is so that business users don’t just get results quicker, but also gain clarity, accountability, and confidence in the technology they’re using.
Redefining Success: From Model Accuracy to Business Impact
There has been much debate about how to measure the success of an AI model. The most effective approach involves evaluating both technical performance and business impact.
It’s not just about whether the model works. “Accuracy is certainly part of the story, but the real measure of AI’s success is whether it moves the business needle,” says Mikhailov. She notes that sometimes teams get trapped in chasing marginal improvements to model statistics and lose sight of the fact that the AI is already providing value. “That last 5% improvement that they are insisting on before moving the model into production—so it can learn and actually improve on its own—will take another 80% effort.”
In financial crime, for example, if AI can pre-process suspicious activity reports so your existing team handles more cases without adding headcount, the “economic value is crystal clear”.
Mikhailov suggests companies ask themselves: Does this AI meaningfully improve revenue per employee, or reduce the marginal cost of growth? Those are the kinds of metrics that translate model performance into real-world business impact.
ALSO READ: Where Startups and Enterprises Are Placing Their AI Stack Bets
