Four AI Predictions for 2023: From the Great Correction to Practical AI

There’s no way to sugarcoat it: 2022 was a rough year for businesses. Tech companies are now learning to solve for new challenges, their once limitless horizons tempered by hiring freezes and fleeing investors. Specific to artificial intelligence (AI), companies are reconsidering moonshot projects, and returning to more practical, near-term approaches to artificial intelligence and machine learning. I call this the Great Correction.

Four Predictions for Practical Artificial Intelligence

Under the umbrella of practicality, companies will strategically rethink how they use artificial intelligence, an attitudinal shift that will filter down to implementation, AI and machine learning model management, and governance.

Here are my predictions for Practical AI in 2023:

1. Novelty applications will be out, practical applications will be in

Generative AI has been a big buzzword lately, with slick image generation capabilities grabbing headlines. But the reality is, Generative AI isn’t a new technology; our data science organization at FICO has been using it for several years in a practical way to generate synthetic data, and to do scenario testing as part of a robust AI model development process.

2. Artificial intelligence and machine learning development processes will become productionalized

To achieve production-quality artificial intelligence, the development processes themselves will need to be stable, reliable and productionalized. This comes back to model development governance, frameworks for which will increasingly be provided and facilitated by new artificial intelligence and machine learning platforms now entering the market. These platforms will set standards, provide tools and define application programming interfaces (APIs) of properly productionalized analytic models, and deliver built-in capabilities to monitor and support them.

3. Proper model package definition will improve the operational benefits of AI

Productionalizing AI includes directly codifying, during the model creation process, how and what to monitor in the model once it’s deployed. Setting an expectation that no model is properly built until the complete monitoring process is specified will produce many downstream benefits.

4. There will be a handful of enterprise-class AI cloud services

Clearly, not every company that wants to safely deploy AI has the resources to do so. The software and tools required can simply be too complex or too costly to pull together in piece-parts. As a result, only about a quarter of companies have AI systems in widespread production. To solve this challenge and address a gigantic market opportunity, in 2023 we will see the emergence of a handful of enterprise-class AI cloud services.

Where Practical AI Lives: The Corpus AI

Over the past five years or so FICO has been evangelizing the need for Responsible AI practices, which guide us how to properly use data science tools to build AI decisioning systems that are explainable, ethical and auditable. These principles are at the heart of an organization’s metaphorical analytic body. But they are not enough. This analytic body, which we call the Corpus AI, is where Responsible AI and Practical AI must be supported by the equivalents of a biological circulatory system, skeletal system, connective tissue and more.

Looking ahead, learning to cope with the ever-evolving market pressures will remain the new normal. These AI predictions will allow the Corpus AI to strengthen and flourish during, and far beyond, the Great Correction – in a mature, standardized, auditable and regulation-ready way.

By Scott Zoldi, Chief Analytics Officer at FICO