Article

Tech Trends 2025, Part I: Expert Models

AI will accelerate into more aspects of the organization’s operations.

Artificial Intelligence (AI) is set to transform business operations in 2025, with organizations increasingly integrating it into workflows to enhance efficiency and unlock new opportunities. This article explores how expert models, a specialized form of AI, are helping companies solve complex challenges, improve processes, and prepare for the future of digital transformation.

The Issue & The Opportunity

Investment in AI is slightly more pronounced among IT departments with higher maturity, with 80% of transformers saying they are already invested or will be by the end of 2025. Seventy-two percent of average IT departments say they will be doing the same.

For the most part, the investment is already made – only slightly more than one-quarter of all organizations say they aren’t invested yet but plan to invest by the end of 2025.

Organizations bullish on AI see it fitting into the next wave of digital transformation. It can augment many different business processes, and it also promises to upend some business models entirely, demanding new ways to interact with customers and likely to raise expectations even higher.

Challenges to Integrate AI Successfully

• Aligning AI’s capabilities with the challenges of their business domain.
• Integrating AI into existing business processes in a way that augments them.
• Hiring or training the requisite AI talent.
• Architecting a high-quality and specialized data pipeline for fine-tuning and pretraining foundation models.
• Navigating myriad tech platforms and interoperability issues.

Those persistent enough to solve these problems will reap the rewards. Higher maturity IT firms are more optimistic about solving these challenges, citing exponential increases in value from AI.

The Concept: Expert Models

Expert models solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than conventional procedural programming code.

Developing Expert Models

• Leveraging a model developed by an industry-focused vendor.
• Collaborating with industry peers to train a model.
• Completing additional pretraining and fine-tuning on models sequestered in an organization’s infrastructure.

Customized models result in more reliable and relevant outputs, overcoming challenges with AI accuracy.

The Effects of Expert Models

1. Augmentation of Existing Processes

Instead of creating a new process or entirely automating tasks, firms augment workflows with AI. Large language models enhance the speed and quality of work through pattern recognition and analysis.

2. Healthy Data Management

The quality of AI outputs depends on the quality of input data. Organizations with robust data hygiene and accessibility practices will achieve the best results.

3. Democratizing AI

No-code environments allow non-technical workers to integrate AI into workflows. These solutions make AI accessible for tailored automations using natural language.

In Conclusion

LLMs alone don’t solve business problems. Success lies in integrating them with existing business systems and ensuring human oversight for safe, effective AI deployment.

Like This Article? Help us Spread the Word

About the Author