The Future of Enterprise Applications and Operationalizing AI

Gartner Application Innovation and Business Solutions (AIBS) Summit 2024 is in Las Vegas this week.

One of this year's main themes is about the future of enterprise applications which consists of four facets according to Gartner:

1) Composable architecture;

2) Autonomous orchestration;

3) Embedded intelligence (e.g. AI); and

4) Adaptive experience.

In short, this aligned with what we learned from Paul Vincent, a former Gartner analyst who retired earlier this year, in AIBS 2023 last year. The essence was that we must refocus on software engineering innovations, instead of just comparing technologies the whole market had gotten used to in the past 10-15 years, due to the SaaS trend.

This mindset change is a must due to the rapid development of AI/ML and Gen AI that impact many aspects of application development.

Since AI will be one of the most historical leaps in the use of technology, that’s why most organizations have budgeted for AI experimentation and implementation.

However, the cost of conducting AI initiatives is rising rapidly.  There is a lot at risk here, so picking the best place to operationalize AI is essential.

At AgilePoint, we believe one of the best places to operationalize AI is through processes, especially end-to-end processes spanning system of records and connecting silos.

Yet not all process solutions can operationalize AI and get you the same level of operational agility without generations of tech debt.

Download the white paper attached: An Executive’s Guide to Operationalizing AI

At AgilePoint, we designed a composable process architecture in 2003 for creating codeless and hyper-adaptive process orchestrations with operationalizing AI in mind. It was enabled by metadata abstraction and a model-driven framework, free of code generation, compilation, and the need for virtual machines.

By combining AI with data, analytics, and automation, AI can guide businesses to adapt to achieve their goals continuously with human-in-the-loop or closed-loop optimization.

At our theatre presentation, The Ultimate Double Move: Operationalizing AI and Eliminating Tech Debt, we illustrated why this architecture can bring the aspiration to operationalize AI to life quickly.

Further, most organizations spend 80% of their time resolving 20% of their exceptions today. In the Age of AI, there will be more exceptions and more of them will be probabilistic.

Those who can operationalize AI through hyper-adaptive process orchestrations to dynamically adapt to handle the unpredictable probabilistic exceptions AI/ML could throw at you will have an advantage in winning the AI race by delivering dynamically optimized and mass-individualized customer experiences