The Enterprise AI Journey: Readiness, Acceleration, Reimagination

What are the patterns of AI investment in Enterprises?

At AuxoAI , we’ve seen our clients focusing on three steps in their AI journeys: Readiness, Acceleration, and Reimagination— sometimes starting in different areas of the business at the same time.

1. AI Readiness: Getting Started

a) Set Goals & Build Support: Creating AI vision and strategy along with identifying the value areas with lower risk. customer support, marketing & sales, Tech & data functions are normally the first adopters. Sometimes enterprises are investing in AI training but it is much lower in the priority than one would imagine

b) Organize Data: Data modernization efforts are getting a boost sometimes with new elements thrown in (like vector databases)

c) Application roadmap: Rethinking application investment or scope of the investments in the roadmap

And we see a lot of data modernization efforts as no regret investment from the technology team while the organization is going through the process of consensus building on value

2. Acceleration: Moving Faster

a) POCs and MVPs: Building quick prototypes to show how AI can solve problems, then improve them based on feedback. Challenge here is that 80% accurate solution is very easy to get to, the last 20% is much harder.

b) Operationalize: Turning successful prototypes into systems that run reliably at scale. This includes monitoring AI to ensure it performs as expected. At the moment, it is a dual challenge: bringing together deep engineering along with the driving adoption by taking people along the journey.

c) AI CoE: If there are a whole range of use cases and there is centralization of AI efforts, we see establishment of AI CoEs to drive the acceleration

3. Reimagination: Thinking Bigger

a) Redesign workflows and processes: As functions see some initial value from operationalization of AI solution, they see the opportunity to rethink the work flow with human labor and AI labor division. Some bold and innovative clients are starting here. These are like erstwhile digital transformation efforts but mostly with a custom AI solution at the core of it rather than implementation of SaaS solution.

b) Create New revenue streams: For many companies, building AI features around their core product and service can help launch new products, enhance services, or business models.

We’ve learned that different functions within the enterprise might be at different stages of this journey at the same time. And That’s okay! There are two or three big areas within the enterprise where AI will make strategic difference. Rest of the use cases will have incremental value and AI will eventually become part of the standard operating process

What matters is starting, learning quickly, and building momentum.