Driving enterprise value with AI: What you need to know

Databricks‘ Naveen Rao, Workato‘s Markus Zirn, and Optimizely‘s Alex Atzberger — moderated by LinkedIn’s Tanya Dua — explore how some of the biggest organizations use data and AI to inspire smarter decisions (and new ideas) across the business, and debunk some of the myths and misconceptions about what it takes.

These insights came from our ScaleUp: AI event in October 2023, an industry-leading global conference that features topics across technologies and industries. Watch the full session below:

Importance of open source models in AI deployment

The panelists emphasized the value of open source models in AI deployment. They argued that the rapid progress in the field of AI has been largely driven by the open dissemination of knowledge, shared code, and collaborative research efforts.

“I think open models have been why this field has progressed so fast.”

Rao stated, “I think open models have been why this field has progressed so fast.” He further argued that enterprises using open source models gain an added layer of safety and control. Zirn echoed this sentiment, explaining that open source models can be introspected and modified, providing enterprises with a higher level of control.

However, the panelists also highlighted the need for caution. They believe using AI needs to be managed, just like any other digital tool, to ensure safety and effectiveness. Atzberger mentioned the need for human involvement in the process to manage and adjust the content.

Revolutionizing operational efficiency

The panelists ventured that in the immediate future, AI will primarily revolutionize operational efficiency in large enterprises before it significantly alters product experiences. They believe that AI’s clear ROI in terms of operational efficiency makes it an attractive investment for large enterprises.

Rao explained, “The CEO [of one of the largest insurance providers] basically said…’I think generative AI is going to be, for the next two to three years, an internally facing thing.’ It’s the operational efficiency because, to them, it’s a very clear ROI.”

However, the panelists also predicted that in the long term, AI will have a transformational impact on product experiences. They expect that innovative AI applications in product experiences will initially appear in smaller companies and startups, before being adopted by larger enterprises.

Measuring success in AI deployment centers around efficiency, customer experience, and financial metrics

When it comes to measuring the success of AI deployment, the panelists identified efficiency, customer experience, and financial metrics as key indicators. They stressed that while AI can increase efficiency, its impact on customer experience and financial outcomes is equally important.

“There are so many cost buckets that will just be wiped out or completely changed.”

Atzberger shared, “There are so many cost buckets that will just be wiped out or completely changed, but obviously, you will reinvest it in other pieces where you can actually create better content…and that should be measured by conversion, and…ultimately by the ability to drive more topline growth.”

The panelists agreed that the ultimate measure of success for AI deployment would be its ability to drive top-line growth and conversions. They stressed the need for enterprises to focus on delivering real business outcomes, which will determine the ROI of their AI investments.

This means that AI must not only be able to optimize processes and reduce costs but also deliver tangible results in terms of increased revenue and customer engagement. As AI continues to evolve and become more integrated into various industries, it will be crucial for businesses to have a clear understanding of their goals and how AI can help achieve them. The panelists also emphasized the importance of having a solid data strategy in place, as AI is heavily reliant on accurate and high-quality data for its performance.