3 Concrete Ways to Drive AI ROI

Scaling AI, Featured Lynn Heidmann

With Generative AI in the picture, there has never been more pressure (especially from the board room and business leaders) to invest in AI. But it’s not enough to implement data science and AI use cases. They also must bring real-world, tangible return on investment (ROI) for the business.

Our June 2023 survey of 400 senior AI professionals revealed that only 21% report high (more than 5x) ROI on AI investments. And 11% of respondents reported delivering $1 or less for every $1 spent on AI.

Only time will tell whether the long-term outlook on ROI will improve. According to Gartner®, “By 2028, more than 50% of enterprises that have built large AI models from scratch will abandon their efforts due to costs, complexity, and technical debt in their deployments.” We think this is potentially a sign that Generative AI ROI and clear revenue growth from the technology will prove difficult.

As a data, analytics, or even IT leader, what can you do to bolster the ROI of AI systems and initiatives? 

→ Get Your Return on AI With Dataiku: Book an ROAI Consultation

1. Track AI ROI

Our survey also showed that the majority of organizations don’t have a way to track ROI from AI. 

survey results for the question How Does Your Organization Account for the Value Delivered With Data, Analytics, & AI Initiatives?

 

Tracking ROI for AI projects can be challenging. However, it is even more challenging if people are doing data work and projects across a variety of tools. Leveraging one unified place for data work makes ROI calculation and tracking easier. Dataiku is the only platform that unifies all enterprise data work, from analytics to Generative AI:

  • Dataiku’s single, centralized environment allows multiple personas with varied technical skill sets to collaborate on AI projects.
  • Dataiku’s end-to-end platform enables teams to do everything from data prep to visualization to modeling. Plus, it provides all the tooling to deploy and maintain AI solutions and data products in production.
  • Dataiku Solutions give teams an easy way to boost and accelerate industry use case delivery. This is a boon for resource-constrained organizations as well as those looking for quick ROI on table stakes use cases.

2. Control Costs For AI Tools & Providers

According to our survey, 22% of organizations plan to spend $10-$50 million on AI services in the next 12 months. Another 10% plan to spend more than $50 million on services (including consultants, system integrators, etc.).

Many companies today rely on third-party tools and organizations to carry out data, analytics, and AI work. This kind of work includes analyzing ad-hoc data all the way up to building out entire AI proofs of concept and business cases.

In Forrester: The Total Economic Impact™ Of Dataiku, one of the specific benefits called out in the study is reduced costs on data tools and consultancies thanks to Dataiku. What does that mean in practice? By using Dataiku and enabling their own users to work with data, interviewees’ organizations realized cost savings from data analytics tools and consultancies/third-party providers.

For example, the analytics and data science product owner at a pharmaceutical company has highlighted: “We are moving around 250 users from a statistical tool to Dataiku. Our annual contract was around $2.2 million or $2.1 million. Now, we have decommissioned that.”

No need to manage and pay for an unwieldy stack of disparate solutions. Dataiku can handle all the needs of your no-, low-, and full-code users in a single product. The same interviewee also said: “Earlier, we used to send data to a consulting company, and they used to run it for us. They were charging us nearly a million dollars a year to run it. We stopped this and brought it in-house.”

Bringing more analytics in house has other obvious benefits besides cost. For instance, as AI becomes more strategically important, it is crucial to develop use cases internally. 

3. Tackle Mundane & Moonshot Use Cases

The potential applications for Generative AI, or even traditional machine learning, fits into two broad categories:

  1. Moonshots: Use cases that introduce fundamentally new capabilities in the business. These use cases are so powerful that these applications have the potential to transform the business directly.
  2. Mundane: Use cases that augment hundreds or thousands of processes and decisions throughout the business.

The most commonly imagined moonshot application of Generative AI in the enterprise, for example, is the all-knowing chatbot. This always-on, always-accurate assistant can provide immediate answers about the current state of the business as well as make accurate predictions about the future.

Could this work? Potentially, but there are many caveats. And whether or not people would fully trust such a system is a significant concern. Furthermore, the ROI is incredibly uncertain. We believe this could be an example of what Gartner mentions in the beginning of this blog. A use case left behind with millions of dollars wasted in investment.

Receiving less attention is the potential for AI to transform the business through the augmentation of countless mundane tasks. In this scenario, the business chooses different models for different applications, balancing considerations like performance, cost, and privacy.

Teams have the power to apply approved technology to the challenges that they face. Technology democratizes, empowers people, and drives a massive increase in productivity. Each mundane application has the potential for massive ROI, and with much smaller upfront cost and investment.

You need to be able to do both. Yes, you should start building the rocket for that moonshot. But even more immediately, you must start augmenting processes throughout your organization for smaller ROI wins.

Get Your ROAI With Dataiku

Dataiku is the leading platform for Everyday AI. We're enabling people to build data into their daily operations, from advanced analytics to Generative AI.

More than 600 customers get measurable ROI using Dataiku for diverse use cases. From predictive maintenance and supply chain optimization to quality control in precision engineering, marketing optimization, Generative AI use cases, and everything in between. We call that a Return on AI.

Gartner Article, Take This View to Assess ROI for Generative AI, Jackie Wiles, August 15, 2023. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

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