How We Build Tangible Value with AI

 

“I think it’s important to focus on the strategic business value you want to achieve first, and then work back to how to leverage AI to obtain it,” said Chief Strategy Officer Robert Sunker of RevGen’s approach to AI.

Just like every new technology, Artificial Intelligence is moving along the path of its own maturity curve. Even before the buzz around Generative AI reached a fever pitch this spring, many organizations were prodding at this exciting technology to understand where it intersected with their own strategy.

At RevGen, we approached AI from a different angle, trying to help our clients identify the clear business needs they have right now. These projects can then earn organizational buy-in and be operationalized quickly. This then leads to real, tangible results that, in turn, builds AI as a core competency able to provide continuous value. By framing AI projects as immediately achievable and valuable, not simply a long-term, transformative initiative, we ensure our clients can mature alongside the technology.

“RevGen is uniquely positioned to help mature organizational AI because we are experienced in creating success through well-planned, revenue-generating applications of this technology.” Anne Lifton, Principal Architect of Artificial Intelligence, was key to building RevGen’s AI framework. With over a decade of experience working in data science, machine learning, and other facets of AI, she knows how to bridge the gap between vision and results. “Our solutions have become launchpads for organizations to reach the next level of using AI to improve their business.”

The parallels between Artificial Intelligence and every other transformative technology – such as the internet itself – are glaringly obvious. Including a hesitance to act because of the blinding hype.

 

Read More: The Intersection of Data Strategy and Artificial Intelligence

 

Pero Dalkovski, RevGen’s VP of Data and Technology understood this sentiment. “Given the hype level around AI, and especially Generative AI, I feel it is extremely important to show quick wins, and how AI can create value for your organization today, in a targeted manner. This is the case with any technology in the early stages of a hype cycle – it’s important to understand that value can be realized incrementally as you mature and scale. Building AI as a core capability is what ultimately generates continuous value.”

It’s important, too, Pero added, to understand that AI can refer to many technologies, not just the ones making headlines. “If we set Generative AI aside for a moment, AI has been used in various applications of business for some time now. For example, we’ve helped clients realize value in pricing optimization through data science, accelerated analytics capabilities with Machine Learning Ops, and improved operational efficiencies via Robotic Process Automation – all disciplines of AI.”

The interdisciplinary nature of this technology also highlights another key to a successful, valuable project: good data.

Ian Foley, Vice President of Analytics & Insights, is intimately familiar with this common challenge. “Time and time again, we hear from experts and have seen in our projects for clients, that data quality and access are critical for success with AI. Taking the time to put in place data governance, data quality, and data democratization capabilities will pay off not just in immediate business value but also in supporting long-term success with AI.”

 

 

As with most technology, finding the path to real value is about recognizing there isn’t a one-size-fits-all answer.

“AI use cases are so diverse that value is going to be different for every project,” said Director of Digital Enablement, Noah Benedict. “As with any investment, the important thing is to establish up front what value you are looking to get from the project and build towards those metrics.

“Are you looking to optimize sales or margin? Increase the effectiveness of call center reps? Make your developers more productive with coding assistants? Each of those use cases have different value propositions, but we can establish KPIs at the beginning of a project, so we understand exactly the value that AI is bringing.”

 

[Read More: 4 Ways Coding Assistants Can Help Developers]

 

Along with the unique problems to solve, we wanted to ensure our approach captured how every company sits at a different point in their own AI maturity. In the 2022 State of AI in the Enterprise survey by Deloitte, only 27% of survey respondents were classified as transformers – companies with an average of five or more AI deployments and processes, while also seeing high achievement from these investments.

Other survey respondents were classified as Pathseekers, with few initiatives but positive results, Underachievers, with several deployments but underwhelming outcomes, and Starters – those who have barely scratched the surface of Artificial Intelligence.

“If your organization has been working with AI for a while – if you have already matured the different facets of AI over the past several years,” Pero said, “yes, absolutely, focus on the transformational nature of Artificial Intelligence. Focus on the potential for significant future benefits.

“However, for all others, focusing on those ‘today’ use cases, where AI can prove beneficial now, is a faster and more sustainable path to success.”

No matter where an organization falls on the transformation scale, there are gaps that can be filled using the RevGen approach. We emphasize using market-leading best practices to deliver low tech debt, highly reusable assets that fit with the overall strategic objectives of our clients. This can manifest as one of a million different powerful use cases.

“What excites me most,” said Anne, “is the potential to assist people with time consuming and repetitive tasks. I am personally eager to explore the many abilities of AI to power enterprise search tools, allowing a ‘Q&A style’ discovery of business assets.”

Noah agreed. “The true promise of AI is to free us from spending so much time on the mundane tasks, so we can tackle the creative, valuable work that humans are so good at.”

 

To learn more about how RevGen finds meaningful business value with AI, visit our Artificial Intelligence page or contact us to speak with one of our AI experts.

 

 

 

Pero Dalkovski is RevGen’s Vice President of Data and Technology. He has spent his career helping clients strategize and implement innovative data, analytics, and technology solutions that deliver business value.

 

 

 

Anne Lifton is a Principal Architect of Data Science and Artificial Intelligence at RevGen. She has over 10 years of experience in building, deploying and managing the lifecycle of data science models across several industries and all three major cloud platforms.

 

 

 

Ian Foley is the Vice President of RevGen’s Analytics & Insights practice.  He is driven by deriving business value from data and has spent his career helping clients develop the capabilities to do just that.

 

 

 

Noah Benedict leads RevGen’s Digital Enablement practice.  He is passionate about using technology to advance business and empower his clients to embrace new opportunities.