Dispatches From the Front Lines of the Generative AI Revolution

In an era where artificial intelligence (AI) is rapidly evolving from a futuristic vision to an essential business tool, generative AI (GenAI) stands out as a beacon of transformative potential. As organizations across the globe grapple with the implications of this burgeoning technology, business leaders are seeking insights into its practical application and strategic integration.

Enter Gokula Mishra and Rob Holland, seasoned executives with a wealth of experience in digital transformation, data, and AI. In their quest to demystify the GenAI landscape for today's business leaders, they have engaged in candid discussions with three visionaries at the forefront of this revolution: Vikram Mahidhar, Glenn Hofmann, and Dan Waibel.

Mahidhar is an operating partner at Apollo and a veteran at the intersection of AI and business strategy; Hofmann is a veteran data and analytics leader and former CDO at New York Life; and Waibel is Chief Data Officer at HPE (Hewlett Packard Enterprise).

Together, they explore GenAI’s ability to impact business performance, early use cases that have demonstrated value, the human role in realizing the full potential of this technology, and some key insights on setting your program up for success.

Let business value guide your priorities

Ever since ChatGPT 3.5’s release on November 30, 2022, the business world has been ablaze with excitement about the potential for GenAI to transform how we incorporate intelligence in our ways of working. It really has been “a perfect storm,” describes Waibel, “...particularly with the introduction of GenAI, technology has been rising on the agenda.” Continuing, he states, “There was a top-down push, but there also was a push bottom-up from technologists across the company.”

With all this interest, there has been an explosion in experimentation within enterprises across every industry. So, one may wonder where to start. At the heart of the GenAI conversation lies a fundamental question: How does it drive business impact?

Mahidhar underscores the significance of aligning GenAI initiatives with clear and measurable business objectives. "It has to result in EBITDA or revenue growth with a clear line of sight," he asserts, adding, "We ask (our executives) to sign up for the number that is associated with the use case."

His perspective is echoed across the executives we spoke with, signaling a shift from experimental AI projects to those with tangible outcomes. The message is clear: for GenAI to be more than an impressive tech showcase, it must be leveraged to create real economic value.

As Hoffman notes, "It’s got to be big enough to make a difference in the financials of the company. If it’s not big enough, it will be difficult to stay motivated on it." This requires a focused approach where GenAI is not a mere tool, but a core driver of business performance.

Selecting the right use cases

To crystallize the opportunity that GenAI has to drive business performance, McKinsey & Co. has recently published research stating that the disruptive technology represents US$2.6 - US$4.4 trillion in economic impact. They believe over 75% of that value will be derived from four key process domains including:

1. Customer Operations

2. Marketing and Sales

3. Software Engineering

4. Research and Development

“Why these process areas?” you might ask.

The answer lies in GenAI’s ability to process large amounts of data more efficiently than humans, learn from this data to improve over time, and generate outputs that would either take humans much longer to produce or might not be possible at all.

It is about augmenting human abilities, automating routine tasks, and opening up new possibilities for innovation and personalization. Our leaders have pinpointed several active use cases where GenAI is not just a supporting actor but a leading force in driving efficiency and growth which serves to validate McKinsey’s findings.

Customer Service enhancement is a notable opportunity as Hofmann calls out the potential for GenAI to assist agents in providing quicker and more accurate responses. “Everybody’s favorite use case regardless of the industry, if they are consumer-facing, is customer service," Glenn observes.

Speaking to the tangible benefits, Glenn elaborates, “So, driving down call-times which is directly correlated to cost, right? And then the second (impact) there, which is not cost, but consumer satisfaction which is an important metric.”

Waibel explains how GenAI is augmenting existing propensity models in the marketing domain where they are, “building dynamic content for websites, marketing collateral, personalized outreach, and lead identification.”

Continuing, he describes how they are extending GenAI in the sales process to assist with, “negotiations, product queries, and understanding our installed base and preparing for customer meetings in a very dynamic auto-generated type approach which allows us to quickly scale up our sales team and really become hyper, hyper-focused on individual customer experiences.”

Software development is another target-rich environment to harness the transformative power of GenAI where its integration has shown to significantly increase the productivity of engineering teams. As Mahidhar has seen, “There is significant promise. Now, is it 30-40 percent as advertised by leaders in space?”

He is quick to point out that as early as we look to find bona fide proof points in the market. This is a fair observation when you consider the multiple factors that can determine the immediate impact of software development co-pilots like the quality of the existing code base, quality of documentation in the code, modularity of the code, production dependencies, etc.

Interestingly, multiple leaders described another set of high-impact GenAI use cases in a process area that has received much less attention up until now - supply chain! Mahidhar points out, “As you can imagine across 50 different portfolio companies, we have a huge spend queue… There are two metrics that really matter. One is working capital, and the second is a pricing parity.”

Placing GenAI on top of traditional data extraction and visioning techniques, he describes the opportunity for line managers or merchandising agents to be empowered like never before when negotiating prices or terms in their contracts. Further reinforcing this point Waibel highlights, “We've built dynamic supplier negotiation playbooks. We've thought through how to leverage AI to optimize our entire supply-chain process.”

Driving the required cultural transformation

As anyone who has deployed new technology knows, simply getting a great application to market does not guarantee a successful return on investment. In fact, creating sustained business value requires very strong alignment among executives and team members who will be impacted by the job reinvention that comes with GenAI programs.

Beyond a typical change management program, our leaders all mentioned this more as a cultural transformation. As Mahildar puts it, “At the end of the day, what we're doing here is capturing intelligence of your best-performing people and turbocharging with a lot of data.” Continuing, he shares, “Earlier, my best salesperson had the ability to connect the dots between the data and decide the next best action.

Now, I've encapsulated this into a tool that is available to every salesperson. So, my average intelligence goes up and the network effect can be massive!”

Inherent in this case, and the many others mentioned earlier, is a need for knowledge workers to reimagine their roles working in concert with supportive intelligence. However, the onset of GenAI in the workplace can elicit a spectrum of emotions, from the excitement of innovation to the anxiety of obsolescence. This fear often stems from the misconception that AI might supplant human roles rather than support them.

To counteract these fears, the leadership must communicate the intent and benefits of AI clearly. Creating a culture that values data literacy and sees AI as a partner rather than a threat is essential. Leaders must foster an environment where employees can experiment with AI, learn from it, and ultimately, excel with it.

By doing so, they can convert apprehension into ambition and lead a cultural shift that embraces AI as an integral part of the business fabric. Hofmann emphasizes this point well - "Describe it as a tool that will make them more productive and eliminate some of the tasks in their job that are of lower value, that they probably don't like to do in the first place.”

Other keys to setting your GenAI program up for success

GenAI programs bring a unique set of considerations that must be addressed when launching a new program. As such, they call for a deliberate governance structure. As Waibel puts it, “Establishing a governance structure that balances (objectives) and makes sure we have the right guardrails in place is very important. And that includes privacy, security, ethics, and compliance. All those areas are well represented in the governance structure that our teams put together.” Starting with this level of involvement will certainly be of benefit as greater regulation in the AI space is enacted.

It is no surprise that the quality of data came up in our conversations, as we all know the adage about “bad data in, bad data out.” Relating to GenAI, the conversation highlights the importance of data labeling, the process of identifying raw data (images, text files, videos, etc.), and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it.

“People underestimate the amount of labeling that has to happen, and that obviously, has to come from experts in that particular area, that's a bit of work,” advises Hofmann. Continuing, he suggests, “So that you can move relatively fast, you may restrict the number of documents… containing the scope in both the type of topics you're tackling and the amount of data that's needed.”

Given that we are all at the beginning of the GenAI revolution, there was a consensus that finding talent can be a difficult obstacle. However, our leaders each indicated that this is something that can be turned into an advantage by developing your teams in this new domain. Offering his approach, Mahildar shares, “I would definitely lean on AI talent who have developed previous AI applications because they know what not to do.

I'm a big believer of that, and I'm one of them.” Further committing to this point, he expands, “Start training and exposing your internal talent to all these techniques. They will make their fair share of mistakes, but one or two years from now, you will have a much deeper bench.”

In conclusion, the Generative AI Revolution is not on the distant horizon — it is here, reshaping the business landscape with breakthroughs happening every day. The insights from industry leaders like Vikram Mahidhar, Glenn Hofmann, and Dan Waibel are instrumental in carving out a path for businesses to harness the full potential of GenAI.

For companies to thrive amidst these groundbreaking developments, they must prioritize business value, select impactful use cases, drive a supportive cultural transformation, and establish a strong foundation for their program focusing on governance, data, and talent. As GenAI continues to evolve, it promises not only to enhance current processes but also to unlock untapped opportunities, urging us to envision a future where AI is a seamless extension of human capability.

About our authors:

• Gokula Mishra: Chief Editorial Reviewer of CDO Magazine editorial board, former VP of Data Science and AI/ML, Direct Supply and Head of Data Analytics and Supply Chain globally at McDonald’s, brings 30+ years of Data analytics and AI/ML experience across many industries in creating lasting business value internally and externally.

• Rob Holland: Co-founder and Chief Customer Officer of Provision Analytics, Former VP of Digital Transformation at WM and North American Digital Experience Practice Leader at Capgemini, with 25+ years of experience helping companies leverage technology to drive growth and profitability.