Innovation Your Teams Should Drive AI Adoption — Not Senior Leadership - Sun and Planets Spirituality AYINRIN

 Innovation

Your Teams Should Drive AI Adoption — Not Senior Leadership - Sun and Planets Spirituality AYINRIN

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Summary.   

Whenever a new technology comes along, large companies think you need to appoint a designated senior leader — a “czar,” in popular parlance — and it will get taken care of. This, however, is a mistake. The process usually starts when teams are pitching leadership on wildly optimistic and conflicting use cases, and the board, excited but unsure how to proceed, puts some poor, unsuspecting soul in charge of the whole thing. It very rarely works out. Instead of a centralized top-down structure, AI implementation is owned by teams close to the work, which can mean a broad set of stakeholders providing real-time feedback. The piece details how Verizon has tried to implement this approach of letting innovation happen at the frontlines and supporting it at the core.

Artificial intelligence has been around for a long time, but it is breaking out in a big way right now. As companies start to appreciate the almost boundless potential of Generative AI, they have begun to fast-track existing AI projects and are starting new ones in all areas of the business, including supply chain, HR, sales, marketing.

Leaders are grappling with managing it all. It’s time for businesses to centralize control under a seasoned executive, right?

Wrong. Whenever a new technology comes along, large companies think you need to appoint a designated senior leader — a “czar,” in popular parlance — and it will get taken care of. In recent years, we’ve seen how this applies to the metaverse, blockchain, and now AI. At many enterprises, the decision to appoint a senior point person to oversee the adoption of a new technology is practically an automatic at this point. It’s also often a big mistake.

The process, as I’ve observed it, usually starts when the board hears about a hot new technology. Teams are pitching leadership on wildly optimistic and conflicting use cases, and the board, excited but unsure how to proceed, puts some poor, unsuspecting soul in charge of the whole thing. It very rarely works out. After struggling with the technology for a few years, with minimal results, the leader who had been charged with charting this bold new course often departs the business. While this is often greeted with great surprise within the company, it shouldn’t be. When these leaders fail, it’s because they don’t have any idea how the company runs on the frontlines, and at the level where the ideas are actually put into practice.

So, what should companies do instead?

Lead from the Frontlines

In developing applied technologies like AI, leaders must identify opportunities within workflows. In other words, to find a use for a new piece of tech, you need to understand how stuff gets done. Czars rarely figure that out, because they are sitting too far away from the supply line of information where the work happens.

There’s a better way: instead of decisions coming down the chain from above, leaders should let innovation happen on the frontline and support it with a center of excellence that supplies platforms, data engineering, and governance. Instead of hand-picking an expert leader, companies should give teams ownership of the process. Importantly, this structure lets you bring operational expertise to bear in applying technology to your business, responsibly and at scale and speed.

That’s how we do it at Verizon. Instead of a centralized top-down structure, AI implementation is owned by teams close to the work, which can mean a broad set of stakeholders providing real-time feedback.

For example, I want my supply chain person to figure out the best use cases. They have insights that a czar — who is typically focused on strategy and revenue and growth— simply doesn’t. People at the functional level recognize the challenges of getting things done efficiently and effectively. They can quickly spot the tools that work best. These frontline groups, which own the budget and the service level agreements, must live with the end result. They will often focus their attention on projects that can benefit both these metrics, which means you get use cases that drive measurable outcomes.

Harness AI

Right now, Verizon is partnering with outside companies to fine tune our models as we apply AI in three areas: 1) In operations, on large language models to help with cognitive and computational tasks, 2) for the network, on using AI in buildout design, capacity prediction, and power amplification to help automate and speed up network response, and 3) in customer care and sales, to help with marketing and personalization.

We’ve been at this a while and have learned from our mistakes.

For example, for the last decade plus, we had a centralized, catchall way of solving customer service issues. But as we learned from our frontline workers, information of all kinds — like how to work a specific device, redeem a promotion, address a specific billing concern, answer a question about network builds, etc. — can be hard to find and overly complicated, which adds to the already heavy burden on our service representatives. We have hundreds of different devices we support, roughly 100 different promotions going at any given time, and our customer care teams are expected to know all of it.

That’s where we’ve turned to AI to relieve some of that cognitive load.

Consider an example: Let’s say that a customer calls in with a question about their mobile promo and wants to better understand what options may be available to them for internet service, too. The representative would likely search for, and pull up, multiple documents across many screens outlining all available promos and configurations for home internet. Now, imagine having 10,000 of these documents and a single AI search bot — a co-pilot, really — that can tell you what you need to know instantly, personalized for that customer. That’s what we’re testing now.

Another example: Previously, the product development cycle was a series of steps: develop requirements, build software, and release it. Then, take feedback and update in the next IT release. Now, AI is constantly learning and updating in near real time as we go so the turnaround is much quicker. Today, AI gets trained based on our agents’ interactions. We get real time feedback, so the evolution of AI is in sync with the use of AI on the frontlines

We are investing only in areas where we’ve measured progress and AI not only informs us in real time, but it also improves our KPIs.

Sharpen Performance

Our results are improving. Using the AI search bot, our answer accuracy rate is on par with our human accuracy rate, but we believe we can bring it to 99% accuracy. And it continues to improve every day, but the best measure of success will be improving our net promoter scores, which is our goal this year.

Similarly with sales, we’ve already started to implement an AI tool to help us anticipate what the customer might want and proactively provide them with options. We capture a significant number of data points to help us better serve our customers and based on that, we’re able to make over 100 different predictions that help us give them a far more personalized experience — like the best network and content options based on their interests, or a prompt on a promotion that’s unique to them based on their tenure. When we can accurately and proactively identify what a customer needs or wants, we can resolve questions on the first call.

This type of proactive work has already helped us increase our sales conversion rate by over 6%. That’s everything from new subscriber signups to adding exclusive benefits and upgrades to higher-tiered plans. AI is letting humans do what humans do best, by letting machines do what machines do best.

And, to punctuate my point, we’ve empowered our frontline teams to guide us on how AI is best used to help them reduce cognitive load and provide efficiencies in the way we serve our customers, so that they can focus on human interaction, empathy, and exceeding the customer’s expectations. There’s no czar in that customer transaction. There are only decisions at the point of contact using the expertise of our frontline employees. The performance results are then sent up the chain so the whole organization can assess the process and fine tune across the enterprise. We’ve decentralized the decision process in a quest to find the best results.

Artificial intelligence reminds me of the internet era in the early 2000s. Everyone had a dotcom they stood up; but not all did really well. It took more than adding an internet SVP position and a .com to your name. Today, every company wants to use AI, but for long term success, it’s going to take more than just putting an executive in charge. At Verizon, the success and the failure of AI is owned not by a czar but by an empowered set of stakeholders who can see results and customer engagement and feedback as it comes in. Yes, as some have advocated here recently, organizations should have a clear vision for AI — and we do — but as history has shown us, companies that harness the talent of their frontline employees to create an operational edge win the day. This time, with AI, it will be no different.

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