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Innovation   |   Your Teams Should Drive AI Adoption — Not Senior Leadership
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Innovation


YOUR TEAMS SHOULD DRIVE AI ADOPTION — NOT SENIOR LEADERSHIP

Many companies appoint a designated senior leader to find ways to integrate new
tech — and that’s a mistake.
by
 * Sowmyanarayan Sampath

by
 * Sowmyanarayan Sampath

April 30, 2024
Juan Moyano/Getty Images
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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...more
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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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   Sowmyanarayan Sampath, Executive Vice President and CEO Verizon Consumer.

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