Across industries, AI is being used in all manner of ways to increase operational efficiencies, lower costs and improve customer experience. Just use a media streaming platform such as Spotify and Netflix and you can see AI in action, as the algorithms analyze your user behavior, and that of people with similar behavior, to recommend new TV shows, films, and music. But these examples, impressive as they are, solve simple use cases with simple AI algorithms.
What is AI doing in more complex industries, with more complex challenges and use cases to solve, where much more is at stake? The financial sector is one that comes to mind, transportation another. But perhaps more complex than both of these: communications. Connecting everyone, connecting everything, everywhere, at any time, on demand, is an enormously complex task, with equally complex infrastructure and technology. AI helps to solve the challenges along the way, but it doesn't do so with one broad, general brush. There is no one-size-fits-all general AI that will solve all problems.
CUSTOMER FOCUSED
Elena Fersman, Director of Artificial Intelligence at Ericsson Research, says the company's approach to AI is specifically focused on solving the challenges of its customers and the telecommunications industry for many years.
Our AI does not power talking cylinders, create art experiments, label photos or recommend movies and music. It is not a general-purpose tool intended for any use case imaginable, and we don't have brand teams think of cute names for it we aren't interested in any of that, Fersman says. At Ericsson, we have been working with AI for more than a decade and take a different approach. We develop AI to solve the right problems: the right problems for our customers, and not problems outside of those. We are embedding AI deeply throughout our portfolio and services specific to use cases that can scale to all our customers.
Because of this approach, we don't sell or market AI as a solution in itself. It comes with what we offer, as standard. I think, perhaps, this has led to a perception that in some way we may not have been associated with AI in the same way as companies who focus only on AI. But for the specific needs that we focus on, our AI expertise and leadership is unsurpassed. We have proven focused industry expertise in AI, so in that sense we are an AI company.
SIX CHALLENGES
It's with this laser focus on solving the right problems, not all problems, that Ericsson has identified six specific challenges for communications service providers that, when addressed with AI-embedded solutions, add significant value:
Ensure network performance
Improve energy efficiency and meet sustainability demands
Turn operations into an efficient business enabler
Realize the value from cloud and a virtualized core
Create an outstanding, future-proof customer (user) experience
Bring out the most from existing infrastructure
Focusing on these six specific challenges for our customers allows us to be very concentrated when developing and embedding AI solutions that truly add value, Fersman says. For example, to improve energy efficiency and meet demands of sustainability, we have developed and embedded an AI solution for automated MIMO sleep in radio base stations that provides 14 percent energy savings when turned on.
When we focus on turning operations into an efficient business enabler, one solution within our Managed Services Offering the Ericsson Operations Engine is able to predict KPI degradation in the Radio Access Network, and now more than 70 percent of KPI degradation incidents are prevented with data-driven operations.
The fact is, for each one of these six specific challenges, we have multiple live AI solutions operating in live networks for our customers that are already adding real value, whether through minimizing costs, reducing incidents, increasing efficiencies, curbing energy consumption, or improving user experience. The list goes on and it is something that I am very proud about.
THE FUTURE
So, what does the future of AI look like at Ericsson? Fersman says it's about solving complex problems now, and a broadening AI mind in the future. The more complex problems that we can solve now with real and tangible value for our customers, the more that will add to a broader AI implementation across our portfolio as we increasingly make these AI-powered solutions work holistically across the network, Fersman says. Imagine networks that run themselves, optimize themselves, solve problems autonomously. That's where we are heading, and we are going there with speed.
Ericsson: AI by design
Ericsson Operations Engine
Ericsson gated report: Supercharging customer experience through AI and automation
Q&A WITH ELENA FERSMAN ON ERICSSONS AI DIFFERENTIATORS
Q: What sets Ericsson apart in the telecommunications industry with Artificial Intelligence?
Fersman: We have five main Ericsson AI differentiators that truly set us apart from the competition: Telecom & AI expertise; AI-ready technology; AI use case co-creation; unique access to data; and an AI-powered portfolio.
Q: Let's drill down into each of these five starting with Ericsson's Telecom & AI expertise.
Fersman: Ericsson AI is built on a strong foundation of combining deep telecom domain expertise, data science and AI knowledge. These skills always work hand-in-hand, close to customer problems, and collaborating to solve them. This means that we know what the right problems to solve are, how to solve them and what a good solution looks like.
Q: And what about AI-ready technology?
Fersman: Ericsson's technology is AI-ready from RAN, to centralized clouds, core and operations to customer-facing areas such as BSS and customer analytics. We have made architectural decisions that mak
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Across industries, AI is being used in all manner of ways to increase operational efficiencies, lower costs and improve customer experience. Just use a media streaming platform such as Spotify and Netflix and you can see AI in action, as the algorithms analyze your user behavior, and that of people with similar behavior, to recommend new TV shows, films, and music. But these examples, impressive as they are, solve simple use cases with simple AI algorithms.
