The coming years will see the application of AI technology across
all sectors of the economy and life. The telecoms industry is no
different. Although I’ve been commenting on telco-sector AI in the context of “TelcoFuturism”
for some time (link), and co-ran a workshop on it in May 2017 (link), the last few months
have seen a notable upswing in interest. I’d say that the public use-cases now
seem to be significantly in advance of those for blockchain, in terms of potentially-transformational
technologies.
That said, it can still be hard for many executives to grasp
exactly what is likely to change, and when, for AI/telecoms combinations. This is highlighted by the surge in
AI-related panels, presentations and even complete streams at industry
conferences – although sometimes I see more interest from generalist AI people
about the telecoms vertical, versus telecom specialists looking at what’s new.
Both sides of the equation have large volumes of obscure acronyms, multi-layered
technology stacks, and complex volume chains – which can mean that mutual
understanding is often confined to narrow niches. AI covers machine- and
deep-learning, language processing, machine-vision and much more. Telecoms
includes vast realms of internal systems and processes that are unknown to most
who are not insiders – domains like core networks, OSS/BSS, network optimisation,
toll fraud and service-assurance are alien to those not steeped in the
industry.
One of the ways I’ve been using to “set the scene” for describing
AI/telecoms intersections is to simplify and categorise the use-case areas. I
count three, possibly four, large “buckets” into which a variety of telecom AI
impacts will fit. These buckets are not based on either specific AI or telecoms technology slices,
but more on understandable business functions and roles:
- Dealing with customers
- Managing operations
- Creating new services
- (External risks)
Within each of these areas, there are many, many sub-sectors
– and also some overlap.
“Dealing with customers” can include everything from voice/text
chatbots for customer-service, through to predictions of which customers are
least-happy and may “churn” to competitors. Where telcos have retail outlets, it
could incorporate various in-store technologies, or it could be about smarter
web-consoles for B2B customers running complex managed services.
“Managing operations” is even more diverse – it could be
fault prediction for network elements, optimising the 100s of configuration
variables for radio networks, spotting fraudulent traffic to international premium-rate
numbers, allocating engineering resources more productively, or protecting
against hackers and malware. There are hundreds of possible uses here, which mostly overlay on top of existing
operational/business support systems (OSS/BSS) See also my recent post (link)
“New services” also spans a range of areas, but broadly
splits between AI-enabled and AI-enabling services. An AI-enabled service could be
a local-language voice assistant added to a cable operator’s set-top box or
remote control. Or it could be the provision of integrated “smart city” solutions
including video-cameras and security analytics. AI-enablement could include
offering “edge” servers for hosting local processing, milliseconds transport-time
away from a device, or it could be the provision of anonymised bulk data for
others to apply algorithms to. Telco opportunities with IoT+AI include both enablement and enabled services, in numerous manifestations.
The “risks” category includes a diffuse set of possibilities
by which AI might harm the telecom industry, or dampen demand for services.
Smarter devices (eg autonomous vehicles) will be able to host their own offline
image processing & route-planning locally, rather than needing realtime
connectivity at 5G speeds/latencies. Another threat could be customers’ smart
assistants renegotiating price-plans on their behalf – after crowdsourcing
millions of conversations to deduce how best to game the retention staff’s
scripts and objections. (Of course in the latter example, the customer-retention
team could themselves be bots). Numerous types of automated “least-cost X” and arbitrage
engines are likely to emerge. Various security risks are also probable here too.
Clearly, using just these four "buckets" misses much of the fine-grained detail. But I find it helpful as a starting point, as most
top-level industry issues apply differently to each.
Consider input data, for
example – for both customer management and operations, telcos have abundant
historical records and ongoing data collection that may generate terabytes per
day. But for the former, privacy considerations often come to the fore in terms
of regulation and risk, while this is far less of a concern for internal operational
data, for example on how the network is running. For new services, almost by
definition the focus is on collecting/processing/transporting new data, rather
than deriving conclusions from existing sets.
This four-way framework is also useful for thinking about different types of ROI model - split broadly between impacting existing revenues, existing costs, new revenues and potential changes to underlying assumptions.
I'll be covering these topics in more depth in various upcoming presentations and reports, as well as looking at other areas of telco-linked innovation such as blockchain, 5G and enterprise verticals. Please get in touch if you would like more detail, or are interested in internal workshops, external support through events or white papers, or are seeking ongoing strategic advisory support.
