Telco use-cases for AI: A simple categorisation model

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.

New: Workshops on Enterprise Cellular & AI/Blockchain in Telecoms, May 30-31

I'm delighted to announce a new collaboration:

Rethink Research & Disruptive Analysis announce joint workshops on Enterprise Cellular Networks, and AI/Blockchain in Telecoms, London May 30th-31st

At the end of May, two of the leading independent thinkers in telecoms research will jointly be running small-group interactive workshops in London, addressing two of the hottest topics in telecoms technology and business models:

• 30^th May: Private Cellular Networks for Enterprise, IoT and Vertical Markets
• 31^st May: Use-cases and Evolution Paths for AI, Machine Learning and Blockchain Technologies in the Telecoms Sector

Each day will have a maximum of 30 attendees to ensure a high level of discussion and interaction. We expect a diverse mix of service providers, vendors, regulators and other interested parties such as enterprises, investors and developers. 

The sessions will combine presentations, networking opportunities, and small-group interactive discussion. Rethink Research’s Caroline Gabriel, and Disruptive Analysis’ Dean Bubley, will be the leaders and facilitators. Both are well-known industry figures, with many years of broad communications industry analysis – and outspoken views – between them.

The two events will run as separate standalone sessions, but there will be common themes and approach across both, to benefit organisations with an interest in both topics.

Enterprise & Private Cellular Networks, May 30th 

The first day will cover the rising need for businesses of many kinds to control their own, well-managed, wireless connectivity solutions. The growing use of mobile devices and the emergence of the Industrial IoT means that high-quality – often mission-critical – networks are required for new systems and applications.  

These can span both on-premise coverage (eg in a factory, office or hospital) and the wide-area (eg for smart cities or future rail networks). It is unclear that traditional mobile operators can or will be able to satisfy all the requirements for enterprise coverage – or assume legal liability for failures. Some enterprises will want to have full control for reasons of security, or industry-specific needs.

Among the topics to be discussed are:

• Key market drivers: IoT, automation, mobile workers, industry-specific operational and regulatory issues, diffusion of wireless expertise outside of traditional telecoms providers
• Evolution of key enabling technologies such as 5G, network-slicing, SDN, small cells and enterprise-grade IMS cores
• Regulatory/policy issues: spectrum allocation, competition, roaming, repeaters, national infrastructure strategies and broader “Industry 4.0” economic goals
• The shifting roles of MVNOs, MVNEs, neutral hosts and future “slice operators”
• Spectrum-sharing approaches, including unlicensed, light-licensing and CBRS-type models. Also: can WiFi run in licensed bands?
• Numbering and identity: eSIM, multi-IMSI, liberalised MNC codes
• Commercial impacts, new business model opportunities & threats to incumbents
• Vendor dynamics: Existing network equipment vendors, enterprise solution providers, vertical wireless players, managed services companies, new industrial & Internet players (eg GE, Google), implications for BSS/OSS, impact of open-source

(I've covered various of these themes in previous posts and presentations. If you want more detail about some of my thinking, see links here and here. I'll include links to Caroline's thoughts on this in subsequent posts. We will be going into a lot more depth in the workshop itself).

AI & Blockchain in Telecoms, May 31st 

The second day will consider the specific impact on the telecoms sector of two of the hottest new “buzzword” technologies in software: Artificial Intelligence (and its siblings like machine-learning) and Blockchain / Distributed Ledgers. Both have already received more than their fair share of hype: but what are the realistic use-cases and timelines for adoption? What problems do they solve, and what new opportunities do they create? Are they just re-branding exercises for “big data” and “distributed databases” respectively, when applied to telcos?

(I've been covering these areas as part of my "TelcoFuturism" research, including presenting on Blockchain at a recent TMForum event (link) and at Nexterday North last November, plus thinking about various AI intersections with telecom trends such as 5G (link). Caroline has done a large amount of work on AI / Machine Learning).

This day will benefit attendees from the telecoms industry looking at new developments; as well as  those from the AI/blockchain mainstream interested in specific applications in the telco sector. It will include some basic “101” introductions so that delegates from both sides can be sure they’re speaking each others’ language & decode the jargon.

Among the topics to be discussed are:

• Understanding and categorising the types of AI (machine/deep learning, image recognition, natural language etc)
• Introduction to blockchain concepts and the complexities of “trust”
• Review of telecoms industry structure, key trends and important components of network/IT systems
• Where will AI have the largest impacts for telcos? Improving customer insight & experience? Improved network operations & planning? New end-user facing services such as chatbots or contextually-aware communications? B2B, B2C, or B2B2C platforms?
• Mapping the possible use-cases for blockchains in telecoms, and current trials / status of projects – from micro-transactions, to roaming settlement & fraud prevention, data-integrity protection, or smart contracts for NFV systems
• Impact of 5G & IoT for both AI and BC
• Risks and challenges: regulatory, privacy, new competitors?
• Vendor and supplier ecosystems and dynamics: new entrants vs. adoption by established providers

Reserve your place today 

Both workshops will take place at the Westbury Hotel in Mayfair, central London [link]. They will run from 9am-5pm, with plenty of time for networking and interactive discussion. Come prepared to think and talk, as well as listen – these are “lean-forward” days. Coffee and lunch are included.

Fees for attending one day: £795 / US$995 / €930 + UK VAT of 20%

Fees for attending both days: £1395 / US$1750 / €1650 + UK VAT of 20%

Reserve Now: Select Your Choice of Workshop Days

Enterprise Cellular May 30th (inc 20% VAT) £954.00 GBP AI & Blockchain May 31st (inc 20% VAT) £954.00 GBP Both Days 30/31st May (inc 20% VAT) £1,674.00 GBP

Payment can be made either credit card or Paypal, or by invoice / bank transfer: please email me at information AT disruptive-analysis DOT com, for payment-request by email or with purchase-order details. Please also contact me for any more information.

5G vs. AI

Last week, I was in Mainz in Germany, at a European telecom regulator's workshop about spectrum and technology evolution for future 5G networks. (link). It was a very formal event, with most people from government agencies, technology standards bodies and telcos, broadcasters and the like. Some industry verticals such as energy, rail and automotive were also represented. I was one of the few analysts there - and there were no journalists, I think.

This week, I've been in San Francisco, at a very different style of event, about Artificial Intelligence. (link). It was a multi-streamed conference, with a small expo area, a press office, lively panel sessions - and a selection of Silicon Valley's finest, from VCs to Google to Uber to innovation outposts of GE and Airbus, as well as countless software startups and enterprise IT folk.

I was there as part of my TelcoFuturism research effort (link) where I'm looking at the impact and opportunities of technologies such as AI, blockchain, drones, AR/VR, robotics and quantum computing on the telecoms industry. I was interested to see both internal applications of AI in running telcos' networks and IT systems, and also in terms of scope for new services and driving connectivity.

It's that last thing that struck me most. There is a huge gulf between the expectations of the 5G community (which talks endlessly of self-driving cars and robots using ultra-high performance mobile networks, or "massive Iot" networks of sensors and actuators) and the AI and robotics community (which doesn't).

I asked quite a lot of people developing both AI software (which is a huge diversity from deep-learning, to image-processing, to personal assistants and bots) and hardware and applications (autonomous vehicles, GPUs etc) how important networks were to their innovations.

The general answer: not that much. They want as much processing done on the device itself as possible, not controlled remotely or from the cloud, especially where anything safety-critical is involved. A speaker from Nvidia showed a board that is essentially a vehiclular supercomputer, using inputs from cameras, engine monitoring, LIDAR and all sorts of other sensors to work out what to do. A self-driving car is not going to ask the cloud for permission to brake in an emergency. There is a recognition that networks are not ubiquitous or completely reliable, so they need to act independently - autonomous means autonomous. This also means much lower latencies.

Other companies are working on facial/emotional recognition systems that can be embedded in smartphones, or even directly in camera hardware, without the need for an OS - or sending data to/from the network all the time. The speaker from GE said that aircraft engines may generate terabytes of data during a flight - but have enough onboard intelligence to do analytics, optimisation and even self-maintenance in flight. That doesn't mean they won't also transmit telemetry data via satellite (or maybe air-to-ground 5G in future), but that likely won't be for realtime control.

The line from Nvidia's website (link) that should be read carefully by 5G advocates is this: 

"With a unified architecture, deep neural networks can be trained on a system in the data centre and deployed in the car"

However, that is not to say there is no requirement for connectivity. There will be a lot of data flowing around, generated by sensors or user/device behaviour, fed back to a machine-learning system and analytics function to help develop, train and improve future algorithms and models. But that doesn't need to be realtime - it can wait until the car gets home, or the handset dips back into 4G/5G/WiFi coverage. Vehicle-to-vehicle data flows will be useful in helping build a better picture of the context, but that is a secondary consideration at the moment, and also may well not involve cellular connections.

There will also be a need for non-critical information to use the network, such as mapping and navigation data for vehicles, entertainment for passengers, or advertising overlays for an AR headset. In an IoT context, the irrigation data from one farm's sensors will implicitly be helping train the AI system used to manage other locations' (and maybe even other industries') systems.

I think there is a gulf in understanding between telecoms and AI communities. I don't think many of the 5G standards and verticals discussions factor in the rise in GPUs at the edge/in devices, for a lot of "heavy lifting". It often won't need to be done in the cloud, or even mobile edge computing nodes. Some of the VCs seemed to get "connectivity" a bit better, but even some of those seemed unrealistic about 5G timelines, deployment and capabilities.

Clearly there will still be many needs for huge volumes of 4G/5G Internet connectivity from smartphones, streaming video for various applications and a lot of genuine IoT requirements. There is definitely an ongoing business model for enhanced mobile broadband. (Sidenote for another post: Home WiFi is also going to be mesh and AI-enabled by companies like Google and Amazon).

So... I think that some of the expected critical IoT and massive IoT uses for 5G are being overstated. There may well be a need for more mobile uplink data to help train deep-learning systems and other analytics tools. But that often doesn't need to be realtime. While they might need software updates from the cloud, a lot of endpoints will be smart enough to make their own decisions and analysis without relying on he network.

I also think that in the 3-5yr timeframe for mobile and IoT 5G deployments to have broad coverage, AI technology (both software and hardware) will have progressed far beyond even where it is today. There are so many branches of AI, from deep-learning to image recognition to bots - and these have much tighter couplings with the enterprise IT systems and end-devices, than the network. 

Meanwhile, the telecoms industry is looking forward to exciting 3-year processes to define "agenda items" in interminable regulatory committee stages, and regional sub-committees, before the next ITU World Radio Congress in 2019, to debate 28GHz vs. 32GHz bands, or work out how to "harmonise" 700MHz for 5G against incumbent desires of broadcasters and others.

At the moment, in the new strategic battleground of Networks vs. AI, I suspect that Moore's Law and deep-learning mostly favours the robots.

This post is from Disruptive Analysis' new TelcoFuturism research programme. This looks at strategical implications of intersections between the telecom/network industry and other adjacent trends. If you are interested in more detail about this, or to arrange an advisory briefing or keynote speaking engagement, please contact information AT disruptive-analysis DOT com.