Data Analytics & AI

Using AI for new paradigms in transformation

With EBITDA margins down from 25% to 17% and cash-flow margins down from 15.6% to 8%, telcos urgently need to address core structural issues: becoming digital first operators, radically cutting cost bases and driving top line growth. These problems are not unique to the telecommunications industry — we see the same structural issues with our clients in the banking and insurance sectors.

Luckily, telco operators are well placed to innovate because they preside over enormous volumes of unstructured data that contain detailed insights into customer, process and operation behaviors that can expand the scope for digitization and automation whilst also providing an invaluable source of insight and data on customer needs. In the next few years, Gartner has claimed, organizations will actually be valued on their information portfolios and data assets.

Chief among these sources of unstructured data is human communications data. That is the conversation an operator is having externally with its customers and internally in its operations channels.

When analyzed thoroughly, these conversations prove to be exceptionally valuable and contain critical business information. Every conversation is typically a description of a manual process, a request for a task to be executed and a rich description of your customer’s needs and experiences. And whilst they are an essential component of day-to-day performance, commercial conversations remain one of a business’s biggest operational costs and causes of inefficiency.

Recent research at re:infer has found that in enterprise service business, roughly 50% of FTE time is spent in the mechanics of communication; emailing, keying in data and servicing customers. This operational model that depends on so much manual labor is not sustainable.

Artificial intelligence, however, is providing a way forward. Recent academic breakthroughs in deep learning and natural language understanding, are enabling a new paradigm of intelligent automation in the enterprise. Telecom operators can leverage machine intelligence and gain a new perceptual ability to listen and act on communications data in real-time, making service resolution 20x cheaper, 100x faster and 1000x more comprehensive.

Moreover, the use of unsupervised machine learning allows for discovery of patterns, themes and opportunities in the largely unmined unstructured communications data a telecom operator is presiding over. Rather, than hypothesize where and when to transition away from legacy systems, or complex offshore shared services, management can accurately quantify and measure every manual process running through the organization. Thereby allowing for ROI-positive automation and digitization initiatives that can be optimized in real-time.

On the advent of this new era of enterprise automation, CSPs have an opportunity to harness existing data assets in AI deployments that can provide the scaling economics of digital-first players like Amazon and Google.



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    About The Author

    Chief Revenue Officer - re:infer

    Stephen is the Chief Revenue Officer for re:infer – an artificial intelligence business formed at the world leading UCL AI Research Lab. Stephen is a technology entrepreneur having founded and exited business in the e-commerce and on-demand transport space. More recently, he was Entrepreneur in Residence at a family office and adviser at private equity house Resolve Capital. Stephen is passionate about working with executive leaders who are thinking about about digital transformation design and leveraging cognitive automation technologies.

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