By Kamesh Chelluri and Narendran Sivakumar
Demand for digital content and services is flourishing, but for those who build and maintain the underlying data networks, monetizing this growth remains challenging.
More often than not, service revenues bypass telecom operators, landing instead with digital natives such as Apple, Netflix, Google and other players who provide their services ‘over-the-top’ of the infrastructure others have paid for.
Losing ground to digital businesses could lead telcos onto a slippery slope towards becoming mere utility players – or ‘dumb pipes,’ in industry lingo. To avoid this, telcos are under growing pressure to recapture a place higher up in the value chain.
To set themselves apart, it will be critical for telcos to embrace data insights and automation.
It is these forces that we see driving the emergence of a ‘cognitive telco’: a firm which makes use of joined-up intelligent systems that can sense, infer and reason, and respond to data.
Keeping it real
While there is no ‘one size fits all’ template for transforming a business into a fully mature cognitive enterprise, we believe there is a framework to guide C-suite decision-makers in implementing a top-down, company-wide and holistic approach.
The journey should begin with an objective assessment of where the organization currently stands in the broader cognitive enterprise maturity framework, as shown in the diagram above.
We have looked at these various stages in more detail here. But in summary, the framework increasingly reduces the level of human intervention to the point where network and operations become ‘zero-touch’: they run themselves and adapt autonomously to changes in the environment, constantly learning and improving.
The route will be different for each company, and arriving at ‘zero touch’ will come sooner for some than for others. It is important to establish a timeframe for this transformation, and set clear milestones for the journey.
Once the fundamental route has been agreed, the next question is which organizational strategy to opt for. There are four fundamental approaches. The choice is not ‘all or nothing’ but more ‘mix and match’ to adapt to each organization’s needs.
1. Uniting behind a common cause
The first step many telcos take is a purpose-centric approach. This typically starts with a problem the telco wants to solve using AI and automation. More often than not, it’s customer care: a low net promoter score, high churn, lots of complaints.
For example, a UK-based telco wanted to turn around its languishing net promoter score (NPS) based on network and customer analytics. More efficient call routing and shorter waiting times, automatic alerts and improved problem resolution boosted the NPS by 21 points, while also creating operational efficiencies.
Dealing with real-world issues is a good way of building the business case for cognitive. It works best when there’s already a common vision for AI strategy, with relevant policies and structures in place, to avoid duplication or divergent approaches in the organization.
2. Letting the experts do the legwork
Another option is to create a centre of excellence (COE). Here, a core team of interdisciplinary experts does all the legwork for the rest of the company, providing the governance framework and guiding cognitive transformation projects.
For example, a multinational telco based in the UK has created a COE to ensure a coherent approach to AI across the company. The COE is responsible for creating common platforms, tool recommendations, training, supplier partnerships, reference architectures, guidebooks, and also offers a help desk.
3. Reaching across silos
The platform approach involves creating dashboards and models for cognitive automation which adapt as the organization’s maturity increases. Like the COE, this is a company-wide undertaking, aiming for a common cognitive platform.
The platform approach can also help pull information together across data silos where these exist, overcoming internal boundaries. While it is a top-down approach, rolling it out has to be done with great care, especially where boundaries are very entrenched.
This approach is good for telcos that have already got the fundamentals for their transformation in place, in particular for data governance, quality and stewardship.
4. Handing over the reins
An evolution from the COE, the Data Custodian role involves creating a separate division to take ownership of data, AI, machine learning and automation. This division can then take care of an entire spectrum of decisions and strategies to be deployed across the entire operation. In some telcos, the Data Custodian may be the Chief Data Officer, if this role already exists.
This approach is suitable for network operators aiming to make the leap from process automation to cognitive at a fast pace, but where skills are a challenge or data poses a regulatory or business risk, and needs to be managed tightly.