It’s clear that the digital future will be driven by Artificial Intelligence and Automation (AI&A) – especially in the telecom industry. In the final instalment of our “The future of telco” series, James Thornhill outlines AI&A opportunities and why CSPs should ensure they have the right strategic initiatives to grow.
In this series we’ve already mentioned the difficulties the telecom industry has had in remaining competitive. We explained how historic poor customer service, complex business operations, and financial pressure have negatively impacted the industry’s ability to compete. This is especially true when looking at the track record of communication service providers (CSPs) in comparison with hyperscalers and over-the-top providers who excel in the experience they offer.
The good news is that Artificial Intelligence and Automation (AI&A) can play a significant role in helping telcos gain the competitive edge they’ve been seeking. This is something that many telecom companies have already understood, as the global AI market size in the industry is projected to reach 14.9 billion US dollars by 2027.
The problem is investing for investment’s sake, risking a poor return on investment (ROI) and losing the competitive potential AI&A opportunities promise. So how can CSPs invest wisely to best utilize AI&A for future growth?
How can AI&A improve CSP operations?
Before answering how best to invest, it’s important to understand how AI&A can help telcos overcome the challenges they currently face. There are four key opportunity areas where AI&A can be applied:
- Digital customer engagement: AI&A can improve the customer experience through direct engagement, e.g., chat. It can also drive hyper-personalization, targeting each customer directly and effectively.
- Network transformation: Network operations and AIOps can be optimized from process automation, machine learning, and reasoning to zero-touch predictive maintenance/self-healing. These technologies can also support network rollout and field force optimization.
- Lean telco operations: Multiple AI&A use cases can be used in lead-to-cash and service assurance. Business and operations support systems (BSS/OSS), as well as supply chain management can also be augmented.
- Revenue growth: B2B services are optimized due to real-time data insights and other advanced technologies. Overall, sales and services are also optimized.
Why is it difficult to know how best to invest?
Clearly, each opportunity area presents many applications for AI&A, but where does a CSP place its bets? There are several factors that make this tricky.
On the one hand, most CSP value streams will yield benefits – for example, both customer-facing lead-to-cash and operational record-to-analyze offer happy hunting grounds. Within each value stream there are a range of addressable opportunities, both straightforward and complex with differing ROIs, from simple data entry to complex back-office virtual agents.
On the other hand, a wide variety of new and established technologies are crowding the market, which can be applied individually or in concert. For example, workflow, decision management, and robotic process automation, through to emerging Generative AI.
Moreover, stakeholders have differing levels of enthusiasm ranging from outright resistance and box ticking to strong advocates – possibly depending on certain technology types or vendors. This makes it difficult for CSPs to understand which technologies would best suit their operations and business goals.
AI&A maturity and governance models also abound, often themselves siloed, and which are required to contend with new ethical AI regulation. Some applications of AI may seem appealing but expose the enterprise to risks like recruiting or the management of critical network infrastructure.
The benefits that some of these technologies provide will only be felt in the long-term. For instance, curating quality data is essential to most higher value AI use cases but can be highly complex, taking years.
Finally, acquiring, developing, and retaining AI skills will remain challenging for the foreseeable future. Finding the right North Star, organization, and roles to help identify, rollout and operate AI&A is also a challenge.
It’s therefore hard to have visibility and make informed decisions. Investment can be applied in an ad hoc fashion, the easy opportunities missed or held up, and the more difficult but interesting overserved. By over-strategizing and governing, you can risk accumulating high overheads, with local enthusiasm and innovation stifled. Too little planning and control leads to losing precious budget and focusing on a plethora of local initiatives and technologies, driven by the enthusiastic, with unknown returns.
Where do CSPs spend to get the most return on investment?
CSPs can start to improve their ROI by focusing on the following areas:
1. Make an inventory of your investments by value stream
Depending on the model used (e.g., TM Forum, APQC) there are around 25-30 value streams in a CSP, more if separated by market segments. Most should see a measure of investment, however, attention should be focused on business-critical value streams where AI&A can add most value.
CSP strategies vary but most customer-facing value streams such as lead-to-cash should be high-focus, as well as key operational areas such as service and network planning, assurance, and lifecycle management.
Opinions on relative levels of investment may also vary, but there should be visibility and direction. Targeted improvements to value stream business outcomes in the form of business critical KPIs can provide achievable North Stars for programs.
2. Use a range of technology
There’s no single killer technology, each macro pain point will have a subset of AI&A technologies that can be considered. If potential is being address, the organization should see:
- A range of simple to complex challenges being addressed across most value streams
- The highest business impact from the biggest investment in the value stream
- A range of established and new technologies being used
3. Check pain points
In CSPs, each value stream has relatively well-known macro pain points where AI&A can be applied, some straightforward – e.g., automation of swivel chair data entry between and within BSS and OSS systems – others higher value but more complex to address. The latter include recommending an optimum price point or guiding back-office staff through labyrinthian business rules for complex B2B products.
If AI&A is being exploited well, then a CSP should expect to see initiatives in certain areas, as well as a range of tactical and strategic endeavors. For example, in lead-to-cash a range of investments should be seen across all areas of the value stream.
4. Direct the investment
Taking this AI&A portfolio view can provide a framework to direct investment to the most appropriate areas for a CSP in the early stages of exploiting AI&A, or to augment more mature strategies and governance.
To do this successfully, ask yourself the following questions:
- Are we addressing the low hanging fruit?
- Is investment being skewed toward an area where the enthusiasts are, as opposed to where most value can be gained?
- Are we avoiding the difficult areas that are strategically important?
- Are we ignoring certain technology types, or applying others where there are better options?
- Are we chasing too many fads or ignoring great innovations?
This can also be further developed to construct an end-to-end view of AI&A initiatives and their aggregated impacts on a value stream performance. This then raises further questions:
- Are initiatives creating siloed improvements but not impacting key end-to-end metrics, or are the overall impacts unknown?
- Has the aggregated customer and employee experience been considered to create value?
- Are we consolidating efforts (data, process, tools) at least within or across related value streams e.g., lead-to-cash, request-to-change, service assurance?
- What is the balance between creating quality data architectures vs. speed of execution and business benefit, especially for ‘low hanging fruit’?
CSPs should expect their internal capabilities or external partners in AI&A to proactively seek to create such views. They can also expect these stakeholders to come to the table with points of view on where investment should be made – basing investment on generic discovery phases where consultants or internal AI&A subject matter experts learn their industry is questionable.
Prioritize, manage, and track activity through a value stream lens applying widely understood knowledge of pain points. This will ensure appropriate coverage using a range of established and emerging technology to manage risk and achieve ROI.
Reach out to our experts, for further information on how AI&A can help telecom companies grow.
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