Wednesday, January 31, 2024

The AI-native telco network

AI, and more particularly generative AI has been a big buzzword since the public launch of GTP. The promises of AI to automate and operate complex tasks and systems are pervading every industry and telecom is not impervious to it. 

Most telecom equipment vendors have started incorporating AI or brushed up their big data / analytics skills at least in their marketing positioning. 
We have even seen a few market acquisitions where AI / automation has been an important part of the investment narrative / thesis (HPE / Juniper Networks)
Concurrently, many startups are being founded or are pivoting towards AI /ML to take advantage of this investment cycle. 

In telecoms, there has been use for big data, machine learning, deep learning and other similar methods for a long time. I was leading such a project at Telefonica on 2016, using advanced prediction algorithms to detect alarming patterns, infer root cause analysis and suggest automated resolutions. 

While generative AI is somewhat new, the use of data to analyze, represent, predict network conditions is well known. 

AI in telecoms is starting to show some promises, particularly when it comes to network planning, operation, spectrum optimization, traffic prediction, and power efficiency. It comes with a lot of preconditions that are often glossed over by vendors and operators alike. 

Like all data dependent technologies, one has first to have the ability to collect, normalize, sanitize and clean data before storing it for useful analysis. In an environment as idiosyncratic as a telecoms network, this is not an easy task. Not only networks are composed of a mix of appliances, virtual machines and cloud native functions, they have had successive technological generations deployed along each other, with different data schema, protocols, interface, repository which makes the extraction arduous. After that step, normalization is necessary to ensure that the data is represented the same way, with the same attributes, headers, … so that it can be exploited. Most vendors have their proprietary data schemes or “augment” standard with “enhanced” headers and metadata. In many case the data need to be translated in a format that can be normalized for ingestion. The cleaning and sanitizing is necessary to ensure that redundant or outlying data points do not overweight the data set. As always, “garbage in / garbage out” is an important concept to keep in mind. 

These difficult steps are unfortunately not the only prerequisite for an AI native network. The part that is often overlooked is that the network has to be somewhat cloud native to take full advantage of AI. The automation in telecoms networks requires interfaces and APIs to be defined, open and available at every layer, from access to transport to the core, from the physical to the virtual and cloud native infrastructure. NFV, SDN, network disaggregation, open optical, open RAN, service based architecture, … are some of the components that can enable a network to take full advantage of AI. 
Cloud networks and data centers seem to be the first to adopt AI, both for the hosting of the voracious GPUs necessary to train the Large Language Models and for the resale / enablement of AI oriented companies. 

For that reason, the more recent greenfield networks that have been recently deployed with the state of the art cloud native technologies should be the prime candidates for AI / ML based network planning, deployment and optimization. The amount of work necessary for the integration and deployment of AI native functions is objectively much lower than their incumbent competitors. 
We haven’t really seen sufficient evidence that this level of cloud "nativeness" enables mass optimization and automation with AI/ML that would result in massive cost savings in at least OPEX, creating a unfair competitive advantage against their incumbents. 

As the industry approaches Mobile World Congress 2024, with companies poised to showcase their AI capabilities, it is crucial to remain cognizant of the necessary prerequisites for these technologies to deliver tangible benefits. Understanding the time and effort required for networks to truly benefit from AI is essential in assessing the realistic impact of these advancements in the telecom sector.

Friday, January 26, 2024

Product Marketing as a Strategic Tool for Telco Vendors

Those who know me for a long time know that I am a Product Manager by trade. This is how I started my career and little by little, from products, to product lines, to solutions I have come to manage and direct business lines worth several hundred of millions of dollars. Along this path, I have become also a manager and team lead, then moved onto roles with increasing strategic content, from reselling, OEM, deals to buy and sell side acquisitions and integrations.

Throughout this time, I have noticed the increased significance of Product Marketing in the telecoms vendors environment. In a market that has seen (and is still seeing) much concentration, with long sales cycles and risk-adverse customers, being able to intelligently and simply state a product's differentiating factor becomes paramount.

Too often, large companies rely on brand equity and marketing communication to support sales. In a noisy market, large companies have many priorities, which end up diluting the brand promise and provide vague and disconnected messages across somewhat misaligned product and services.

By contrast, start ups and small companies often have much smaller range of products and services, but having less budget, focus in may case on technology and technical illustrations rather than exalting the benefits and value of their offering.

My experience has underscored the pivotal role of product marketing in shaping a company's valuation, whether for fundraising or acquisition purposes. Yet, despite its proven impact, many still regard it as a peripheral activity. The challenge lies in crafting a narrative that resonates—a narrative that not only embodies the company's strategic vision but also encapsulates market trends, technological evolutions, and competitive dynamics. It's about striking a delicate balance, weaving together product capabilities, customer pain points, and the distinct value proposition in a narrative that is both compelling and credible.

Many companies will have marketing communication departments working on product marketing, which often results in either vague and bland positioning or in disconnects between the claims and the true capabilities of the products. This can be very damaging for a company's image when its market claims do not reflect accurately the capabilities of the product or the evolution of the technology. 

Other companies have the product marketing as part of the product management function, whereas the messaging and positioning might be technically accurate, but lack competitive and market awareness to resonate and find a differentiating position that will maximize the value of the offering.

As the telecoms vendors' sector braces for heightened competition and market contraction, with established players fiercely guarding protecting their market share against aggressive newcomers, the role of product marketing becomes increasingly critical. It's an art form that merits recognition, demanding heightened attention and strategic investment. For those poised to navigate this complex terrain, embracing product marketing is not just an option; it's an imperative for sustained relevance and success in challenging market conditions.