If AI adoption was a pervasive trend in 2024, then 2025 promises full-scale AI domination. Companies of all kinds will continue to explore how best to leverage AI, monetize AI products and services, and in some cases, protect themselves from obsolescence.

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As AI continues to revolutionize business processes across industries, many organizations are coming face-to-face with a harsh reality: AI is expensive! While AI promises substantial productivity boosts and personalization, the hidden costs of running AI can quickly spiral out of control without proper metering and restraint.

The market dynamics for B2B SaaS businesses are shifting, with profitability now taking precedence over the traditional growth-at-all-costs mindset. At the same time, SaaS companies are facing mounting pressure to offer better value.

Across the global business world, enterprises are racing to determine how best to leverage and integrate AI technologies to improve business performance and outcomes. The opportunities are certainly compelling. AI can positively impact all aspects of the customer lifecycle, from product management to sales to customer service.

We wanted to know just how big an impact AI could have on CSP EBITDA, so, always up for a challenge, we participated in a TM Forum Moonshot catalyst to prove that using AI to deliver smarter solutions, including copilots to boost call center performance, could boost EBITDA by up to 30% (spoiler alert: we smashed it.)

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While the adoption of AI is on the rise among CSPs, the journey towards becoming an AI-driven telco is far from uniform. There’s no silver bullet – the key lies in striking a balance between preparing your organization for AI and capitalizing on emerging opportunities as they arise.

AI is now firmly front and center of all operator strategies and is set to form the foundations of their future architectures. But first they need to address some fundamental issues, such as adopting a coherent, company-wide data infrastructure as well as putting the right technologies and processes in place.

Any organization attempting to leverage GenAI to bolster customer experience and support without integrating rich, fulsome, real-time billing data is bound to fail. Not just fall short. Straight up fail.

Using generative AI (GenAI) for the wrong business use cases leads to high failure rates and diminishes the value of AI in organizations. To avoid this, IT leaders can use this guidance to evaluate if GenAI is the right fit for their use case or whether to consider alternative AI techniques.

The average consumer is probably not familiar with the term “conversational commerce”. But chances are most, if not all, have experienced it in some way if they have recently engaged with a brand.

GenAI has burst onto the scene in the past 12 months, with OpenAI’s ChatGPT to the fore. But where do operators fit into the value chain? Should they build their own LLMs? And what are the early use cases in telecoms?

In late February, the global telecommunications ecosystem once again descended upon Barcelona for the annual Mobile World Congress industry conference. To no-one’s surprise, AI was once again the dominant theme, with widespread discussion among exhibitors and attendees alike, and too many AI-powered solutions to count.

The dominant theme of Mobile World Congress 2024 was AI, with much discussion about which applications and use cases would generate the most value for CSPs.