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Ep 134 – Show me the money (MWC)
In this special episode, DR explains the one tough question you should be asking vendors before you buy AI and why the Totogi Ontology is key to delivering real world results.
Podcast
Ep 135 – Telco has a context problem (John Abraham)
This week’s guest
John Abraham
Partner and Principal Analyst Appledore ResearchEvery telco exec is talking about AI agents. Almost nobody is talking about what those agents actually need to work: context.
Not just data — the logic, rules, and decision-making processes that define how your business actually operates. That knowledge currently lives in two places: buried in vendor code nobody fully understands, and inside people’s heads. No agentic system, no matter how shiny, can make decisions without it.
In this episode, I sit down with John Abraham, Partner and Principal Analyst at Appledore Research, to dig into why a lack of context is the real blocker for AI at scale, why most approaches only solve half the problem, and how an ontology gives AI the business logic it needs to not just analyze, but decide and act.
Listen now to hear:
- The three Cs holding telcos back from using AI at scale [04:42];
- Why AI agents risk creating a new generation of silos [08:59];
- How a small use case can unlock compounding AI value [14:13]; and
- Why a data lake is a costly side quest [15:09].
Links and resources
- Read the Appledore Research report: Telecom-specific Ontology, the key to AI-native telco (paywalled).
- Watch my MWC26 talk for the Agentic AI Summit, Show me the money.
- Learn more about the Totogi Ontology and how it’s helping Tier-1 operators turn AI into actual profit.
- Want to see the Totogi Ontology in action and hear more customer success stories? Find Team Totogi in Hall 2 at MWC26 in Barcelona through March 5. We’ll be showing off our telco ontology that manages enterprise context at scale. Set up a meeting.
- Check out these additional Appledore Research reports: GenAI in BSS and Predictive Analytics and GenAI: Choosing the right tool for the job (paywalled).
- Listen to my conversation with Rick Lievano from Microsoft, who talked about AT&T’s AI agent deployment and why they pumped the brakes before going to production.
- Curious how a Tier-1 operator turned a $1–2M dormant cell problem into tens of millions in CXO-level impact—all on the same ontology foundation? Listen to What’s up with Totogi: The biggest AI use case for telco.
- I once gave John a Yeti that he still raves about. Since then, he’s grown a beard and likes using Vikings Revolution products to take care of it. I guess our next gift to him will be a Mustache Guard mug.
- Check out this episode on our YouTube channel.
- You can find the episode transcript here.
Wanna talk AI and public cloud? Telco execs, set up a meeting with our team to learn how to tap the immense business value it can bring.
Guest bio
With over 16 years of experience in the telecom industry, John leads Appledore’s Digital Enablement & Monetization program. Previously, he was at Analysys Mason for 11 years, where he led the digital experience research segment as principal analyst. He has experience working with a varied client base on topics ranging from digitization benchmarking and procurement for CSPs; strategy and go-to market for vendors; and commercial and technical due diligence for financial institutions. Earlier, as a consultant at a BSS vendor, he led requirements gathering, solution definition, and implementation at multiple Tier-1 telcos in Asia and Europe. John holds a bachelor’s degree in computer science from Anna University (India) and an MBA from Bradford University School of Management (UK).
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Podcast credits
- Executive Producer and Host: Danielle Rios, TelcoDR
- Senior Producer: Lindsay Grubb, TillCo Media
- Senior Editor/Brand Manager: Alisa Jenkins, Springboard Marketing
- Audio Editor: Andrew Condell
- Supervising Producer: Amanda Avery
- Associate Producer: Kriselda Dionisio
- Music: Dyami Wilson
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Frequently Asked Questions
1. What are the three biggest challenges holding telcos back from AI at scale?
According to John Abraham, partner and principal analyst at Appledore Research, the three Cs are cost, context, and communication. Cost involves both the price of AI and uncertainty around its value. Context means agents often operate partially blind, lacking the business logic needed to make decisions. Communication refers to the challenge of enabling AI agents to coordinate and understand each other’s intent. Without solving these, telcos risk creating a new generation of agentic silos.
2. Why aren’t data lakes and APIs enough to give AI agents the context they need?
Data lakes and APIs can move data, but they can’t capture how a business actually makes decisions. As John Abraham explains, the missing piece is the business logic layer—the rules and processes currently buried in vendor code or inside people’s heads. Without that knowledge graph and decisioning layer, AI agents are flying blind. Context isn’t just about data access; it’s about encoding what your business is and isn’t allowed to do. Learn more in the Appledore Research and Totogi white paper titled Telecom-specific Ontology, the key to AI-native telco.
3. What is a telco ontology, and why does it matter for AI?
An ontology has three components: a semantic layer (the “nouns” of your business—how things are defined and related), a kinetic layer (the “verbs” — how things interact and behave), and a dynamic layer (how decisions are made and actions are taken). Together, these give AI agents the full business context they need to act—not just access data. A data lake provides only the first layer; a full ontology enables AI to actually think and execute.
4. Why does Danielle Rios call the context layer a telco’s competitive moat?
DR argues that AI models are commodities—everyone gets access to the same LLMs. What differentiates a telco is its context layer: the codified business rules, relationships, and decisioning logic unique to that operator. That knowledge compounds over time and is owned by the telco, not locked in a vendor’s system. DR believes it’s also why vendors like Amdocs keep that logic away from you. The Totogi Ontology is built specifically to give that ownership back to operators, making it the real investment layer that allows AI to scale.
5. Do telcos need to finish their data lake before building an ontology?
No—and DR is direct about this. A data lake is, in her words, “a $20 million, multi-year side quest.” Even with clean data, you still need a way to act on it in live systems. The Totogi approach is to apply semantic consistency to systems of action now, start small with a focused use case, and build from there. A Tier-1 operator in the Middle East started with a dormant cell problem worth ~$1–2M and expanded to CXO-level fuel deployment decisions worth tens of millions—all on the same compounding ontology foundation. Learn more about this use case in the Telco in 20 episode: What’s up with Totogi: The biggest AI use case for telco.
6. How did Totogi demonstrate real AI value with a Tier-1 operator?
Totogi started with a narrow problem: identifying dormant cells causing roughly $1–2 million in revenue loss for a Middle East Tier-1 operator. The AI identified the issue, the Totogi Ontology defined what actions were valid, and the system executed. No tickets, no human intervention. As more systems connected to the ontology, value compounded. The same foundation now supports CXO-level decisions on fuel deployment across the network, worth tens of millions of dollars. Every decision made the ontology smarter for the next one.