AI to ROI Podcast: "Context Graphs – AI’s Trillion-Dollar Technology"

Host: Ray Rike
Guest: Peter Buchanan
Date: February 6, 2026

Episode Overview

In the debut of the relaunch as “AI to ROI,” hosts Ray Rike and Peter Buchanan take on the “Big Story” of the week: context graphs—a breakthrough technology poised to fundamentally reshape enterprise AI. They explore why context graphs are grabbing headlines as a “trillion-dollar technology,” what sets them apart from knowledge graphs, who in the enterprise should be paying attention, practical use cases across industries, vendor landscapes, and strategic recommendations for business and technology leaders.

Key Discussion Points & Insights

1. The Emergence and Promise of Context Graphs

• Definition & Opportunity:

  • Context graphs are described as the connective “glue” in the next-generation AI stack, enabling AI systems not only to execute actions but to explain and document why decisions were made, under what constraints, and by whom ([01:39]).
  • They bring a new dimension to AI: auditability and transparency, paving the way for more trustworthy and reliable production AI systems.
  • Notably, Foundation Capital’s much-discussed article predicts context graphs will unlock trillions of dollars in value by drastically improving AI reliability and trust ([02:39]).

• Quote:

  "This technology is a glue technology that takes the promise of things like agentic AI and makes it much more effective."
  — Peter Buchanan [03:25]

2. Evolution from Semantic Web to Knowledge Graphs to Context Graphs

• Historical Context:
  • Semantic Web (early 2000s), then knowledge graphs (2010s)—the latter used extensively by Google—focused on mapping data relationships.
  • Context graphs “inject steroids” into this lineage, linking not just what happened, but how and why, critical for agentic AI ([05:17]).
• Differentiation:

  "A knowledge graph will tell you customer X renewed a contract. The context graph will say ... who approved that renewal? What policies went into making that decision?"
  — Peter Buchanan [05:17]

3. Who Should Care? Stakeholder Impact Across the Enterprise

• Broad Relevance:

  • CEOs (justifying investments, brand risk, regulatory reporting), CFOs (ROI, resource allocation), Chief Risk and AI Officers (compliance, liability), CTOs/CIOs (infrastructure, future-proofing), and business line managers (competitive advantage, better outcomes) all have a stake ([07:18]).
  • Context graphs are foundational for auditability, governance, and trust in AI-driven business ([08:10]).

• Quote:

  "...this is a really, a glue technology. It's, it's something that's just missing most places."
  — Peter Buchanan [08:18]

4. Practical Use Cases Demonstrating Value

4.1. Data Governance and Change Management ([09:29])

• Scenario: When a data engineer changes a database field, context graphs track dependencies across applications/models so issues are caught proactively.
• Industries: Highly relevant for regulated industries (e.g., finance, insurance).

4.2. Customer Lifecycle & GTM Stack Integration ([11:21])

• Challenge: Most companies have deeply fragmented tech stacks; context graphs unify data, enabling end-to-end traceability from marketing through customer success.
• Quote:

  "With a context graph ... you're not just understanding what the customer bought, but ... their support experience, their product usage, their payment behavior, how they act in negotiations, all those sorts of things."
  — Peter Buchanan [12:44]

4.3. Supply Chain & Manufacturing Quality ([13:33])

• Use Case: Tracing relationships down to the supplier and batch levels. One auto supplier reduced quality control cycle times from weeks to hours.
• Quote:

  "...been able to cut quality processes that used to take weeks down to hours with, with better results..."
  — Peter Buchanan [15:20]

5. Market Landscape: Leading Vendors and Future Entrants ([15:42])

• Current Players:

  • Atlan: Metadata lakehouse, “context control plane” for AI governance.
  • Neo4J: "Graph Rag," strong LLM integrations; used in regulated sectors.
  • Ryder: Context graphs for content, brand/legal compliance.

• Incumbent Expansion:

  • Expect “lurkers”—Microsoft (Agent365), Salesforce, Sierra (agent-first companies), database vendors, possibly OpenAI/Anthropic—to integrate context graph capabilities.
  • Prediction: Early pioneers may not be tomorrow’s winners; expect market shake-up as capabilities evolve rapidly ([17:46]).

• Quote:

  "There's that phrase pioneers get the errors and settlers get the land..."
  — Peter Buchanan [19:50]

6. Practical Guidance: What Should Executives Be Doing? ([20:34])

• Immediate Action Items:

  • If you're in a regulated environment, begin investigating context graphs now—retrofitting will be more expensive and complex.
  • Native AI SaaS vendors: competitive pressure is coming; auditability will differentiate products.
  • For all: Don't lock in a “context graph for life” yet. Pilot in highly regulated, high-value applications, stay attuned to developments from major platform providers.

• Quote:

  "If you're not building this context infrastructure now, you're going to be forced to do it one to two years and it's going to be much more expensive and much harder to try to retrofit an agentic AI implementation that's not built on top of a context graph."
  — Ray Rike [20:56]

• Strategic Perspective:

  • Be experimental, engage with multiple vendors, and expect rapid tool evolution—think “Y2K,” where solving late was often cheaper ([21:36]).
  • Focus on outcomes (auditability, reliability, compliance), not just technology selection.

Memorable Quotes & Moments

• What Is a Context Graph?

  "You not only see what happens, you see who decided to make those things happen, why they decided that, under what constraints they made those decisions and what precedents they used ... it provides the why things happen, which agentic systems generally don't know."
  — Peter Buchanan [01:39, 05:35]

• Step Change over Knowledge Graphs:

  "It's like an injection of steroids, to be honest with you."
  — Peter Buchanan [05:17]

• Enterprise Stakes:

  "...the CEO has to justify these AI investments. They have to work well. They don't want their customers complaining ... they have to have confidence that they're following rules and that they understand end to end. So it's auditing, it's governance ..."
  — Peter Buchanan [07:51]

• Pragmatic Market Advice:

  "...I would be tracking this closely. I would be experimenting with technologies. I probably wouldn't make a selection that basically says this is going to be my context graph for life."
  — Peter Buchanan [22:28]

Timestamps for Important Segments

• [01:39] – What are context graphs and why they're important
• [05:17] – Evolution from knowledge graphs to context graphs
• [07:18] – Who in the enterprise cares about context graphs
• [09:29] – Four example use cases (data governance, GTM stacking, supply chain, manufacturing)
• [13:33] – Deep dive on supply chain/manufacturing application
• [15:42] – Current market, vendors, and platform-provider moves
• [20:34] – Executive recommendations and next steps

Final Takeaways

• Context graphs represent a foundational shift in AI systems, enabling trust, visibility, and business value at scale.
• Adoption is just beginning—executives should prioritize exploration, pilots in critical use cases, and flexibility in vendor/tools selection.
• Competitive pressure will quickly make these capabilities table stakes, especially in regulated or highly visible industries.

For more detail and practical guides, see the AI to ROI Newsletter's "Big Story" on Substack.