Josh Edwards

“The Ontology is the substrate through which humans and AI work together to create operational value — rooted in your business, compliant with your norms, and measured against the outcomes you care about.“ — Palantir Chief Architect, Akshay Krishnaswamy 🎤 ⬇️

571

12 Comments

AI isn’t just a tool for architects; it’s changing how enterprise architecture itself evolves. The maturity model highlights three stages: Level 1: AI-Augmented – Documentation support, standards drafting, diagram creation, trade-off analysis and rationalization insights. Level 2: Predictive – Portfolio trend forecasting, standards drift detection and cost/risk scenario modeling. Level 3: Autonomous – Governance on autopilot, continuous optimization, and self-updating architecture models. These aren’t abstract ideas. AI is already generating diagrams from code, surfacing portfolio risks, and proposing optimizations that once took weeks. EA Leaders: where does your organization sit today, are there other scenarios people are using AI in evolving the EA capability?

10

2 Comments

It’s always rewarding to get together with peers from diverse industries and learn from each other. Tonight’s Nashville CDO Peer Group was no different. From an engaging discussion on the shifting skills needed for the AI workforce of the future, common themes emerged. Across Healthcare, large CPG, Industrial, Financial Services, Academia, Manufacturing, and Engineering Services industries there were shared insights and learnings. Key takeaways: - The increasing value of Domain-specific knowledge/data - Encourage and look for Grit and Curiosity as attributes that are differentiating those who are creating opportunities from AI - Build a champions network with a thriving community to share best practices and inspiring use cases - Technical acumen is increasing across organizations and you need progression paths to avoid debilitating dead ends - With increased accessibility of AI tools, IT/Technology/Data teams have a different value proposition. You can no longer be the “gatekeeper” to technology and have to be the “enabler” who is connecting dots and building capabilities that scale and extend localized innovations - And many more… Special thanks to Charlie Apigian for leading the discussion! Interested in joining us in Q1? Check out Greater Nashville Technology Council Piyush Singhal, MBA Olivia Kew-Fickus Ashutosh S. Manu Manrao Satya Srighakolapu John Cattani Ray Spangler Charlie Apigian Chaitanya Garikapati Timothy Cebulski

118

7 Comments

As someone who has worked in the RPA space for nearly a decade, I believe people with our background will be crucial to a successful AI implementation within the companies we work for! Gartner Forecast: 90% of RPA vendors to include Gen‑AI by 2025 According to Gartner’s 2023 Magic Quadrant, nearly all RPA vendors are either prototyping with GenAI or embedding it into their platforms—and by 2025, 90% are expected to complete that transition

8

1 Comment

The Foundation of Enterprise Native AI Transformation Enterprise Data Platform Readiness is not just a prerequisite. It is the foundation that determines how far and how fast an organization can move with Native AI. When data lives in silos, AI stays trapped in pilots. But when platforms are unified, governed, and scalable, they unlock the full potential of intelligent and autonomous operations. This isn’t about upgrading a data warehouse. It is about designing a system that feeds AI continuously, learns from every interaction, and adapts across the enterprise as conditions change. https://lnkd.in/eUekn_pC #dataplatforms #vectordb #pinecone #milvus #chroma #weaviate #Qdrant #faiss #opensearch

18

We're relying more on data to innovate and grow, which is why ineffective data product practices are becoming a problem. Companies may need to change their approach to data. Instead of developing data products to generate better data, develop them to generate value. Focusing on value will bring your company opportunities. #Data #Innovation #Growth

8

The best insight I've had recently about data leadership came from catching up on my read-it-later list during a recent family trip to the snow. Nicholas Carr's concept of "ambient overload" perfectly describes what every Head of Data or CDO faces today. The problem isn't finding the one thing worth doing. It's being confronted by hundreds of things that could all be done, all technically feasible, many genuinely useful. The question is no longer "can we build it?" It's "should we?" New essay on why that shift changes everything about the data leader's role. Link in the comments.

22

18 Comments

I came across a thoughtful article by Tom Davenport and Randy Bean in MIT Sloan Management Review on AI and data science trends for 2026. (https://lnkd.in/er22iud6) It takes a clear position on where AI is really headed in current business world and make a point that the gap between technology acceleration and organizational adoption is widening. A market correction may actually help leaders refocus on impact over experimentation. For electric and gas utilities, AI’s value won’t come from flashy GenAI demos but it will come from industrializing AI through shared platforms, trusted data, and strong governance, and applying it to core functions such as customer operations; grid operations and reliability for electric utilities; safety and leak detection for gas utilities. AI isn’t the bottleneck but organizations are. The next phase of maturity is grounding innovation in a relentless focus on real, measurable business outcomes.

10

3 Comments

*What is the need for Palantir Foundry?* Fundamental Problem with standard platforms like Snowflake, Databricks, and Cloudera for Data Production and Data Pipelines: fragmentation is the core issue when using disconnected tools. While each tool handles a specific piece of the business, they do not provide a full picture. This makes it difficult for a manager to instantly connect a tenant's work order with relevant financial or scheduling data Foundry's main advantage is that it combines storage, transformation, orchestration, and application building into a single governed environment, whereas other solutions require stitching multiple separate tools together

2

Excellent insights as usual Charlie! Agree completely with the CIO role evolving into the integration role - foundational core while enabling self service analytics and AI across the enterprise. https://lnkd.in/eVauH8B3

49

1 Comment

Listen to CGI´s 'From AI to ROI' podcast where Andy Donaher, Cheryl Allebrand and Fred Miskawi discuss the differences between agentic AI, traditional RPA and generative AI. They discuss how enterprises can responsibly implement agentic AI while maintaining human guidance and ensuring trust, transparency and quality assurance are integrated. #AI #GenAI #AgenticAI

27

Working through the proofs of concepts for ai and learning from the experience of peer companies here are two of my takeaways: 1. Bring ai to where the data lives, rather than data to where the ai platforms are. This elevates the need for centralized or federated very large scale data platforms with lineage and data quality governance mechanisms built in or tightly integrated. (#snowflake,#databricks etc) 2. Bring ai to where the user lives, rather than the user to where ai lives. This means that now its less about the capabilities of competing ai platforms but how to surface the results of ai agents in the environment that users already live in (#slack, #msteams etc)

93

6 Comments

Dashboards say one thing. AI says another. Everyone blames the data. But the real problem is often the missing translation layer. Semantic modeling is the bridge between your data platform and trustworthy AI. In this meetup we will discuss why it matters, why many teams don’t practice it, and how capabilities like Semantic Views, Cortex Analyst, and AI agents depend on it to produce consistent answers. And who better to walk us through it than Kishore Aradhya, who brings over two decades of experience building and teaching data, analytics, and AI. 📅 Mar 26, 12:00 – 1:00 PM (EDT) 📍 #Snowflake Applied Data Modeling User Group: https://lnkd.in/eCEVcjyX You do not need to be a Snowflake user or customer to attend. #snowflake #semantics #snowflake_advocate #datasuperhero #AI

89

5 Comments

Second session from the CDO Magazine Columbus event yesterday. Rewriting the rules: Strategy and Leadership in the Age of AI, Data and Cybersecurity. Some of the AI challenges in adoption (and I've been asked this question by my own executive leadership team and private equity), who are the major/dominant players (outside of ChatGPT and CoPilot)? Tonjia Coverdale, PhD pointed out, there are none. There's no Microsoft Office of the AI world (yet). Some other "golden nuggets" from the panel: As you strategize, this allows for flexibility and is "loosely coupled to pivot" per Sean Erikson. "Can vs should, and AI ethics," Tonjia. Agentic AI overlap from an earlier Centric Consulting session was referenced by Sean. Great question from an attendee, " who owns the AI spend?" Meaning it's not insignificant especially when you consider IT budgets already hover in the 7-8% range of revenue. Scot Burdette discussed transitions in AI/tech partners, who owns AI adoption and being ambassadors within the business. Sean referenced, "thousands flowers bloom approach to AI." Lisa Runkle Michelle Ziegler, Global VP, Partner Development and Events https://lnkd.in/gNn6yNiK

18

1 Comment

📌 Digital Twin Infrastructure: From Simulation to Strategic Ops Most ops plans collapse when volatility hits. The top COOs know that already. That’s why they’re building digital twins of their supply chains, warehouses, and even labor models so they can test chaos before it happens. Gartner ranks digital twins as a top 3 ops tech for 2025–2027. Because static SOPs don’t cut it anymore. Here’s what the best are doing: Mapping their physical ops into digital layers Modeling SKU flow, pick-path congestion, labor fatigue Stress-testing “what if” events like cold chain failures or strike disruptions The result? → Lower fulfillment costs → Faster dock-to-stock → Fewer decisions made under pressure Smart COOs aren’t reacting they’re rehearsing. I’ve built a cheat sheet on digital twin use cases: slotting, labor sims, latency KPIs, and tech stack design. Want it? Drop a in the comments or DM me. #COO #DigitalTwin #SupplyChainResilience #WarehouseOps #FulfillmentTech #LogisticsInnovation #OperationalExcellence #ERP #WMS

9

3 Comments

What if all your documents could become instantly searchable through AI? With Palantir Foundry + AIP, it is possible to design and structure a use case demonstrating how: - Complex PDFs (text, images, charts) - Can be transformed into structured datasets - Organized within a business Ontology - And queried using AI (GPT-4.1 or any other model) Simply ask a question and receive an answer grounded in all available documents. This type of solution can be deployed across multiple industries: 🏥 Healthcare: medical research, protocols, clinical reports ⚖️ Legal: contracts, compliance, regulatory documentation 🏭 Industry: technical documentation, maintenance, procedures 🏦 Finance: audit reports, risk analysis, regulatory files 🏢 Large enterprises: internal knowledge bases and document management At Neyo, we design and implement these types of solutions to transform your data into a real decision-making asset. #Palantir #Foundry #AI #Data #Ontology #Neyo

6

The strongest data warehouses succeed because modeling, governance, and shared meaning guide every design decision. When metadata and standards drive the process, teams trust their data and scale without confusion. ER/Studio gives organizations a model-driven foundation that keeps definitions consistent, integrations clean, and communication clear across technical and business teams. Benefits to the business: 💡 Faster approvals and smoother delivery ✔️ Consistent KPIs and trusted analytics 🔹 A scalable framework that adapts as platforms evolve Read the blog 🔗 https://lnkd.in/eR-xsFsE #ERStudio #DataArchitecture #DataGovernance #KnowledgeGraph #DataProducts

11

1 Comment