🧮 Information retrieval from internal sources (unstructured)
🧭 Information retrieval from internal sources (structured)
📚Vector storage & database deployment patterns
🔏 Pre and Post filtering of content as per security guardrails

📐Reranking of the information retrieval
📌 Compression of the information retrieval
📝 Structured information retrieval optimization
🔎 Information retrieval from public sources

An illustrative maturity model (image generated with Nano Banana)

Stage 3 (Run): Build or leverage bespoke and enterprise-specific autonomous AI agents scaling organizational efficiency and achieving new possibilities

Stage 2 (Walk): Build enterprise or product-specific AI Assistants for individuals & organization productivity (e.g. Google Cloud Assist, Amazon Q, Microsoft Copilot, Salesforce Einstein, ServiceNow Now Assist, Oracle AI Agents, IBM watsonx Assistant, Adobe Firefly Assistant, Workday AI, Zoom AI)

Stage 1 (Crawl): Building the foundation layer and Agentic AI platform for building AI assistants and Autonomous Agents

✔️Berkeley Function/Tool Calling: gorilla.cs.berkeley.edu/leaderboard....
✔️LMArena: lmarena.ai/leaderboard
✔️SWE Bench: https://

2️⃣ Apply Auto-Reasoning and Auto-Selection of Models using solutions such as Semantic Routing instead of static binding
3️⃣ Reference existing industry LLM benchmarks for initial guidance and gradually build an enterprise-specific benchmark for diverse set of scenarios. Industry benchmarks:

1️⃣ Based on the LLM capabilities and Enterprise alignment, build a decision matrix to help drive LLM selection within the enterprise (Reference research: arxiv.org/html/2402.06...)
www.swebench.com/

3️⃣ Reasoning engine (aka the "Brain "): receives feedback from the environment, self-controls and adapts its actions

4️⃣ Actuators: Action results can go back into the model, agent interacts with environment with actions too

Reference: aima.cs.berkeley.edu

3️⃣Continuation of massive GPUs procurement and redesigned data centres/ AI factories with innovative power infrastructure (liquid cooling, high density racks, PDUs, etc.), low latency ultra-fast network (with tech like InfiniBand and NVIDIA's NVLink for communication between GPUs) & storage systems.

2️⃣Training and Inference cost (with surge in exponential demand for tokens throughout) will continue to be the top most budget in upcoming years

3️⃣ Cost per token = capex (annualized) + energy costs + other opex​/annual token output.
4️⃣Revenue = tokens X dollars per token

siliconangle.com/2025/08/30/r...

1️⃣ Jensen’s law (Buy more, make more) — revenue goes up with focus on reducing token cost (focused towards selling more Blackwell GPUs, enterprises’ AI strategy need to find the right balance as per the current & predictive workload)
2️⃣ Tracking Tokens per year = power X efficiency X utilization

Tracking these New Metrics in your Enterprise helps to measure ROI and effectiveness of the Enterprise-specific AI strategy 👇

📚 Claude Sonnet is the most admired AI model
📉 66% of developers are frustrated with AI solutions that are almost right
📈 More than one third of respondents use AI-enabled tools to learn AI this year

✅Operational Efficiency: Distributed Cloud, Hybrid Cloud Compute/Storage, Edge Data Management Capabilities
✅ Security: XDR, EDR, ZTNA, ITDR

emt.gartnerweb.com/ngw/globalas...