Signals from the Edge #11

Written By Michael Krouze

Our Quick Take

This week, we are taking a different approach to the newsletter.  Google recently released their “The ROI of AI 2025” report, and we thought it might be helpful to review and surface some of the key points so you don’t have to. If you would like to read the whole report, you can find it here: The ROI of AI 2025.

GenAI has shifted from novelty to operating layer. Google Cloud’s new ROI of AI 2025 report shows agentic systems moving beyond pilots: half of genAI adopters now run agents, and early movers are scaling into double digits. ROI is showing up fastest where work is repeatable (productivity, customer experience, marketing, and SecOps), yet the biggest unlocks still hinge on data quality, integration, and executive sponsorship. The message for tech leaders: architect for agents, not just apps; fund the plumbing, not only the models; and measure speed of return alongside size of return.

New & Newsworthy

1. Agentic AI has left the lab

Enterprises are deploying agents at scale, not just chatbots. Over a third of organizations report having 10 or more agents in production, and early adopters are far ahead. Treat agents as products with SLAs, not experiments. Build an agent ops layer (orchestration, policy, guardrails, audit, observability) and standardize patterns from Level 1 tasks to Level 3 multi‑agent workflows. Start with contained workflows (support, marketing ops), then expand via tool access and secure API contracts. 

  • pp. 8–9 (maturity levels), p. 11 (39% have >10 agents), p. 17 (82% of early adopters >10 agents)

2. ROI is real—and accelerates with C‑suite sponsorship

74% of organizations report ROI within the first year, and 88% of agentic early adopters already see returns. The correlation with leadership is significant: comprehensive C‑suite sponsorship materially increases the odds of ROI. Define ROI up front (speed + size), instrument outcomes, and keep human‑in‑the‑loop. Put an executive owner on the hook for value realization, not just delivery. 

  • p. 22 (ROI in first year), p. 3 (88% early adopters see ROI), pp. 42–43 (C‑suite sponsorship → ROI)

3. The money is moving from models to plumbing

Budgets are rising even as model costs fall: 77% report genAI spend increasing. Mean 26% of IT spend is already AI; early adopters are at 39%. Dollars are flowing toward change management, data/knowledge management, talent, and tooling—the guts that turn clever prompts into dependable value. Partner tightly with Finance to shift from CapEx‑style pilots to productized, OpEx‑sustainable AI services with unit economics. 

  • p. 40 (spend rising; new vs. reallocated budgets; 26% mean IT→AI), p. 19 (early adopters: 39% IT→AI), p. 41 (top investment areas)

4. Where value concentrates: Productivity, CX, Marketing, and Security are maturing fast

The strongest, cross‑industry gains: Productivity (70%), Customer Experience (63%), Business Growth (56%), Marketing (55%), and Security (49%). Near‑term ROI shows up in individual productivity (39%), CX & field service (37%), and sales/marketing (33%). Translate these into programmatic plays with clear KPIs: cycle‑time compression, NPS/CSAT, conversion, lead velocity, MTTR, and ticket deflection. 

  • p. 24 (top impacts), p. 26 (productivity ROI), p. 28 (CX ROI), p. 33 (sales & marketing ROI), p. 36 (security outcomes)

5. Data privacy and integration trump model horsepower

When choosing LLM providers, leaders put data privacy/security first, followed by integration with existing systems and cost. The biggest blockers are foundational: systems integration and security. Prioritize an integrated data plane with governance, lineage, and access control; minimize data copies; and give agents least‑privilege, auditable tool access.

  • pp. 44–45 (key challenges; top LLM selection factors)

6. Regional and industry patterns matter - aim your first agents where the pain is sharpest

Adoption is broad, but entry points vary: tech support in Europe, customer service in JAPAC, marketing in LATAM. Industry‑specific top‑three agent use cases are consistent with real‑world pain curves (e.g., security ops in public sector and telecom; quality control in manufacturing; software dev in healthcare/life sciences). Localize your roadmap and pick one frontline workflow per business to automate end‑to‑end. 

  • p. 12 (regional patterns), p. 13 (adoption rates by region/industry/size), p. 16 (top 3 use cases by industry)

Our Thinking

Architecture now beats raw throughput. In the agentic era, shipping faster code into brittle systems just compounds hidden costs; composable architecture, clear contracts, and robust observability are what unlock durable ROI from AI. 

For a deeper dive, check out the full article → Architecture Over Throughput: Why System Design Now Outweighs Raw Coding Speed

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Michael Krouze https://www.brainrazr.com
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Architecture Over Throughput: Why System Design Now Outweighs Raw Coding Speed