Rick Schultz

MIT’s recent report found that 95% of enterprise AI pilots fail to deliver ROI, with value trapped in workflows, slow scaling, and misplaced bets in sales and marketing instead of back-office and integration wins. But these are symptoms, not the root cause. The real issue is strategic misalignment, as oftentimes organizations don’t clearly define the kind of bet they’re making. At the core, every pilot is a capital allocation and risk management decision. Pilots fail without clarity on the business angle, hypotheses, and go/no-go criteria. To address this, I’ve developed The Pilot Strategy Framework™, a structured approach to categorizing AI pilots based on their strategic intent and business impact. 1️⃣ Validation pilots - validate feasibility before scaling and committing further resources. 2️⃣ Internal adoption pilots - phased rollout of AI within departments, inside existing workflows, to see if it can unlock ROI. This can often signal market readiness to customers, competitors, investors, and the industry overall. 3️⃣ Customer-facing pilots - test whether existing customers will adopt new AI capabilities and whether it’s commercially safe to scale. 4️⃣ Legacy integration pilots - answer if AI can unlock value in legacy systems. 5️⃣ New product pilots - test whether additional AI offerings can be introduced; offerings that are core or adjacent to our current products and generate new ROI. Each pilot is a bet: upfront decide how much capital to risk, what value it’s meant to unlock, tradeoffs to consider, and the conditions under which you’ll scale or stop. Ultimately, it's about prioritizing the one(s) that provide the most enterprise value. Which pilot types is your organization running, and how are they moving the needle on your business outcomes? I am currently talking to leadership teams to apply this framework to drive clarity and ROI; if your organization is navigating similar questions, I'd be happy to connect. #productstrategy #aistrategy #aitransformation

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Cloud compute costs are one of the biggest drains on enterprise AI budgets. The latest Domino Blueprint shows how to use spot instances to cut costs by up to 90%, without sacrificing the workloads that matter. A practical guide for admins and data scientists: https://gag.gl/Q96Qmn

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Cloud cost uncertainty isn’t a fluke; it’s by design. Cloud providers profit from complexity: 💸 Granular billing 💸 Shifting discount programs 💸 Opaque pricing for AI workloads 💸 Exploding SaaS and shadow IT The result? Engineering and FinOps leaders are left scrambling to explain unpredictable cloud bills while innovation can’t slow down. Traditional dashboards and static budgets won’t save you. You need adaptive cloud cost management that embeds cost awareness into engineering workflows, detects anomalies early, and drives shared accountability. We break it all down in our latest post, including 5 steps to start leading through the chaos. Read it here: https://lnkd.in/ge4jadxr #FinOps #CloudCost #EngineeringLeadership #CloudComputing #AI #SaaS #Ternary

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Ending the week on a strong note, with Forbes recognizing the importance of Postgres in the AI race, and the exciting position EDB is in at the convergence of data, AI, sovereignty, and open source. Kevin Dallas - "We are very clear on what our platform must deliver. Companies want open source (Postgres); transaction, analytics, and AI capabilities, a single pane of glass through which to view their large data estates, shorter time to implement AI agents, and the power to decide where the company runs its agentic AI applications” https://lnkd.in/eamkJMaE

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ARR growth rates for public SaaS co's in Q1: Rubrik 54% PegaSystems 50% AppLovin 40% Palantir 39% Klaviyo 33% Monday 30% Gitlab 27% Cloudflare 27% Snowflake 26% Confluent 26% DataDog 25% SentinelOne 23% Zscaler 23% MongoDB 22% Crowdstrike 20% Veeva 19% Atlassian 19% Nutanix 18% ServiceNow 17% Elastic 16% Hubspot 16% Palo Alto Networks 15% Procore 15% Appian 14% Workday 13% Okta 12% UIPath 12% Purestorage 12% Asana 9% Salesforce 8% Docusign 8% PagerDuty 8% Box 4% Zoom 3% ZoomInfo -1.40%

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This chart explains the Confluent acquisition. ARR growth declined from ~70% to 19% as the business scaled past $1B in ARR, with no clear signs of re-acceleration. At that point, valuation pressure is inevitable - and a strategic sale starts to make sense. Want the full breakdown? Click the link in the comments below. #Confluent

Proud to share that CData Software is a featured MCP launch partner on Databricks Marketplace—alongside AI leaders like Glean, You.com, and Dataiku. 🚀 While these companies are redefining how enterprises interact with AI, CData is solving a critical piece of the puzzle: giving AI agents production-grade access to live data across 350+ enterprise systems. Our Connect AI platform enables Databricks Agent Bricks to securely read, write, and act on data from NetSuite, Salesforce, SAP, ServiceNow, and hundreds more—with the semantic intelligence and reliability that enterprise AI demands. Deploy agent connectivity in minutes. No complex pipelines. Production-ready from day one. Grateful to Ariel Amster & the entire Databricks team for including us in this launch. Excited to help enterprises deploy AI agents that can truly understand and act on their business context. Learn more: https://lnkd.in/etWASk-J

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