Nadim Hossain

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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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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As an Adobe employee and shareholder, I’m frequently asked for my take on the company’s valuation. I think this article explains it best: the market often overlooks the depth and complexity of the enterprise business while overestimating the momentum of VC‑backed companies fueled by the AI hype cycle. https://lnkd.in/eb63sa4A

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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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Quick take on Lovable’s Series B and why it’s getting attention. What’s interesting here isn’t just “AI app building without engineers.” Plenty of tools claim that. Lovable is really selling fast cycle times to turn a rough idea into something usable in days, not quarters. Forward-thinking Data and AI leaders see the problem coming ... and already feel (and you see it in the comments): • The bottleneck isn’t models • It’s translation — turning business intent into working software Lovable compresses that gap. MyPOV for data & AI leaders: • Watch for AI app sprawl with the potential for the app layer to outrun the data layer (e.g., faster than data leaders can standardize semantics and policies) • Decision logic risks getting hard-coded because in the absence of shared context, these apps optimize locally, not enterprise-wide • Data + AI foundation players gain enabling ecosystems to their increasingly consolidating platform that spans data to context and governance • OK, I'll get hate for this ... but agent builders and tools like Lovable are a feature, not a strategy. Enterprise winners will let teams move fast while necessarily focusing on anchoring apps to shared data, semantics, and decision rules. Reminder Friday (a day early): If you don’t provide that data + AI backbone, it’ll get recreated ... and badly ... inside every “easy” AI app. #AIdebt. Lovable's announcement here https://lnkd.in/gMiUcg_7 #EnterpriseAI #DataToDecision #DecisionVelocity #CDAO #CIO #AIArchitecture

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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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🚀 First partnership launch since joining ActiveFence 🎉 . We’re now integrated into Databricks' Mosaic AI Agent Framework, enabling developers and enterprises to build LLM agents with real-time guardrails baked in. This integration allows teams to: - Apply company policies at both input and output level, and enforce it at runtime - Protect agents and users from prompt injections, jailbreaking, and other risks  - Gain visibility into agents behavior, not just outputs As AI agents are rolled into production more widely, I believe this kind of control becomes a must have. Proud to be working with brilliant folks on both sides to make AI not just better, but safer. Thank you for the partnership Maria Pere-Perez, Prasad Kona, Lior Knaany, and Ziv Gabovitch! More to come. #AI #Security #Databricks #ActiveFence #AIagents #Partnerships

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Snowflake and OpenAI partner in a $200M deal to embed GPT-5.2 into Cortex AI. Find out how this changes enterprise AI deployment strategies across industries. #Snowflake #OpenAI #SNOW #EnterpriseAI #AIagents #GPT5 #CortexAI #CloudComputing #DataCloud #GenAI Snowflake OpenAI

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Chris Degnan joined Snowflake as employee #13 (and the 1st sales hire). He scaled the sales org from 0 to over $3B in ARR, spanned four CEOs, and retired as CRO after 11 years. In his first podcast post-retirement, Chris opened his CRO playbook, from early enablement to hiring rigor and fending off threats from competitors. If you’re a founder or running sales at a startup, this one is for you… ➡️ Degnan’s non-negotiable rituals for building a world-class sales org #1: Always – Obsess over enablement. It isn’t a nice-to-have—it should be a top priority. Pick a methodology (like MEDDIC) and hold a weekly enablement session with every rep. #2: Weekly – Every single person in your sales org needs to have a forecast call every week. There should also be a direct team meeting. #3: For milestones – Run sales kickoffs & quarterly business reviews. Chris was shocked by how many startups skip these fundamentals. If you don’t invest in enablement and real coaching systems, he says you’re just asking for failure. Don’t try to reinvent the wheel here. ➡️ Should you hire sales reps in less than 2 weeks? If you’re trying to interview dozens of people for one role, the process is set up to fail. What if you were limited to two people and had to decide if either is your person? Much simpler. At Snowflake, the sales team would limit hiring processes to two weeks. Both sides needed to commit to a decision by the end of that window. If a candidate just “wanted to get to know people” and wouldn’t be ready to sign by day 14, they weren’t in the process. This requires a good pitch to candidates and keeps everything efficient. ➡️ The one thing that makes a great sales leader The best measure of a leader is the performance of their individual contributors. Chris believes great sales leaders do three things: they hire great people, they train those people well, and they drive individual productivity. At Snowflake, his ultimate success metric was whether reps could consistently generate $250K in new ARR per quarter by month seven (after they were fully ramped). Full episode: https://lnkd.in/djfzCd7e

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I am pretty excited about what's going on at Snowflake particularly around automating the front end of data science and analytics. Check out my latest short on the topic below.

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