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For years, FinOps has been:

➡️ Dashboards

➡️ Reports

➡️ Monthly Reviews

But the future is 𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝, 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞, 𝐚𝐧𝐝 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞.

The focus is on '𝐖𝐡𝐚𝐭 𝐰𝐢𝐥𝐥 𝐡𝐚𝐩𝐩𝐞𝐧 𝐧𝐞𝐱𝐭?' instead of '𝘞𝘩𝘢𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘦𝘥 𝘭𝘢𝘴𝘵 𝘮𝘰𝘯𝘵𝘩?'

Here is what an AI-first FinOps Ops Center looks like ↙️

🤖 𝐀𝐈 𝐂𝐨𝐩𝐢𝐥𝐨𝐭𝐬 that explain spend changes in easy to understand language

📊 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 based on usage, seasonality, and product growth

📈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐀𝐧𝐨𝐦𝐚𝐥𝐲 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 with context (who owns it, what changed, what is the likely root cause)

🔣 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 that show cost impact before a feature is deployed (models that work well with well-architected framework)

☑️ 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐬 proposing right-sizing, commitment purchases, or architecture trade-offs

Now imagine this becoming your real-time FinOps cockpit:

🪶 Engineering asks the AI: '𝘞𝘩𝘢𝘵 𝘸𝘪𝘭𝘭 𝘣𝘦 𝘮𝘺 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘤𝘰𝘴𝘵 𝘪𝘧 𝘵𝘳𝘢𝘧𝘧𝘪𝘤 𝘥𝘰𝘶𝘣𝘭𝘦𝘴?'

💵 Finance asks: '𝘏𝘰𝘸 𝘸𝘪𝘭𝘭 𝘰𝘶𝘳 𝘤𝘭𝘰𝘶𝘥 𝘣𝘪𝘭𝘭 𝘣𝘦𝘩𝘢𝘷𝘦 𝘯𝘦𝘹𝘵 𝘲𝘶𝘢𝘳𝘵𝘦𝘳 𝘣𝘢𝘴𝘦𝘥 𝘰𝘯 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘨𝘳𝘰𝘸𝘵𝘩?'

📦 Product asks: '𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘦𝘥 𝘶𝘯𝘪𝘵 𝘤𝘰𝘴𝘵 𝘰𝘧 𝘵𝘩𝘪𝘴 𝘯𝘦𝘸 𝘧𝘦𝘢𝘵𝘶𝘳𝘦?'

The Ops Center becomes the 𝐧𝐞𝐫𝐯𝐞-𝐜𝐞𝐧𝐭𝐞𝐫 for cloud / hybrid decisions!

💡 Here are some real world signals:

• Microsoft Azure Cost Management with AI-driven forecasting

• Amazon Web Services Cost Anomaly Detection

• Google Cloud Predictive FinOps (built into BigQuery FinOps ecosystem)

• Tools like Apptio / CloudZero starting to roll out AI copilots & recommendation engines

This is no longer a theory - 𝐈𝐓 𝐈𝐒 𝐀𝐋𝐑𝐄𝐀𝐃𝐘 𝐎𝐔𝐓 𝐓𝐇𝐄𝐑𝐄!

💭 Thought for today

When AI can simulate, explain, and optimize your cloud costs in real-time... where does the human factor fall in?

#FinOps #AI #ArtificialIntelligence #AIOps #strategy #FinOpsMaturity #Governance 

We always ask what is the business value we are getting by investing into cloud? If I am running a service on cloud, how much am I spending on it Vs how much am I earning by utilizing that service on cloud to provide services to my customers? < Legit question.... right?

Smart FinOps teams ask:

📊 What’s the cost per customer?

📶 Is it per transaction or stream?

🚀 How does that cost scale?

This changes the game - Connecting cloud spend directly to 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲, not just budgets.

💡 Here are some real-world examples that shows how tracking unit economics have helped large scale organizations into achieving their cloud investment objectives:

🎬 Netflix tracks cost per streaming hour - around $73 per thousand viewing hours in 1H23. (Link: https://lnkd.in/gg2TxMev)

🎵Spotify uses user-level metrics (e.g., ARPU and active users) to drive product growth and unit economics decisions. (Link: https://lnkd.in/g5A57Qme)

💭 Here's today's closing thought for you: Will unit economics become the north star metric for mature FinOps teams?

#FinOps #CloudFinance #UnitEconomics #CloudOptimization #FinOpsMaturity #CloudStrategy #CloudFinOps #UnitMetrics #Metrics 

🚀 Cloud financial management is evolving – and it is no longer just “how much did we spend?” or “how much did we save?” but also:

👣 What is the 𝐜𝐚𝐫𝐛𝐨𝐧 𝐟𝐨𝐨𝐭𝐩𝐫𝐢𝐧𝐭 of that workload?

⚙️ How can cost optimization and 𝐠𝐫𝐞𝐞𝐧 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 work together?

🛩️ How can FinOps drive both 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 and 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐚𝐥 outcomes?

🫵 Here are few quick wins that FinOps & Sustainability teams can tackle together:

• Rightsizing and shutting down idle resources → less cost, less carbon

• Choosing regions and instance types with lower emissions

• Forecasting not just spend, but also environmental impact

☁️ All major cloud providers viz AWS, Azure, and GCP now provide emissions dashboards.

Forward-thinking FinOps teams are 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞𝐢𝐫 𝐜𝐨𝐬𝐭 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬.

💭 Here is a thought for you... Will sustainability reporting in cloud become 𝐚𝐬 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 as financial reporting in the next few years? What do you think?

#FinOps #Sustainability #GreenCloud #CloudFinOps #CloudStrategy #CloudOptimization #CloudCarbon #Emissions