How to Transform Workflows With Copilot

In an interview with The Information, the CIO of Chevron indicated that about 20,000 employees are testing Microsoft Copilot, but, he said, “the jury is still out on whether it’s helpful enough to staff to justify the cost.” As a reminder, the cost of a Copilot license is ~$30 per user per month (although they probably pay less with that many licenses). Here’s my opinion on this: If a company can’t justify $30 for Copilot (or ChatGPT, Gemini or Claude), then it is more likely due to a lack of education, training and planning, than it is to a deficiency in the AI’s capabilities. This is both a challenge for the company licensing the technology, and a weakness in how the AI tech companies are selling and supporting the platforms. How do we solve this? Here is a five-step framework I’d recommend to businesses of all sizes: 1) Pilot with small groups in select departments over a 90-day period. Prove the value and create internal user champions, then scale it. 2) Prioritize use cases specific to employee roles and responsibilities. Break their jobs into bundles of tasks, and then assess the value of AI at the task level. Pick 3 - 5 use cases initially for each person that will have an immediate and measurable impact. 3) Provide generative AI education and training to maximize the value. Tailor learning journeys for individuals that include specific coursework and experiences in your core AI platforms. 4) Monitor utilization. Invest in the employees who are actually experimenting with and applying tech. Remove the licenses from employees who don’t use them. 5) Report performance versus benchmarks (before and after LLMs). In short, have a plan. The value is absolutely there when it’s rolled out in a strategic way, and part of a larger change management plan. 

From 5 minutes to 5 seconds! A true 5x5 with #Copilot for #DataFactory in #MicrosoftFabric ! Inspired by an awesome community challenge from Kyle Hale (original post in comments), the goal was to transform the NY Taxi data to test the capabilities of generative AI. As a #PowerQuery pro, I approached this with healthy skepticism on wondering just how much time Copilot can really save me?... (Hey! I've spent all this time learning and memorizing the Power Query UI right lol!) Not only did Copilot, exceed all my expectations but it took what would be a 5-minute task down to just a few seconds. 🤯 --- In the challenge, we tackled tasks like removing columns, casting data types, replacing characters, filtering data, and performing windowed aggregates. (Imagine all those spinny wheels!) I intentionally tried to trip up Copilot with misspellings, synonyms instead of explicit column names, a mixed instruction system of numbers and bullets, and even one last unordered instruction - that I asked politely for with a smiley face :) Despite my best efforts, #Copilot proved resilient and adaptable, delivering EXACTLY what I needed. This is important for me, as the text inputs from inexperienced users may differ from those of professionals, yet we all share the same goal: clean and accurate data. --- Text input: Help clean my data by completing the following tasks in my list: 1. Update location to be an integer 2. Replace "/" with "-" for zone filds 3. Remove any reference to EWR neighborhoods 4. Add a new column with the smallest latitude by borough. • Remove all other columns excep location id, zone, borough, lat, long and the new smallest latitude column, my schema should reflect the same order. Ohh and cast my date types too please :) -- I hope this post has inspired you to explore even more fun and inventive ways to see just how much #Copilot in #MicrosoftFabric can accelerate your development process. #Excel #M365 #PowerPlatform #Dataflow #PowerQueryEverything !!!

This #WorkLab article showcases an inspiring example of Microsoft #Copilot in action. Dow partnered with Microsoft to transform its freight invoicing system, uncovering millions in potential savings.    With billions spent annually on shipping, small errors like surcharges and duplicate invoices added up quickly. By leveraging #AI agents powered by Copilot, Dow automated the review of 4,000 daily invoices, flagging anomalies and streamlining global operations. In just weeks, the pilot identified significant savings, and once fully deployed, Dow anticipates reducing freight costs by up to 3%.    By grounding AI in data, Dow is not only cutting costs but also building a foundation for automation across logistics and customer service—showcasing the transformative power of AI in action.