Automating Business Processes

Big news: Microsoft just dropped a new AI formula in Excel — =Copilot() Yes… Excel basically has ChatGPT built in now. I created a full guide showing how CFOs, Finance and FP&A teams can put this to work immediately: https://lnkd.in/erM6KiNv And a video guide too: https://lnkd.in/ePYdTaCZ Inside the walkthrough, I show how the new =COPILOT formula can help you: ✅ Automate variance analysis against budget ✅ Generate KPI summaries in seconds ✅ Speed up Accounts Payable workflows ✅ Auto-draft monthly budget memos ✅ Even create Python code for deeper analysis The Excel file also includes 30 finance & FP&A use cases using the =COPILOT formula. You can download it here: https://lnkd.in/eXwB7Vhm A few examples: =COPILOT("Write a one-line variance explanation for", A2:H2) → instant commentary for any P&L row. =COPILOT("Label this row as Risk, Opportunity, or On Track based on actual vs budget", A2:H2) → risk tagging, fully automated. =COPILOT("Tell me whether this is driven by revenue growth or cost pressure", C2) → quick driver classification. =COPILOT("Summarize the top 3 variance drivers for this selection", A2:H20) → monthly snapshot in one click. =COPILOT("Create a CFO-level narrative for this month’s FP&A results", A2:H50) → board-ready storytelling. =COPILOT("Flag any unusual variances that might require leadership attention", A2:H20) → automated red-flag detection. Hope this helps more finance and FP&A teams to use this great new Excel formula!

Deloitte will issue a partial refund to the Australian Federal Government after admitting that AI had been used in the creation of a $440,000 report littered with errors, including three non-existent academic references and a fabricated quote from a Federal Court judgment. It’s a headline that lands right at the heart of the transformation happening across the Professional Services industry. AI can now analyse summarise and even simulate insight at scale. But what it can’t yet do is know when to stop. When to question. When to say: ‘this doesn’t seem right.’ For all the talk of automation, this story is a reminder that the value of human advisors isn’t disappearing, it’s being redefined. AI will take thousands of junior roles in Professional Services firms, but senior judgement and quality assurance remain the last line of defence. In an era where a single model can generate a thousand pages of analysis almost instantly, the firms that win won’t just be those that move faster, they’ll be the ones that still think deeply. And this is exactly where the next generation of Partners will earn their keep. The best will become orchestrators, pairing technology’s scale with human discernment. They’ll know how to deploy AI inside the workflow without losing control of the narrative. Because when the model does the work, but the human still owns the outcome, that’s where trust, and value, still live.

𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐦𝐚𝐝𝐞 𝐰𝐢𝐭𝐡 𝐜𝐥𝐚𝐫𝐢𝐭𝐲, 𝐧𝐨𝐭 𝐠𝐮𝐞𝐬𝐬𝐰𝐨𝐫𝐤. Digital twins take the guesswork out of decision-making by creating a virtual model of your operations that reflects reality in stunning detail. From improving design to reducing downtime, they transform the unknown into actionable intelligence. To simplify the broad range of potential digital twin applications, a classification approach I like to use is called the “𝟓 𝐏𝐬“. This model is easy to remember and covers nearly all use cases of industrial digital twins: • 𝐏𝐚𝐫𝐭 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: Digital representation of individual components or parts typically to understand the physical, mechanical, and electrical characteristics of the part. This allows companies to monitor, analyze, and predict the performance and health of that particular part, optimizing maintenance schedules and extending its lifecycle. • 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: Digital representation of the interoperability of components or parts as they work together as part of a product. This enables companies to simulate and test product behavior under various conditions, improving design, ensuring quality, and speeding up the time to market. • 𝐏𝐥𝐚𝐧𝐭 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: Digital representation of a plant, facility, or system to understand how assets work together at an operational level. This allows businesses to enhance operational efficiency, reduce downtimes, and optimize production processes through real-time insights and predictive analytics. • 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: Digital representation of a specific process or workflow within a system or a facility. This helps companies refine and optimize processes, identify inefficiencies, and ensure smoother and more cost-effective operations. • 𝐏𝐞𝐫𝐬𝐨𝐧 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: Digital representation of a person to capture their movements, habits, interactions, skills, knowledge, and preferences. This helps companies gain insights into workflow patterns, fatigue patterns, and safety concerns ensuring increased productivity and a reduction in workplace-related injuries. 𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐚 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐡𝐫𝐞𝐚𝐝 𝐫𝐞𝐥𝐚𝐭𝐞 𝐭𝐨 𝐚 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧? A digital thread is a continuous flow of data and information that integrates processes, systems, and devices throughout the product lifecycle. It serves as the foundation for a digital twin, which is a virtual representation of a physical product or system, leveraging data from the digital thread to simulate, predict, and optimize its performance. For high-resolution image and to read full version: https://lnkd.in/ezmPkSag ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

I recently built an internal brand agent for Lovable and a lot of people asked how I did it, so here's the exact guide: 1/ Build a brand environment first: I asked Lovable to create a web app where you can view our logo, colors, typography, and all brand assets. This became the database, a single source of truth for everything brand-related. Prompt: "Build a minimal brand hub where team members can browse, search, and download logos, colors, typography, and marketing assets. Should feel like a premium internal tool. Think Linear or Notion. Use placeholder assets so we can replace them later." 2/ Add a home page with an AI agent: I then asked Lovable to build a home page with an AI agent that has access to all of those documents. Prompt: "Add a new page called Brand Agent - a ChatGPT-like chat interface where I can ask questions about our brand." 3/ Connect AI to the assets: The agent can now automatically browse those documents - answering questions, finding assets, and guiding anyone on how to use them correctly. Prompt: "Enable Lovable AI and give the agent access to our brand guidelines. The agent should let users upload a screenshot and tell them if it's on-brand or off-brand with clear reasoning. It should also help find the right assets when asked." The whole thing took less than an hour and it's saving us a lot of time. Hope this helps and now go build you own brand agent today!!

𝗢𝗳𝗳𝗶𝗰𝗲 𝗷𝗼𝗯𝘀 𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝘁𝗼 𝗰𝗵𝗮𝗻𝗴𝗲 — 𝗳𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗺𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸! Let me show you what I mean: ⬇️ Take the example below! Felix Schlenther just shared how he’s using the AI agent Manus AI to handle five parallel tasks in his daily workflow. And no this isn’t about just asking ChatGPT a basic question. It’s structured, autonomous execution — across research, content creation, Excel modeling and more. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝗵𝗲 𝘄𝗼𝗿𝗸𝘀 𝘄𝗶𝘁𝗵 𝗶𝘁: - He defines a clear goal + context for each task (1+ A4 page per task)   - Writes a structured briefing prompt using Gemini   - Sends the tasks to Manus in the morning   - Manus plans, researches, builds files, and delivers results asynchronously   - Around noon, he gives feedback   - In the afternoon, he builds on the outputs 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝗲𝗲 𝘁𝗵𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗳𝗶𝘃𝗲 𝘁𝗮𝘀𝗸𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗶𝗻 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗯𝗲𝗹𝗼𝘄: 1. Build his AI business case calculator in Excel 2. Optimize his AI use case canvas 3. Conduct AI use case research 4. Design a 30-day AI challenge 5. Research DAX40 Chief AI Officers (CAIOs) 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗵𝗲 𝗴𝗲𝘁 𝗶𝗻 𝗿𝗲𝘁𝘂𝗿𝗻? - Around 80% usable output on first run, 90–95% after feedback - Execution time: ~30–60 min per task - Cost: ~$40/day for 5 tasks Most people think of AI tools still as assistants, but this is different. We are pretty close tho have very soon for the first time a "real co-worker" — one that works independently, doesn’t wait for meetings and delivers structured output across tasks. And what really changes here isn’t just productivity. It’s the complete workflow: → Async delegation → Iteration instead of micromanagement → AI-first routines baked into the day Yes, we’re still early. But it’s clear where this is going. We’re moving from prompting to managing complete AI-driven workflows. And I am pretty sure: 𝗢𝗳𝗳𝗶𝗰𝗲 𝗷𝗼𝗯𝘀 𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝘁𝗼 𝗰𝗵𝗮𝗻𝗴𝗲 — 𝗳𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗺𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸!

When rigidity becomes the secret to agility: the Standardization Paradox Most leaders think standardization kills agility. Levi Strauss & Co.’s proved the opposite this week at #SAPSapphire 2026. I was impressed. Jason Gowans, CTO at Levi's, shared that the company now has more than 1,000 AI agents deployed across the company. That number is impressive. But it is not the real lesson. The real lesson is what had to change before the agents could scale. Levi’s is a global business. Different markets. Different habits. Different ways of working. And this creates a familiar leadership debate. Should we standardize, or should we let local teams stay flexible? Most companies choose a messy compromise. A bit of global control. A bit of local freedom. And a lot of invisible complexity. That model worked when humans were the glue. Humans could interpret exceptions. They could translate between systems. They could remember the undocumented rule. They could ask the person in another country how things “really work.” But AI agents change the equation. Agents cannot scale on tribal knowledge. They cannot guess every local exception. They cannot orchestrate a business where every market speaks a different operational language. This is the counterintuitive part. In the age of AI agents, standardization is not about control. It is about making the business legible. That is why standardization becomes a speed strategy. Levi’s gave a great example. Some wholesale orders used to take 2 to 5 days to process because they came through PDFs, emails, and Excel files, with long lists of sizes, colors, and styles. Now, with AI agents built on top of SAP, the same work can take 20 to 30 minutes. This is the kind of shift SAP is helping companies make: moving from fragmented work to AI-powered execution at scale: https://lnkd.in/eWajp4YB⁠ The company had to make work understandable enough for agents to execute it. That is the shift. And that is the only way to become an autonomous company. Where do you think your company is losing the most speed today: too much chaos, too many exceptions, or too little standardization? #SAPAmbassador #SAP #AI #AgenticAI #Leadership #DigitalTransformation #AutonomousEnterprise

When working with multiple LLM providers, managing prompts, and handling complex data flows — structure isn't a luxury, it's a necessity. A well-organized architecture enables: → Collaboration between ML engineers and developers → Rapid experimentation with reproducibility → Consistent error handling, rate limiting, and logging → Clear separation of configuration (YAML) and logic (code) 𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗧𝗵𝗮𝘁 𝗗𝗿𝗶𝘃𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 It’s not just about folder layout — it’s how components interact and scale together: → Centralized configuration using YAML files → A dedicated prompt engineering module with templates and few-shot examples → Properly sandboxed model clients with standardized interfaces → Utilities for caching, observability, and structured logging → Modular handlers for managing API calls and workflows This setup can save teams countless hours in debugging, onboarding, and scaling real-world GenAI systems — whether you're building RAG pipelines, fine-tuning models, or developing agent-based architectures. → What’s your go-to project structure when working with LLMs or Generative AI systems? Let’s share ideas and learn from each other.

A group of IIT'ans from Gurgaon have built an app to report complaints to the government called Raasta Fix... and this starts a whole new avenue for startups to build in... CivicTech... There are two problems of complaining about these problems to the government: 1/ You don't know which agency to write to 2/ You are afraid to disclose your details as you think a disgruntled officer could make your life miserable As an example, the Haryana Govt has several avenues to lodge consumer complaints... there is a CM Window, a Mera Pariwar portal, a CP Gram Portal, eSamadhan, Swachhta App, Jan Sahayak App... And the issue is that some grievances fall under the municipal authority of Gurgaon... MCG, the GMDA, HUDA... and people are confused which portal to go to for which kind of complaint... Now, when I open RaastaFix . com on my device, it asked for my location, and asks you to upload a picture of the problem with a categorization such as garbage dump, stray cattle, electric hazard, stagnant water, construction debris... And the platform automatically submits the complaint to the right authority with geo tagged and timestamped proof without having to disclose the complainant's details. Integration with the backend dashboard of the Government is still pending, and I would hope this resolves problems for the residents of Gurgaon who are tired of poor drainage and road construction among garbage woes. And this brings another opportunity for other states too... If a Zomato like listing platform could be built for cities with ratings and live imagery of potholes, number of days that they have been ignored by the administration, which adds to a social audit of the cleanliness and state of civic sensibilities across cities and states... We might see some transparency and accountability from the administration. The app could monetise from ad placements... and can become a bootstrapped business as a social enterprise. If there are any such built for different cities in India, let me know in the comments please, would love to see. #casarthakahuja

Today we’re presenting the findings from our clients using AI agents, in production for 3 months. We can cut customer wait times stuck in a KYC/sanctions queue from 20 days to 2 minutes. This is a huge unlock for anyone onboarding customers. “Compliance Officer” is the 5th fastest growing occupation in the United States! Major banks average 307 employees just for KYC alone, yet can't hire more compliance officers fast enough. More than headcount, this costs customers and revenue. We deployed AI agents in production environments at multiple financial institutions for 3+ months and show AI Agents can meaningfully improve KPIs: - For one FI, the daily backlog was 14 hours and they couldn't keep up with it.  - So the backlog kept growing  - As did the average customer wait time stuck in a queue, to 20 days. Using Agentic AI, we were able to  - Automate majority (95%) of the cases and  - bring down daily backlog to 41 minutes (from 14 hours).  - Most importantly, avg customer wait time went down drastically to 2 minutes. Perhaps the most counterintuitive finding. Agentic AI when trained and deployed according to our framework, can be more accurate than humans. We found AI agents follow operating procedures in 100% of cases vs <95% for humans. Humans never follow SOP to the minute details and with rote work, they are more error prone. FI's rightly worry, what about hallucination? What about data privacy? Will the regulator allow it These live, production data points are all within existing regulatory frameworks (SR 11-7 compliant). Our Agentic Oversight Framework maintains complete human accountability while delivering: - Alignment to Standard Operating Procedures (SoPs) - A full audit trail of every data element accessed - A full, explained decision rationale, reviewed before every case is progressed - Continuous learning from expert reviewers - Automated drift detection and safeguards The white paper is a playbook for how financial institutions can safely implement agentic AI while fully complying with regulatory requirements. Real results. Real institutions. Real transformation. You might ask: what is AI about all of this and how's it different from ML and rules based systems. In short, rules systems are rigid but Agentic AI can adapt. All those details in the white paper:

The Claude AI HR Plug-In Has Arrived Anthropic has expanded Claude Cowork with a specific AI HR plug-in designed to streamline and automate routine HR tasks. What it can do 1. Draft job descriptions, offer letters, onboarding documents, and performance summaries. 2. Run compensation analyses and generate template-based HR content. 3. Connect to internal systems to pull real-time, accurate data. This plug-in augments existing HR platforms, like Workday or SAP, but does not replace them. It also does not replace your HR team—despite some analyst speculation. The Claude plug- in: . Automates repetitive administrative work. . Pulls and synthesizes data across multiple platforms. . Provides drafts and insights for human review and approval. What it cannot do: . Replace human judgement in (strategic) HR tasks. . Serve as a system of record. The HR plug-in address a long-standing gap by automating tasks that previously required manual coordination between different HR systems. However plug-ins are just a part of the opportunity. The real power will come from combining three elements: 1. System agnostic AI Plug-ins – Draft, automate, and summarize tasks accross HR platforms 2. Systems of Record & In-System AI – Ensure secure, auditable, and compliant actions while leveraging the AI capabilities built directly into the system if record HR platform. 3. Humans – Drive strategy, approve decisions, manage culture, and handle judgment-intensive work. Enterprise transformation happens when AI, systems, and humans are orchestrated together. Plug-ins, in-system AI, or systems alone aren’t enough — orchestration ensures efficiency, accuracy, compliance, and oversight where it matters most. https://lnkd.in/e7qyihpS