Energy companies don't have a data problem. They have a workflow problem. Critical operational information exists everywhere: production records, field activity, spreadsheets, emails, vendor systems, accounting platforms, and individual employees who know how everything fits together. When processes depend on manual coordination, businesses spend valuable time moving information instead of moving work forward. By designing a connected workflow platform around existing people

If there's one solution that consistently delivers outsized value for our clients, it's this one. By bringing together Microsoft Copilot, Power Automate, Planner, and SharePoint, organizations turn disconnected information into a connected operating system where data, SOPs, tasks, approvals, documents, and organizational knowledge work together. The result is greater visibility, faster execution, fewer errors, and dramatically less time spent managing work. Many energy compan

The Challenge Large organizations often struggle with a problem that is hiding in plain sight. Critical initiatives require collaboration across multiple teams, stakeholders, and decision-makers. Yet the information needed to make those decisions is frequently scattered across emails, spreadsheets, file repositories, meeting notes, and individual employees' institutional knowledge. As projects become more complex, teams spend increasing amounts of time searching for informati

The Challenge Like many growing manufacturers, this company relied on a large Excel workbook to run critical business operations. Purchasing, estimating, quoting, production scheduling, work orders, vendor management, and billing all lived inside a spreadsheet that had evolved over many years. The spreadsheet worked. But as the company grew, visibility declined, manual effort increased, and critical workflows became dependent on individual employees who knew how the spreadshe

The Challenge Every month, a growing energy company spent approximately 160 hours reconciling trucking invoices across multiple systems. Critical operational data was spread across ERP systems, trucking applications, and land management platforms. Finance personnel downloaded reports, manipulated spreadsheets, investigated discrepancies, and manually validated transactions before producing a final reconciliation. Despite significant effort, leadership still lacked confidence

Pipeline construction projects depend heavily on accurate geographic data. Yet, extracting and working with this data from alignment maps has long been a challenge. Large engineering and construction firms face a time-consuming, error-prone manual process that involves typing complex map details into Excel sheets. This slows down project timelines and increases the risk of mistakes. A recent success story shows how artificial intelligence (AI) can change this. By combining OC

Current Consulting Trend There's a certain kind of meeting that happens a lot right now. Someone has zoomed in from a large firm with a very nice deck. The fonts are clean. There's a slide about "the future of work." Someone uses the phrase "broadscale transformation" and nobody laughs. Two hours later, you walk out nodding, mildly inspired, completely unclear on what you're doing Tuesday. Vs. Assure The unsexy truth about AI is that it works best on boring things

Stop Plugging Probabilistic AI Into Deterministic Workflows There’s a mistake I see over and over again. It’s expensive. It’s subtle. And it explains why so many “AI projects” stall out after the demo. Here it is: LLMs are probabilistic systems. Business processes are deterministic systems. That mismatch is the problem. The Probabilistic Trap A large language model does one thing exceptionally well: It predicts the most likely next token. That is powerful. It

The Real Question: Where Are You Losing Time? The power of this moment isn't really about technology at all. It's about clarity. The question for leadership teams should be: Where are we burning hours on processes that could be automated or streamlined? Where is our team doing repetitive work that pulls them away from strategy and growth? Take a hard look at your standard operating procedures. How much time goes into data entry, report generation, customer follow-ups, sche

There's a quiet revolution happening in enterprise technology, and for once, David has the advantage over Goliath. While Fortune 500 companies struggle under the weight of their Workday implementations, ServiceNow integrations, and Salesforce ecosystems, smaller companies are discovering something liberating: they're not trapped by the very tools that were supposed to set them free. The Hidden Cost of Enterprise Scale Large organizations have spent years, sometimes decades

If your finance team spent the last week of December buried in spreadsheets, manually reconciling year-end numbers, refreshing dashboards, and trying to explain variances line by line—you're not alone. It’s the same grind year after year. But it doesn’t have to be. This January is different. For the first time, enterprise-ready AI tools are available inside the systems you already use. Microsoft Copilot, Gemini, and deterministic small language models (SLMs) now let you ask

Here’s the traditional model: you hire a consulting firm that assigns four people to your team. They interview users, gather requirements, maybe train a model, and build a prototype... someday. Here’s our model: you give us a process that’s breaking your finance team. In 2–4 weeks, we hand back a working solution that runs inside Excel or SharePoint, built on deterministic AI, scoped tightly, and scoped once. That’s it. This isn’t a knock on consultants — it’s a knock on i

If you're still selling an AI product, good luck. This fall, enterprise buyers made a subtle but decisive shift. They weren’t looking for new SaaS logins or another monthly subscription. They wanted results. Inside their existing tools. Without six-month deployments or $500K contracts. What won? Services. Not managed services or staff augmentation. Not "AI consulting" with 100-slide decks. But modular, fixed-scope service blocks that solve a specific problem in 2–4 weeks.

October was the turning point. Microsoft expanded CoPilot across Excel, Teams, SharePoint, and PowerPoint — and it was suddenly clear that many niche AI platforms were on borrowed time. Including some of the tools we’ve deployed for our clients. We’d spent the better part of a year building around a conversational analytics layer for finance. But the moment CoPilot started reading tables and summarizing slide decks inside the Microsoft 365 suite, that model broke. Instead

2025 was the year everyone in the enterprise started playing with ChatGPT. It helped with email drafting, brainstorming, and answering quick questions. But by September, our clients had all hit the same wall: It doesn’t actually fix anything I care about. In operational finance, accuracy matters. Latency matters. Traceability matters. “Just ask ChatGPT” doesn’t work when you're reconciling reports across three ERPs, cleaning up vendor data, or explaining variances to the b

The honeymoon phase is over. According to the Economist , after two years of breathless excitement about generative AI's transformative...

Following up on my recent posts on small language models, I wanted to delve again into how they are revolutionizing business. AI has...

Overview: What CFOs Should Know LLMs (Large Language Models) – Massive AI systems (hundreds of billions to trillions of parameters)...

Following up on my recent posts on small language models, I wanted to delve again into how they are revolutionizing business. When...