All Insights

A family business with more than a century behind it wanted AI on its own files, with none of its data leaving the building. The answer came down to four areas to define, and none of them involved training a model.

Your buyers ask an AI assistant for a shortlist before they ever call you. What SEO, AEO, and GEO each win, why standard reports miss it, and how a midsize company should measure all three.

Every major business AI plan now promises not to train on your data. What separates ChatGPT, Copilot, Claude, and Gemini is what the AI can find, who can read it, and how long it stays.

Most inventory policies are built for the plan. Responsiveness is won or lost after it, when real demand and real supply show up. Where AI closes that gap, with pilot results.

A medical practice wanted AI for its most repetitive work. Its choices were to ban AI or scrub sensitive data by hand. Why an approved tool alone won't stop shadow AI, and what will.

Western Pennsylvania is building again. Here is where a manufacturer should start with AI to keep that growth profitable: materials, supply chain, people, and teams.

Getting started with AI consulting usually costs a small or midsize business $5,000 to $35,000. There is no universal AI SKU, so the step you choose matters more than the price.

Pittsburgh spent two decades building a deep AI and robotics economy. What the Tech Council's new member breakfast showed about how the region's manufacturers, distributors, and service firms can grow with it.

Sovereign AI is a company taking responsibility for its own intelligence. Here is which AI capabilities to keep under your control, what you can safely outsource, and what it takes to start.

The technology will keep changing and so will your business. Your most important AI decision is choosing the partner you trust to help you steer through everything that comes next.

America's oldest bank could have waited for a vendor. Instead BNY built its own AI platform, named it Eliza, and enabled every employee. Here is the three-phase blueprint, and what it means for a business your size.

Someone on your team pasted the customer list into a chatbot this week. Whether that just leaked depends on one decision most companies never made on purpose. Here is what happens to your data, and how to take back control.

Six times in two centuries, a new technology reset the rules of business. The outsized returns went to the companies that grew, not the ones that only cut costs. AI is the seventh wave, and a growth opportunity first.

You removed the constraint that was holding the business back. What you got was not a finished state. You got a clear view of the next constraint, which was there the whole time.

Most AI arrives as one more tab to check. The deployments that survive a real workday put the intelligence behind a screen your team already knows, and two client stories show what that takes.

Companies have quietly accepted a payback period on AI they would have rejected for any other technology. The fix is structural, and it starts with how the deal is built.

Somewhere a good company is on its third month of AI readiness assessment and still has not started. The way to get ready for AI is to start moving, with governance and risk as the opening phase of the work.

The best AI starts look inward first: a goal worth pursuing and a few lightweight guardrails that keep you in control. It is the opportunity in front of every SMB, and one you are already equipped to seize.

Every generation of planning technology delivered a step change, then flattened. After 30 years inside those systems, I can name the ceiling: deterministic math. The compute to finally move past it just became affordable.

A computer that runs a 200 billion parameter AI model now costs $4,000 and fits on a bookshelf. Apple, NVIDIA, and AMD just made owning your intelligence a decision one executive can sign.

A Kaiser shipyard built a full Liberty ship in four days during World War II. Most AI implementations still take the better part of a year. The shipyards prove speed was never the problem. Method was.

Most AI pilots are built to look impressive in a sandbox, then stall before they reach production. The companies earning real returns skip the ritual, pick a use case that pays, and ship into reality.

AI is not one thing. Building your own capability means making five elements play together: your tools, your agent workflows, your data fabric, your machine learning models, and the orchestration that conducts them. Meet the Intelligence Orchestra.

For two decades, companies offloaded development onto employees and called it empowerment. With AI, that hands-off habit becomes a strategic liability. AI fluency is built through coordinated, company-led enablement, not left to individual initiative.

For the first time on record, small businesses are adopting AI faster than large enterprises. The cheap tools that opened the door will not stay cheap. The lasting advantage comes from owning your intelligence, not renting it.

The race to deploy AI agents is real and the momentum is genuine. It also risks generating expensive noise if it is solely concerned with speed and not directed toward generating understanding that was not possible before.

Governments are spending $100 billion to avoid renting their AI from foreign hyperscalers. The same logic applies to your company. A sovereign AI operating system is not a product you buy. It is a capability you build and own.

Replanning feels productive. The alerts clear, the numbers make sense, the supply chain looks manageable. Until reality unfolds again. There is a better question to ask.

Most companies are treating AI like a hotel room, paying nightly, leaving no lasting mark, and checking out when the rate goes up. There is a better way: build an Intelligence Estate.

Taking new technology to market is a team sport. Learn how creating a partner ecosystem can accelerate commercialization and maximize the impact of your innovations.

How do we ensure AI is a net benefit to humanity? Exploring foundational values that human potential and experience should be enhanced and not diminished in the emerging age of AI.