Resolved.

Resolved has been named to the 2026 MSPs to Watch list. 🎉 The recognition comes from MSP Summit as part of the MSP 501 program, which evaluates managed service providers on recurring revenue quality, efficiency, and year-over-year growth. This year, 130 companies made the Watch list. The driving force of our success is the work our team does every day:  • Answering the phone in 60 seconds.   • Building AI workflows that hand real hours back to senior staff.  • Earning our seat in clients' boardrooms. So, this one belongs to the whole team. Our team, who treat our clients' time like the billable asset it is. Thank you. We're just getting started. #MSPsToWatch #MSP501 #ManagedIT #ManagedAI #AEC #Architecture #Engineering #Construction #TechnologyStrategyPartner

Bloomberg reports Starbucks, which spends $400M a year on software, is building AI-assisted in-house replacements for its Microsoft inventory and IBM maintenance platforms. The market noticed: the stock had its best day in two months. Days earlier, Palantir's Alex Karp said it plainly on CNBC: models alone are not enough. Companies need an application layer on top, the software that makes AI safe, useful, and precise. Investors just rewarded a coffee company for becoming a software company. When AI is wired into an application layer built around how your business actually operates, it actually creates enterprise value. Traditional software that only half fits is a tax on every workflow, every day. Bolt AI on top and you just automate the tax. But look at what the Starbucks move actually takes: • Someone deeply strategic who knows what AI is really capable of. • Who decides what gets built and what doesn't. • Who shapes workflows around the business and the business around better workflows, with guardrails. • Who is accountable for business outcomes, not for shipping tools. Hire that team, then redesign processes around it. That's the tanker an enterprise has to turn. Small and mid-market businesses don't have that role on the org chart. No knock on their people. And most don't have the budget without taking massive risk. Your IT team is excellent at what you hired them for. Driving technology strategy is a different profession: what to build, what to skip, fitting software to workflows, measured in results. No mid-market firm carries it in-house, for the same reason you don't keep a law firm on payroll. The work is real, it's specialized, and it only makes sense shared... sounds familiar. But traditional software vendors won't fill it: one product for everyone, never custom enough for how your firm runs. That's where an AI & technology strategy partner comes in. We sit on your side of the table: which workflows, which modules, which guardrails, measured in business results, not features shipped. The expensive part is already done for you at Resolved. - a fully built, secured, maintained base application layer, so fitting it to your firm is cost-effective customization, not a seven-figure build. Here's the part nobody says out loud: once the base layer exists, the advantage flips. Starbucks has to turn the tanker. You're trimming sails on a sailboat. This has been our bet from the beginning, and why we have a head start. We built Resolved OS for ourselves first: our whole company runs on a custom application layer that weaves AI into how we work, our processes redesigned around this new way of working. Now we're building the client-facing version starting with one industry: AEC. With advice from our peer group of AI integrators at AEC firms, every module shaped with them. The AI proposes. Your people authorize... guardrailed agentic AI workflows are the natural next step in application layer maturity. We're working on it.

When a firm's technology starts to hurt, the instinct is to hire one person to own it. For most AEC firms under 150 people, that solves the wrong problem. It fixes the day-to-day. Tickets get answered, things break less often. But two things show up that have nothing to do with technical skill. First, the gap is almost never support. It's strategy. Most IT people are exactly that: technical professionals who keep systems running. That's a real skill. But it's a different job from asking whether the lights should be on at all, what the firm is trying to achieve over the next two years, and which technology decisions actually get it there. You can have every technical box checked and still bleed time and margin, because no one is treating technology as a business decision. Second, the person who fixes everything becomes the person you can't afford to lose. The documentation is always thin, because they're too busy fixing things to write it. When they leave, or just take a month off, you find out how much lived in one head. I've watched firms unable to change their own website or billing system because the one person who understood it was gone. That isn't efficiency. It's a risk sitting on your balance sheet. So the real question was never "in-house or outsourced." It's this: Who owns technology as a business decision here? And what happens if the one person who understands our systems is gone next week? And to be clear, the answer isn't "fire your IT person." If you have someone good, the answer is usually co-managed. They stay, keep the context and the relationships, and finally get a team behind them: a Fractional CIO setting direction, documented systems, and backup for the work that's too big for one set of hands. The single point of failure goes away, and they get to do the interesting work instead of living in the ticket queue. And if the honest answer to "who owns strategy here" is "no one, really," the seat you're trying to fill was never another technician. It's a Fractional CIO, the strategic seat that sits empty in most firms your size. I wrote up the full tradeoff here: https://lnkd.in/gvzxr9Td

The firm most likely to win with AI in architecture and engineering today isn't the biggest one in your market. It's the 30- or 40-person shop down the road. For a decade, scale won. The giants could afford the enterprise software, the dedicated IT team, the pilot programs. If you ran a small practice, you waited for the tools to trickle down to your budget. The constraint this time around isn't money or headcount. It's how fast you can change the way your team actually works. And that's a game small and mid-market firms are built to win. The firms that moved first, back in 2023 and 2024, built on strategies and models that weren't ready. Now they're carrying the sunk cost and the internal politics. They spent a fortune to be early, and they can't move now that it finally pays off. Meanwhile a 30-person firm can shift how it works in a quarter. No committee. No two budget cycles. One conversation. Here's the part most people miss: using AI isn't the moat. It's fast becoming a commodity. What matters is the layer you build on top of it, the workflows and guardrails that turn a clever tool into output a licensed professional can put their name on. It's handing your senior people back the hours busywork steals. It's connecting and assembling your data so you can run models against it, unlock business insights, and make better decisions. It's knowing what your projects actually cost, so you can price the next one accurately and win more of the work you want. The goal was never to be first. It's to be a firm that gets it right the first time and sticks the landing. If you run a small or mid-market AEC firm, this window is open right now. For once, it favors you. I wrote up the full argument here: https://lnkd.in/g-akF8iv

95% of corporate AI projects deliver no measurable return. That stat gets quoted as proof AI is overhyped. I read it the opposite way. For the better part of a year I have worked close to 24/7 on one question: what it takes to build a business around AI, not bolt it on the side. Re-founding Resolved around it. MIT's NANDA report found 95% of AI deployments produced no measurable P&L impact. McKinsey agrees: 80%+ of companies see nothing on the bottom line. The easy answer is the tech isn't ready. But the successful 5% use the same models everyone else has. So, the model isn't the problem. The real cause, in both reports: companies force AI into existing workflows instead of redesigning the work around it. That gap has a name. The application layer. The models are an interchangeable commodity. The application layer is the asset. Here is how I think about the model. Every new session is like a clone with no memory and a 150 IQ. Brilliant at solving problems, and it knows nothing about you or your business until you tell it. Raw intelligence, zero context. That is why a non-technical professional opening a chat app rarely gets a return. Feeding it the right context, guardrails, and process is homework most people never signed up for. So the options are short: use an application that already wraps AI around your workflow, build your own layer if you are big enough, or get trained to pilot a model yourself. A raw model with no layer around it does not return the investment. That is my stance. Alex Karp put it less politely this week. Palantir's CEO called the AI industry "effing insane" on CNBC and said businesses pay for tokens that create no value. I agree. The tokens were never the point. The value is the layer around them. I just got back from Flow, where operators actually shipping this compare notes. The most useful thing I brought home: how to find the work worth automating. Follow the human glue. Watch where people copy, paste, and re-key data between systems. Map the handoffs nobody owns. Pick one painful, repeatable flow, automate it with tools you already run, measure the outcome, then do it again. Keep the guardrails simple: let AI decide what runs and when, but only allow changes through pre-approved paths. We built our own layer and proved it on ourselves first. Today it saves my team 200+ hours a month, another 160 from automation. Governed from day one, every deployment tied to a number it should move. The change has been bigger than I expected. AI absorbed the busywork nobody misses: lookups, first drafts, re-keying. What is left is the work that stays unmistakably human: strategy, judgment, intent, and the relationships behind every project. Our people moved up the chain. That is not a projection. It is what happened here first. AI isn't failing companies. Strategies are skipping the layer that create real-world win conditions. The winners won't have the best model. They will be the ones willing to rebuild business processes around the new capabilities of an application layer.

Happy Independence Day to our US clients and our team in Chicago. Kick back, enjoy the show, and let the fireworks do the talking. Happy 4th from all of us at Resolved. #IndependenceDay #July4th #Chicago

The work we do is measured in one currency: time given back. So, this Canada Day, we hope you get some of yours back too. Happy Canada Day from all of us at Resolved. #CanadaDay

Written by a human. AI and automation are changing the way we work—and changing who gets to shape the future of our industry. Not long ago, setting industry standards was something reserved for the largest vendors and most established organizations. The rest of us simply followed along. Today, small teams and even individuals can use automation and AI to create experiences, workflows, and service levels that raise expectations across the board every day. When we start delivering at this level, we elevate the benchmark. What was once considered exceptional quickly becomes the new expectation. This raises an interesting question: If you aren’t actively embracing automation and AI, how do you know how your customer experience compares to the rest of the industry? If you’re not part of the community, the conversations, and the innovation happening around you, how do you know how you measure up against your competitors? The organizations that will lead the next decade won’t necessarily be the largest. They’ll be the ones willing to challenge traditional processes, adopt new tools, and continuously raise the bar for the people they serve. The future isn’t waiting—and neither are your customers. #automation #msp

We spent a week making AI do real architecture work. Not marketing demos. Not hypothetical scenarios. Actual project tasks that eat up hundreds of hours across A&E firms every year. Chris Briggs and I sat down with Claude (Anthropic's AI assistant that is currently the front-runner by a mile) and ran it through six tasks that architecture and engineering professionals deal with every week:  • Drafting an RFI response for a cladding coordination issue  • Reviewing a joint sealant spec and flagging non-standard provisions  • Calculating occupant loads under the National Building Code of Canada  • Turning messy OAC meeting notes into organized action items  • Analyzing 12 months of project hours data for budget variance  • Writing a client email about a milestone delay Some of the results were genuinely impressive. The meeting minutes task that usually takes 30-60 minutes? Done in seconds, with accurate action items and deadlines pulled from context. Others required a very honest conversation about limitations. AI doesn't know your projects, can't produce drawings, and absolutely does not replace professional judgment. It also comes with accepting a good amount of risk. We wrote the whole thing up with honest assessments of each test, what worked, what didn't, and what we think it means for firms that are serious about staying competitive. If you run an AEC practice and you haven't tested these tools on your own work yet, this is a good place to start. Full results: https://lnkd.in/gHUAMp3P #AEC #AIinArchitecture #ITStrategy #FractionalCIO