A.Team

Many AI strategies will fail. They'll fail because most companies will treat AI the way they treated every prior software shift. That old playbook is where enterprise AI pilots go to die in 2026. Read more: https://hubs.la/Q04dysrJ0

There's a trap most enterprise planning teams have fallen into, and the insidious thing about it is that it looks like competence from the outside. The team is diligent. They pull from every relevant source. They reconcile the numbers carefully. They build a pre-read leadership can trust. They do it every month, and they do it well. The trap is that doing it well takes most of the cycle. By the time the slides are ready, the data is already aging. Questions leadership asks in the room go unanswered until next month. Decisions that should happen in the session get deferred, because the context that would inform them isn't there yet. A Fortune 200 CPG partner asked us to compress the cycle. Their planning team was experienced and well-resourced, running 80% of their capacity on assembly and 20% on analysis — not because they were inefficient, but because the architecture underneath had never been designed to do anything else. We rebuilt it around continuous ingestion and an intelligence layer that owns the assembly work. Slide generation dropped from 1-2 hours to 90 seconds. For the first time, the planning team was spending most of their time on the questions only they could answer. Read the full story: https://hubs.la/Q04dxrkh0

Traditional enterprise AI is assistive — a chatbot answers questions, a copilot suggests edits, a dashboard surfaces data. Agents are different. They monitor conditions, make decisions within defined parameters, and take action across systems autonomously. The question changes from "did the right person see the insight?" to "did the right person define the right goal?" Which is why centralizing these deployments in IT is the wrong move. Read the full breakdown: https://hubs.la/Q04dyFZy0

The unfortunate truth: static implementations never compound. A dashboard you bought and deployed in 2023 is the same dashboard in 2026 - nothing changes unless your team does it manually. But for AI automation to be successful, the system has to be dynamic. We built the dynamic intelligence layer of AI for a Fortune 200 CPG partner's monthly S&OP cycle. By month six, the system had absorbed twelve planning cycles worth of decisions, debates, and outcomes. It knew which questions leadership consistently asked. It knew which retail channels required which explanations. It knew what the team had tried before and what had worked. That knowledge became queryable. When team turnover happened, as it does, the institutional knowledge didn't leave with the person. The new team member inherited a system that knew everything because it learned. Onboarding time compressed. Context transferred. The organization's planning intelligence became genuinely durable. Read the full breakdown: https://hubs.la/Q04dxxzl0

Love this read by Crawford Del Prete on "Agent Ops." It's spot-on. At A.Team, we have found that one of the keys to generating lasting ROI from an AI investment/program is to have it ingrained into your workflows, and the ROI gets even better as you adapt the workflow to the new efficiency or capability the agent brings. This is not just a function of defining and managing the organizational change of an improving workflow, but also managing (and improving!) the agents that are performing the work. It's a compounding loop of improvement that drives exponential results. Crawford writes: "Advising on Agent Ops strategy without owning any of the implementation pain will produce the same shelf-ware that enterprise transformation projects have always produced." We own the build, implementation pain, and "AI Ops" (as we call it) right along with our customers and have found that service matches the "Rough Job Description" in Crawford's article almost identically and is an essential ingredient to driving great results. https://lnkd.in/eMm4KRjR

In your last planning cycle, what share of time went to building the pre-read versus acting on what was in it? A Fortune 200 CPG partner ran a sophisticated monthly S&OP cycle across multiple brands, channels, and retail partners. Their team was experienced and well-resourced. Data landed around the 15th. The next two weeks were spent pulling from media performance systems, POS feeds, retailer portals, syndicated data, and trade promotion tools, reconciling them against each other, then building the slides. But, by the time the slides were ready, the data was already aging. The questions leadership asked in the room went unanswered until next month. The decisions that should have happened in the session got deferred, because the context that would inform them isn't there yet. We compressed that process and rebuilt the cycle around an intelligence layer that connects it all to help the team make better faster decisions. Within the engagement, the team identified $7M in incremental revenue that had been obscured by the manual process. Not because the data wasn't there, but because the assembly work had consumed the capacity that would have found it. Read the full case study: https://hubs.la/Q04dxgtQ0

We’re excited to welcome Debra J. Bass to A.Teams CxO Network as an AI CMO in Residence! With 25+ years of experience, Debra has led major transformations at J&J Consumer Companies (pre-Kenvue) and Procter & Gamble — building and scaling brands across CPG, medical devices, and health tech from multinational corporations to growth stage startups. As Founder & CEO of Bass Collective LLC she advises C-suite leaders, boards, and investors on growth strategy and brand building. Now, she's bringing her expertise to A.Team. Her take: “AI will fundamentally reshape how companies create and capture demand — enabling real-time personalization, predictive insights, and continuous optimization at scale. Future advantage lies in blending human ingenuity with AI-driven precision and efficiency." Welcome to the team, Debra J. Bass!🚀

Headed to POSSIBLE 2026 to dig into one of the biggest questions brand CMOs are asking right now: how the shift to agentic systems is changing the way marketing actually operates. Enterprises aren't short on data. They're short on data their systems can act on. Most of it sits fragmented across functions and optimized for reporting — built to answer "what happened" in a slide, not to drive a decision in real time. Agents need data that reflects current state, carries context, and maps to the decisions a team actually makes. Over the past year, A.Team has partnered with some of the largest CPG and retail companies in the world to build this layer of AI — moving organizations from disconnected pipelines to decision-ready environments. Connecting sources, enriching signals, and shaping data so the systems meant to act on it actually can. If you're in Miami, send me a DM -- I'd love to trade notes on where we're seeing the real unlocks. #MeetMeAtPOSSIBLE2026 #POSSIBLE2026 POSSIBLE

Your team has access to the latest AI tools and models – does that make your org ‘AI-future built’? Not exactly. More AI tools have led to more fragmentation – data silos, tool sprawl, low adoption rates, and a growing distrust and disconnect in what teams should prioritize. This has made the competitive strategies and personalized customer experiences promised by AI increasingly difficult for F500 brands to achieve. Wondering how top marketing teams are breaking through the noise and turning AI experiments into a competitive asset? Find out in our latest issue of Build Mode 👇

The 𝐒𝐭𝐚𝐧𝐟𝐨𝐫𝐝 𝐇𝐮𝐦𝐚𝐧-𝐂𝐞𝐧𝐭𝐞𝐫𝐞𝐝 𝐀𝐈 𝐈𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐞 𝐀𝐈 𝐈𝐧𝐝𝐞𝐱 𝐑𝐞𝐩𝐨𝐫𝐭 𝟐𝟎𝟐𝟔 offers a useful lens into how #AI is showing up across industries. Looking specifically at 𝐂𝐨𝐧𝐬𝐮𝐦𝐞𝐫 𝐆𝐨𝐨𝐝𝐬 𝐚𝐧𝐝 𝐑𝐞𝐭𝐚𝐢𝐥: AI adoption is highest in #marketing and #sales, followed by #serviceoperations and #IT. This aligns with where #retailers focus when it comes to driving revenue and executing in-store. What is interesting is that early adoption of AI agents is emerging in these same areas, which suggests a natural progression from insight to execution. In #marketing, teams are moving toward more continuous decision-making with platforms like A.Team, Buynomics, i-Genie.ai, Newton Research, Nimble ... helping connect signals to action. In store operations, solutions such as Ariadne Maps, ShopperAI, Worlds, Omnistream, YDISTRI, Metreecs (YC F24) ... are improving visibility into how customers engage with physical spaces. One area that remains early is #supplychain. Adoption of #AIagents is still limited here, even though the potential impact is significant. #Startups like Auger, PAXAFE, Intelo.ai ... are beginning to address this by connecting fragmented data and enabling more real-time decision-making. #Retail is starting where decisions are frequent and measurable and then expanding outward. At Microsoft for Startups, we are seeing strong interest from #retailers looking to move beyond pilots and into production using solutions from the Pegasus portfolio. If you are exploring how this applies to your organization, happy to connect. 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐭𝐡𝐞 𝐝𝐞𝐭𝐚𝐢𝐥𝐞𝐝 𝐫𝐞𝐩𝐨𝐫𝐭 𝐡𝐞𝐫𝐞: https://lnkd.in/gHE4KJ4t