In Part 1, I argued that a tracker quietly makes humans the integration layer, and that moving delivery artifacts into the repo hands much of that assembly work to the tooling. 

This is the operating model that fell out of that idea. 

The goal is simple: 

Every artifact should be readable by both a human and an assistant without translation. 

Stories, decisions, sprint history, status updates, technical plans, reports: all of it lives in a form that the team can read directly and that an assistant can traverse without APIs, connectors, or synchronization. 

None of this is exotic. It’s markdown files, Git, and an agent-aware editor like Cursor or Claude Code. 

The discipline is what makes it work. 

Two repos, two rhythms 

The first decision is to stop forcing artifacts and code to share a history. 

They have different cadences, different gates, and different owners. 

So they get different repos. 

The delivery repo holds artifacts only: stories, sprints, decisions, context, prompts, and reports. 

The code repos stay exactly as they should be: feature branches, pull requests, CI, and release workflows. 

Each code repo’s CLAUDE.md points back to the delivery repo so that any assistant reading the code also has access to the project’s intent and decisions. 

That separation solves a surprisingly common problem. 

Trackers go stale because updating them competes with shipping code. When artifacts live in their own frictionless repo, there is nothing competing for attention. A decision or status update takes seconds to commit and lands immediately. 

No connector, no second system 

There is no tracker and no connector. 

The files are the context. The same editor the engineer uses to build is where the assistant reads stories, specifications, decisions, and status. 

That is also why this is a team model rather than a PM productivity hack. 

Managing a project through a Jira connector is something one person does from their own AI session. Here, everyone works from the same source. Engineers build from it. Designers contribute to it. Reports generate from it. 

Nobody waits for a board to catch up. 

The story is a directory, not a ticket 

Our unit of work is a folder, not a line item. 

A story directory holds a few files, each with one job: 

Together they form the work surface. 

When an engineer opens a story, the assistant sees the same picture they do: the requirements, the implementation plan, prior decisions, open questions, and evidence. 

Dependencies are recorded as simple wikilinks such as [[E2-1]], giving assistants a graph they can traverse rather than a collection of disconnected tickets. 

For engineers: a control plane, not PM docs in Git 

It would be easy to read all of this as project managers moving their paperwork into Git. That is not what it is. 

For an engineer, the repo turns delivery artifacts into part of the work surface, instead of a parallel system to keep in sync. 

Start with the thing engineers feel every day: context switching. Recovering intent from a tracker means leaving the editor, finding the ticket, reading a description written weeks ago, and scanning the comments. Here, story.md, technical-spec.md, and grooming.md sit beside the code. You read the contract, the plan, and the reasoning without leaving Cursor, and so does your agent. 

It also front-loads the ambiguity. The spec-drafting prompt asks the agent to flag acceptance criteria that are vague or untestable before any code gets written, so you are not halfway through a build when you realize the AC could mean two things. 

Evidence lives with the work. artifacts/ is the natural home for eval outputs, screenshots, scorecards, generated datasets, and review forms. When someone revisits the story to debug or review, the proof is right there, not scattered across Drive and Slack. 

Dependencies are plain text. A wikilink like [[E2-3]] is easier to traverse than a tracker’s dependency UI, for you and for the agent, and you can grep the whole tree. 

The code ties back to the contract. Code repos point at the delivery stories, and tests validate the acceptance criteria in story.md, so a reviewer checks against a real definition of done rather than “matches the ticket.” 

The quiet win under all of it: no stale duplicate truth. A tracker usually becomes a second copy of what the engineer learned while building, and it drifts the moment the build teaches you something. Here, when the spec changes, the same repo captures the change, the reason, and the evidence, in one place. 

A lightweight engineering control plane, where context, decisions, specs, evidence, and status live where developers and agents can use them directly. 

The coding agent inherits the contract 

When a coding agent opens a story, it inherits the contract. 

The story, acceptance criteria, technical spec, grooming notes, and code all sit in the same context window. 

Before implementation starts, the engineer has the agent draft technical-spec.md from the story and checks that the plan actually matches the intent. 

Misunderstandings surface in prose instead of three commits later. 

Then the acceptance criteria become something the agent can actively steer toward. 

The definition of done is no longer a memory of a meeting. It is a file sitting beside the code. 

That changes behavior. Agents drift less. They gold-plate less. They spend less time solving adjacent problems and more time solving the one the story actually describes. 

In a tracker-centric workflow, those criteria often live somewhere the coding agent never looks. 

The sprint is a narrative, not a board 

If the story is the unit of work, the sprint is the unit of time. 

One sprint. One file. 

The sprint document contains the goal, committed stories, a running log of mid-sprint events, delivered work, and the retro. 

The key rule is append-mostly. 

Committed work stays committed. When reality changes, and it always does, you add an event explaining what changed and why. 

The sprint becomes a readable history instead of a constantly rewritten snapshot. 

That turns out to be useful for both retrospectives and stakeholder conversations. 

Decisions live where their scope lives 

One small rule removes a surprising amount of friction. 

Story-level decisions live with the story. 

Sprint-level decisions live in the sprint file. 

Project-level decisions live in /decisions/. 

Six months later, when someone asks why a choice was made, there is a dated file with the answer. 

Every ceremony ends in a commit 

The decision lives in the file before the meeting ends. 

If “I’ll update it later” creeps back in, the model degrades into Jira-by-other-means within a sprint. 

Planning, standup, review, and retro all follow the same pattern: 

1. Read the repo. 

2. Make decisions. 

3. Commit the outcome. 

The ceremony is the conversation. 

The commit is the memory. 

The same data feeds all of these. 

A single status.md file becomes the morning brief, the risk log, the retro, and the stakeholder report. 

You author once and query many ways. 

Status itself remains human-owned. The assistant drafts it from commits and chat context, but a person reviews and commits the result. Otherwise the no-code days (debugging dead ends, credential issues, design discussions) disappear from the record. 

Where it becomes an asset 

Prompts do not stay prompts. 

A query used a few times becomes a skill. 

The skills, plus the directory structure and conventions, become a scaffold. 

The scaffold becomes reusable. 

The next engagement starts from a template that already knows how stories, sprints, decisions, and ceremonies fit together. 

The cost of standing up a new project keeps dropping because the operating layer already exists. 

One principle keeps that from tipping into over-automation: 

Automate aggregation and synthesis. Do not automate judgment. 

Draft the brief. Draft the retro. Draft the report. 

But decisions, scope, priorities, and commitments stay human. 

The moment you automate judgment, the model quietly rots. 

What it costs, honestly 

It isn’t free. 

The committing discipline is load-bearing, and no tool nags you into doing it. 

It assumes the team works in an agent-aware editor. Open these files in a plain editor and much of the leverage disappears. 

Design tools do not go away. Figma and Miro still exist. The repo simply becomes the place where design decisions and handoffs are recorded. 

There are still open questions too. On a small team without dedicated QA, who formally signs off that acceptance criteria have been met? We are still working that out. 

I’d rather name those edges than pretend the model is finished. 

The honest reframe is that this approach does not eliminate the tracker’s job. 

It absorbs it. 

The consistency Jira enforced for free becomes something you own on purpose. 

For the kind of AI-native work we do, that ownership is the point. 

The repo is already where the work happens. 

So we started asking a simple question: 

What happens if coordination happens there too? 

This operating model is our current answer. 

Not because Git is magical. 

Not because Jira is broken. 

Because operational context now accumulates in one place, and we’re interested in what becomes possible when the people, the artifacts, and the assistants all work from the same source. 

Whether this becomes a broader pattern remains to be seen. 

But after working this way, it is difficult to imagine moving the source of truth back into a system designed primarily to describe the work rather than participate in it. 

The post Pt. 2: Repo-Native Delivery – The Operating Model  appeared first on Robots & Pencils.

Robots & Pencils, an applied AI engineering partner designed for enterprise velocity and measurable business impact, today announced the appointment of Brendan Flynn as Senior Vice President, Strategy within Robots & Pencils’ Studio for Generative and Agentic AI in Bellevue, Wa. Flynn will work with clients to ensure the strategies behind their AI investments scale and deliver the defined business outcomes.

Robots & Pencils has built its business around a solution-delivery flywheel, accelerating the velocity of delivery and client impact with live solutions launching weeks, not quarters. Flynn’s mandate is clear. Deliver the business outcomes for which clients are investing and put the foundation in place for agentic transformation to scale across the enterprise.

A Career Forged in Scale and Speed

Flynn hails from Capgemini, where he led large-scale product engineering and AI transformations for global brands in QSR, hospitality, and enterprise software.

His reputation is built on a rare pairing: the organizational change management expertise to shift how a company works, and the product engineering discipline to make the shift hold.

“Two decades working inside large Global Systems Integrators showed me exactly where the legacy delivery model strains under the demands of enterprise AI,” Flynn said. “That gap is why I’m here. Robots & Pencils was built to close it — and that’s what delivers the outcomes enterprises are chasing.”

Velocity Compounds by Design

The Robots & Pencils operating rhythm compounds by design. Production solutions reach testable market fit in weeks, not quarters, and from there rigorous evaluations turn real usage into the signal that drives learning, optimization, and scale. Flynn’s role is to align that rhythm to how each client runs, so the pace holds once the engagement scales.

“Executives are past the pilot stage. They want to know what changes in the P&L and when,” Flynn said. “Starting from proven patterns moves the conversation from ‘can this work’ to ‘what does this deliver, on what timeline, and what has to change in the organization to keep it running.’”

A Leader Built for This Moment

“Agentic AI enables companies willing to transform how the work itself gets done, not the ones that bolt it on,” said Len Pagon Jr., CEO of Robots & Pencils. “That rethinking takes a strategy set before the first line of code. Brendan has solved that problem before, at real scale. He’s been the client, and he’s been the partner. That combination is rare. Clients feel it in the first conversation.”

Built on AWS. Driven by Outcomes.

Flynn’s appointment follows recent company milestones, including AWS Advanced Tier Services Partner status, one of only 11 inaugural AWS Pattern Partners selected from the global AWS Partner Network, the addition of Robots & Pencils’ Studio for Generative and Agentic AI in downtown Bellevue, and most recently, selection as an AWS BVR (Business Value Realization) Motion Launch Partner.

Robots & Pencils is unabashedly all in on AWS, and Flynn’s strategy work is aimed squarely at helping enterprise leaders get more from their AI investments and more from their investments in AWS.

“Live in weeks vs. quarters change the conversation,” Flynn said. “When a client sees something real in production that fast, the question stops being whether AI works and starts being how fast we can do it again. That is the moment I want to create, over and over.”

Ready to see what applied AI like inside your organization? Request an AI briefing with Robots & Pencils and find out what changes first.

The post Robots & Pencils Enriches Studio for Generative and Agentic AI with Addition of Brendan Flynn as SVP, Strategy  appeared first on Robots & Pencils.

Part 1 of 2: The why. Part 2 walks through the operating model itself. 

Almost every product tool I open lately is reaching for the same idea: put an AI copilot on top of the backlog. Jira has Rovo. ServiceNow ships agile story generation that claims to cut epic breakdown by around 80%. Linear, Azure DevOps, and the rest are adding a button that turns a fuzzy feature into a tidy stack of tickets. 

That is genuinely useful. I do not want to wave it away. 

The version I run into most these days is subtler. Plenty of teams now connect Jira to their AI assistant as an MCP connector (in Claude’s Cowork, for instance) so the assistant can read and update tickets for them. For a solo PM, that can feel like a small miracle. The assistant can summarize the board, draft updates, and move work along without the usual tab-hopping. 

But the shape of the system has not really changed. 

The tracker is still a second system the AI has to reach into over an API, one round trip at a time. Someone still has to keep that system in sync with the work. The integration layer did not disappear. It just got automated. 

Which leaves a more interesting question sitting in the open: 

If AI can generate the work, and read the work, do we still need the tracker to be the home of the work? 

At Robots & Pencils we build AI products for enterprises, which means we have felt the gap between how these systems get built and how we were managing the building. On a recent enterprise pilot, I started an experiment: run delivery without Jira as the source of truth. 

Generate the epics and stories from a skills pipeline. Let the whole team (PM, designer, engineer) work out of a Git repo where stories, sprints, and decisions are versioned markdown files. 

The goal was not a tidier way for me, the PM, to manage a project from my own AI session. That would have been a nicer cockpit, not a better operating model. 

The goal was to put the whole team on one source: the engineer in their editor, the designer in theirs, no connector in the middle, and nobody waiting on me to sync a board. 

It worked well enough that it is quietly becoming how we deliver AI-native work. 

This piece is the why. The next one is the how. 

The tell: who’s doing the integrating 

Jira makes the human the integration layer. 

The work is there, but it is scattered into pieces: the ticket, the related tickets, the research doc, the decision from three weeks ago, the acceptance criteria that changed in grooming, the Slack thread nobody quite remembers. 

Before anyone can move, someone has to stitch the picture together. 

That was a reasonable deal when the only thing that could read a ticket was a human. 

It’s a worse deal now. 

The assistant doing more of the building can read the surrounding context, if the context is somewhere it can actually reach. When a story is a markdown file in the repo, the assistant can take in the story, the technical spec, the grooming notes, and the code itself in one pass, because they’re all just files in the same working surface. 

Nobody has to describe the map. The assistant can follow the links. 

Wiring the tracker into an AI assistant over MCP is still progress. It hands some of that assembly work to the assistant. But it also preserves the old shape: second system, API round trips, and a project record that has to be kept aligned with the place where the work is actually happening. 

Repo-native delivery asks for a cleaner cut. 

Move the artifacts to where the work and the AI already live, and the integration layer stops being a person. 

What about Linear? 

Linear is excellent. It is what a better tracker looks like: clean, fast, developer-friendly, with strong GitHub integration and improving AI. Plenty of teams, including parts of ours in the past, moved happily from Jira to Linear. 

But even Linear is still a tracker. The work artifacts live in Linear’s database. The AI reaches in through integrations. Engineers still cross from the issue tracker to their editor and back. The fundamental shape, a separate coordination system sitting next to the execution environment, stays intact. 

Repo-native delivery goes one step further. It asks whether we still need that separate system at all, once context is cheap and an assistant can read across stories, specs, decisions, and code in one pass. We are not trying to replace Jira or Linear with a nicer interface. We are moving the source of truth into the environment where the work, and the agents, already live. 

What changes when the repo is the truth 

The benefit I expected was speed. 

The benefit that actually mattered was quieter seams between people. 

The engineer felt it first. Their coding agent could open a story with the real acceptance criteria in context, not a paraphrase pasted into a chat window. That meant the agent built toward the definition of done instead of drifting toward a plausible interpretation of it. 

Even better, the criteria became something the agent could check its own work against. The story was not just an instruction. It was the contract. 

And the engineer never left the editor to find any of it. The story, the plan, and the reasoning sat beside the code, not behind another login. 

Handoffs changed too. 

A designer could drop an artifact and a one-line note into the story folder, and the next person’s editor would read it when they opened the story. No tagging. No “did you see my message?” No quiet dependency on someone remembering to move context from one place to another. 

The same source also starts feeding many views. One status.md file can become the morning brief, the sprint risk log, the retro, and the stakeholder report. 

You author once and query many ways. 

That is where the repo has an advantage over the tracker. A tracker’s data mostly feeds the tracker’s own views. Repo data feeds anything a team can write a prompt for. 

The query I lean on most isn’t a fancy one. Each morning, instead of asking the team what they did yesterday, I ask the repo. 

Jira is a coordination layer. The repo is where execution happens. 

The same assistant helping implement a story can also summarize sprint risk, trace dependencies, draft stakeholder updates, and explain why a decision was made. 

When the same system can participate in both coordination and execution, the boundary between the two starts to look less inevitable. 

That shift shows up in small ways at first. 

The prompts we run do not stay prompts. A query used a few times becomes a skill. The skills, plus the repo structure and conventions, become a scaffold. And that scaffold is portable. 

The next engagement does not begin with an empty Jira project. It begins from a template that already knows how stories, sprints, decisions, prompts, and ceremonies fit together. The operational cost of standing up a new project drops because the operating layer already exists. 

That is what turns a workflow into a capability. 

It also reframes the PM’s job. 

When a skill can draft the stories, the PM’s edge moves upstream: framing the right epics, encoding judgment into the pipeline, and curating the context the whole team’s tools draw from. 

The craft shifts from producing the backlog to designing the system that produces it. 

The judgment was always there. Now it has somewhere explicit to live. 

Maybe the bigger question is not how we teach our trackers to work with AI. 

Maybe it is whether the work still needs a separate coordination system at all. 

Was the tracker ever the right home for the work in the first place? 

Ours moved into the repo, and it has not asked to move back. 

Stay tuned for Part 2, where Nilesh walks through the operating model: the two-repo pattern, the story directory, the sprint-as-narrative, and how every ceremony ends in a commit, so you can copy it, not just nod at it. 

The post Pt. 1: Repo-Native Delivery – Why We Ditched Jira for AI Work   appeared first on Robots & Pencils.

Robots & Pencils, an applied AI engineering partner known for high-velocity delivery and measurable business outcomes, today announced Jansen Meyers as Senior Vice President of Operations. Meyers joins the executive leadership team to scale delivery capacity, sharpen operational execution, and lay the operational foundation for the company’s next phase of growth.

Meyers brings more than 25 years working with organizations to drive scalability. Most recently, he spent 16 years at Centric Consulting as a partner concentrated on operations, technology and transformation leadership, where he helped enterprise clients and Centric modernize operations and execute company-wide transformations.

“We are focused on client outcomes and accelerating everything we do for our clients. Scaling our operations requires the same emphasis,” said Nathan Carmon, Chief Operating Officer of Robots & Pencils. “Jansen simplifies the complex and designs systems that let exceptional people maintain their focus on outcomes and acceleration, not internal work and systems.”

In his new role, Meyers will oversee internal operations, expand delivery capacity, and engineer the operational backbone behind every Robots & Pencils engagement, the systems that let Velocity Pods take generative and agentic AI live in weeks, not quarters. His leadership will help the company scale with the same agility, craft, and client focus that have defined the company since it was founded in 2009.

“Complexity is easy; efficiency is deliberate. My focus is eliminating drag, so the business moves as fast as its ideas,” said Meyers. “The people here already move like they have done this before. My job is to make sure the processes and systems never get in their way.”

Meyers joins Robots & Pencils as more organizations turn to the company to move generative and agentic AI from pilot to live. His appointment reflects continued investment in the leadership and operational depth behind Robots & Pencils’ AWS Advanced Tier Services Partner and AWS Pattern Partner status, and its record of measurable business outcomes across 175+ clients worldwide.

Interested in a career with Robots & Pencils? View our open positions.

The post Robots & Pencils Appoints Jansen Meyers Senior Vice President of Operations appeared first on Robots & Pencils.

This is the first of many stories we’ll tell about what happened here. Before we get to the pods, the points, and the people who pulled all-nighters, it’s worth answering the plain questions. Who did this, what actually happened, when, where, and why any of it mattered. Three phases carry that story: Spark lit the competition; Ignite brought it together in Cleveland, and Surge is the follow-through happening right now. 

What is RoboCon? 

RoboCon 26 is Robots & Pencils’ internal AI competition, built to transform how we work with our clients and each other. Our CEO Len Pagon kicked things off on June 12th with 56 leaders from across the company. 

“Most companies are still figuring out how to start. We are already doing it. We design the architecture that takes AI from a demo into the actual work of the business. That is harder than it sounds. Most cannot. We can and we do.” 

That gap, between demoing AI and actually running a business on it, is the whole reason RoboCon exists. Pagon had the data to prove it, a six-level AI proficiency ladder gleaned from usage and a proprietary assessment tool. It showed exactly how and where the company could accelerate and codify its generative and agentic AI capabilities across every single current and future employee. “The pace is fast because the window is now,” Pagon told the room, “not next quarter, not another planning cycle.” 

So he picked roughly a quarter of the company, the leaders who could carry the change back to their teams, split them into seven pods of about eight people each, and told them to compete. 

Spark: The Competition  

Spark ran six weekly challenges, and each one moved the pods a step further down the same road: start by setting a shared culture baseline in the newly written Pencil Way, then build something real with the tools, then improve it, then apply it to actual client work. Partway through the six weeks, the competition stopped belonging only to the seven pods. Spark opened up to the whole company, a cohort of allies, and the job was to mentor them through the similar tasks the pod had already worked through. Their learning and their work counted toward their pod’s score. One rule sat above all the scoring: “Client work comes first,” Pagon told everyone. “That if you ever have to choose between working for our client, or you got to do this challenge, the client comes first…always!” 

Ignite: Three Days in Cleveland 

Six weeks of remote sprinting funneled into three days in person, July 21st through 23rd. Jeff Kirk, EVP of Applied AI, set the tone once everyone was on site.  

“It’s time to get into ultra-learning mode,” he said. “To work backwards from our most important customer outcomes and continue to incorporate AI agents into our professional services motion.” He pushed the room toward something harder than a workshop agenda. “Use radical candor with your colleagues and solve some of the most critical challenges that we’re facing on a day-to-day basis as we innovate on behalf of our clients – we transfer our know-how to our clients as a result of Robots & Pencils working with them.” 

The three days played out exactly that tension between celebration and work. A championship reception opened things on July 21st, in Pagon’s own backyard. The next two days filled Key Tower’s meeting rooms with keynotes, breakout sessions, and afternoon hackathons, where seven pods turned six weeks of practice into something they could stand behind. Stay tuned for individual and pod stories. 

Surge: Bringing it Home 

It would be easy to read Ignite as the climax of the story. It was the culmination of Spark, no question. But Pagon frames it as the first 2 phases as preparation for the most important phase, not an ending. Ignite closed with a personal commitment from every participant: one shift they were making, a few ideas for their team, and a 30, 60, and 90-day plan to make it stick. Read a handful of them back-to-back and the range says more about Surge than any recap could.  

“RoboCon reinforced something for me,” said Kristy Andersen, senior delivery manager. “Individual AI fluency is the floor. Organizational AI capability is where things accelerate and impact compounds.” 

Pagon called RoboCon an investment in our people and the company, the kind that doesn’t pay off in three days. It pays off in what people do with what they learned once the reception ends and the ballgame’s over. That work is Surge. It’s already underway, and it’s the story we’ll keep telling from here. 

The post RoboCon 26: How Robots & Pencils Turned a Spark into a Surge  appeared first on Robots & Pencils.

Now it does. Last week (July 15), ReFiBuy and Digital Commerce 360 published their inaugural AI Commerce Rankings, a quarterly benchmark that scores the Top 1000 retailers 0 to 100 on how ready their product catalogs are for AI shopping agents to read, interpret, and recommend. It is the first hard measurement of Learn phase readiness at scale, and it confirms both halves of the no-regret argument at once. The opportunity is real. Almost nobody has captured it yet.

The Diagnosis: 42 Out of 100

Cognizant’s Comfort Quotient research, which I cited last time, put Learn phase comfort at 47, the highest of the three purchase phases, with Early Adopters and Accelerators scoring 58 and 50 respectively. Consumers are ready. The AI Commerce Rankings show what retailers have built to meet them there. The average score across all 1,000 retailers is 42, with a median of 44.1. Only 20 retailers score above 60. The highest score recorded, out of 1,000 of the largest retailers in the country, is 72.

Scale does not buy readiness. Online Labels ranks #1 on AI readiness with a score of 71.8 and sits at #814 by online sales. Everlane ranks #4 in readiness at #264 by sales. Several of the retailers with the largest online sales volumes in the country rank in the bottom half of the readiness index. The retailers most exposed to AI-driven discovery traffic today are, in some cases, the ones AI agents can least see. Deloitte’s Stephan Ritter put a finer point on it recently on the Retailgentic podcast: most products will never be seen by AI, because most catalogs weren’t built for AI to see them.

There’s a concentration risk layered on top. ChatGPT accounts for more than 80% of AI-referred traffic to retail sites right now. Adobe Analytics clocked 693% year-over-year growth in generative AI traffic during Holiday 2025, then 393% year-over-year growth in AI-source traffic in Q1 2026. That traffic is real, it is growing fast, and most of it is flowing through a single door.

What to Build in the Next 90 Days

The Learn phase playbook is not a strategy exercise. The AI Commerce Rankings methodology is, in effect, a build spec. It scores exactly what an AI shopping agent experiences when it hits your catalog.

Bot friendliness first. Can ChatGPT, Gemini, and Alexa for Shopping actually reach, read, and transact against your product data, including support for emerging agentic commerce protocols like UCP and ACP? Only 26% of retailers in the index have verified UCP status. That’s the single highest-leverage fix available in the next 90 days for most retail IT teams, and it’s an engineering problem, not a marketing one.

Second, build for more than one engine. A single-engine dependency on ChatGPT is a single point of failure the moment a competing model changes its retrieval behavior or a new entrant like Perplexity gains share. Diversity of AI sources is one of the four signals the rankings measure for a reason.

Third, treat product content as multimodal infrastructure, not marketing copy. Cognizant’s research is direct about this: businesses will need multimodal approaches to conveying product and service information as AI-powered discovery becomes the default research path. That means structured attributes, image and video content agents can parse, and the metadata layer that turns a SKU into something an agent can reason about.

Fourth, know where your category actually sits. The rankings break out readiness by category, and the spread is wide. Office Supplies leads at 47.4. Food & Beverage trails at 37.2, the lowest score and the lowest AI traffic penetration in the index, in a category that touches nearly every consumer every week. One top-10 retailer by online sales in that category ranks below #550 on AI readiness. For CPG and grocery brands, that gap isn’t a warning. It’s the opening.

The Infrastructure Argument Hasn’t Changed

None of this ships from a slide deck and a vendor demo. It requires the same technical foundation I described in the first piece: unified product data, a semantic layer that lets an agent reason across your catalog instead of guessing, and guardrails that keep that reasoning inside the lines. AWS makes the case plainly in its own executive guidance on agentic AI. Organizations that have already operationalized generative AI with production-grade rigor, on a foundation like Amazon Bedrock, are the ones positioned to turn isolated catalog pilots into governed, reusable capability instead of another one-off integration.

That’s applied engineering work, not a repositioning exercise. It’s also the same discipline we walked through recently in how a marketing function runs on Amazon Quick with generative and agentic AI: real data, real guardrails, real production deployment. That’s what separates a working system from a slide.

90 Days, One Scorecard

The October constraints haven’t moved. The pending holiday code-freezes still lock down the production environments for most retailers and consumer brands ahead of peak season, and Amazon’s Fall Prime Event still opens the early holiday promotional calendar in early/mid-October (exact dates still pending, which makes preparation for brands even more challenging). What’s changed is that there’s now a public, quarterly, refreshed scorecard measuring exactly which retailers used this window and which ones didn’t.

An average score of 42 means the race is still open. Twenty retailers have separated from the pack. The other 980 have less than 90 days to decide which side of that line they land on before the next edition publishes.

Almost every retail and consumer goods brand I speak with agrees the opportunity is real. The AI Commerce Rankings just gave everyone a number to measure it against. If your team wants to know where your catalog stands and what to fix first, I’d like to be part of that conversation. Reach out on LinkedIn or directly.

Next up: the Use phase, and what “taking care of itself” actually means in production.

Let’s build what’s next in retail. Request an AI Briefing.

Saul Delage is SVP Client Partner at Robots & Pencils, focused on the Retail and Consumer Goods vertical. Robots & Pencils is an applied AI engineering partner, all in on AWS. Connect with Saul.

The post NO REGRET, Part Two: The LEARN Phase Agentic AI Playbook for Holiday 2026 appeared first on Robots & Pencils.

I hear this almost every week. Someone tells me they’ve applied to hundreds of jobs, heard nothing back, and blamed the ATS. Maybe you’ve said something similar yourself. My resume never reached a human. The AI rejected me. If only I had the right keywords. As Head of Talent at Robots & Pencils, I read resumes for a living, and here’s what might surprise you. Most resumes aren’t rejected because of AI. They’re rejected because they don’t tell a compelling story.

Here’s the short version. AI isn’t deciding who gets hired. People are. Yes, we use applicant tracking systems. Yes, technology helps me process thousands of applications. But I still open the file. I still read the first few lines. I still make the call. If your resume isn’t landing interviews, the fix isn’t a new set of keywords. It’s a better story.

The 75 Percent Myth Has No Study Behind It

I’ve spoken to candidates who have applied to 500 jobs, even 1,000. Then I ask one question. How many interviews have you gotten? Usually the answer is somewhere between zero and five. That tells me the problem isn’t the ATS. The problem is strategy.

The claim that three out of four resumes get auto-rejected before a human ever sees them shows up everywhere, from career coaches to LinkedIn posts. It traces back to a 2012 marketing claim from a resume-optimization company that went out of business the following year, not a peer-reviewed study or an industry survey. I use applicant tracking systems every day, and what they actually do is organize and rank applications. They don’t run a silent purge on formatting or keyword scores alone.

That distinction matters more than it sounds. If you believe your resume vanished into a black box, you’ll keep tweaking keywords. If you understand that a person like me is going to open that file, you’ll start writing for that person.

Your Resume Is a Marketing Document, Not a Biography

Here’s something I wish every job seeker understood. Your resume is not your biography. It’s not supposed to document every job you’ve ever had. Think about Apple. When Apple launches a new product, they don’t spend an hour talking about every device they’ve ever built. They focus on what matters to today’s customer. Your resume should do exactly the same thing. Every line should answer the same question. Why should I interview you for this role? If something doesn’t help answer that, it probably doesn’t belong there.

One mistake I see constantly is candidates trying to include everything. Every internship, every volunteer project, every certification they’ve ever earned. More information doesn’t make your resume stronger. Relevant information does. I’d rather review one focused page than three pages filled with information that has nothing to do with the role you’re applying for.

What Recruiters Scan for in the First Few Seconds

I don’t spend minutes carefully reading every resume, at least not at first. I’m scanning for clarity. An eye-tracking study conducted for TheLadders found recruiters spend roughly six to eight seconds on that initial scan, and more recent research puts the number closer to eleven. Either way, I have seconds, not minutes, to find three things.

1. Can I immediately understand who you are?
2. Can I see what value you bring?
3. Can I quickly connect your experience to the position I’m hiring for?

If I have to work too hard to figure that out, you’ve already made the process harder for both of us. When you’re competing with hundreds of other applicants, clarity becomes your biggest advantage.

Where AI Actually Belongs in Your Job Search

Can ChatGPT help you write your resume? Absolutely. Should it write your entire resume? Absolutely not. AI is an incredible assistant. It can help identify important skills, rewrite bullet points, improve grammar, and organize your ideas. But it shouldn’t replace your own voice. The more resumes I read, the easier it becomes to recognize the ones written almost entirely by AI, not because they’re bad, but because they all sound the same. Everyone is using AI to try to stand out, and they’re accidentally making themselves sound identical. Ironically, the candidates who actually stand out are the ones who use AI as an editor, not as the author. Your story still has to sound like you.

Your Resume’s Real Job

If there’s one thing I want you to remember, it’s this. Your resume doesn’t get you hired. It gets you a conversation. That’s its job. So stop trying to outsmart AI. Stop trying to game the ATS. Instead, write a resume that makes it easy for another human being to understand who you are and why you’re the right person for the role. No matter how much the tools change, one thing hasn’t changed. People still hire people.

I go deeper on all of this in the full episode above, including the exact professional summary line I want every job seeker to delete immediately.

Watch and then view our open positions and connect with me on LinkedIn.  

Frequently Asked Questions

Does AI reject resumes before a recruiter sees them?

No. I use applicant tracking systems to rank and organize applications, not to reject them silently. I’m the one making the hiring decision, not the algorithm.

Why do I keep getting rejected after applying to hundreds of jobs?

Usually it’s strategy, not the ATS. I’ve talked to candidates who applied to 1,000 jobs and got five interviews. Applying broadly without tailoring your resume to each role’s actual requirements produces a high application count and a low interview count.

Should I use ChatGPT to write my resume?

Use it to edit, not to author. I can tell almost immediately when a resume was written entirely by AI, and not because it’s bad. It’s because it sounds like everyone else’s. Let AI tighten your bullet points. Don’t let it replace your voice.

What should a professional summary actually say?

Skip “seeking an opportunity to utilize my skills.” I already know your objective is the job. Use the summary to tell me who you are, what problems you solve, and what you’re known for.

The post The Robots & Pencils’ Talent Perspective: What Recruiters Look for in a Resume  appeared first on Robots & Pencils.

Twenty years ago a parent told their kid not to major in art. Ten years ago they said it about English. Five years ago the script tightened to anything that didn’t end in -ology or -engineering. STEM was the moat. STEM was the parachute. STEM was the answer to every dinner-table question your kid was too polite to ask out loud. 

You know how this ends. 

I’ve been watching it end in slow motion for eighteen months. The CS grads I talk to are anxious in a way the comp lit grads aren’t. Not because the comp lit grads have it figured out. They don’t. Nobody does. But they were never promised the floor wouldn’t move. The CS grads were told it was bedrock. Then somebody started writing code at the speed of thought, and the bedrock turned out to be a Jenga tower with a six-week release cycle. 

The irony is thick enough to spread on toast. We trained a generation to speak the language of machines. The machines turned around and learned the language of people. If a screenwriter pitched that arc, the room would tell them to dial it back. 

The company whose name explained everything

I want to tell you a story about a company I ran, because I think it explains what’s actually happening, and what’s coming for the people who were paying attention. 

In 2014, I brought a Canadian agency called Robots & Pencils down to the States. I was CEO of the US operation. We grew thirty-four hundred percent in eighteen months, finished the year as the 35th fastest-growing tech company in the country, and most of the press I gave at the time was about the numbers. The numbers weren’t the story. The name was. 

Robots & Pencils. The name was lifted, more or less, from C.P. Snow’s 1959 lecture on the Two Cultures. Snow’s argument that the sciences and the humanities had drifted into separate languages that could no longer talk to each other, and that the gap between them was the central problem of modern life. He wrote it about Cambridge dining halls. We built it as an agency. 

The robots were the developers. People who could build the thing. The pencils were the designers. People who could see the thing before it existed. Two trades, two trainings, two languages, two halves of any product worth shipping. We had robots. We had pencils. We were good at both. 

And the founder, who was (ironically, beautifully, to his own ongoing amusement) robot #1, was the most insistent voice in the building that good products are always visioned pencils first. You can’t bolt art on at the end. Try it and the seams show forever. The most technical man in the room kept telling the room to start with the drawing. 

Two people who were already the bridge

He didn’t found the company alone. His wife was the pencil to his robot, a brilliant artist, an interior designer with the eye that finds the wrong wall and tells you why, an accountant sharp enough to serve as the company’s CFO. He was the technical man with a love of art he couldn’t fake. She was the artist with the operational backbone most companies wish they could hire. Each of them stretched toward the center. Each of them was already half ampersand before the company had a name. 

That’s why it worked. The two people at the top of the org chart were the bridge they were asking the rest of us to build. You felt it the second you walked in. It pulled in robots who suspected they were also a little bit pencil, pencils who knew they were also a little bit robot, and the people who’d never picked a side at all. The brand wasn’t a logo. The brand was the marriage. 

But the ampersand was the entire reason the company existed. 

The rarest person in any room

You know who I mean. The dev who notices the kerning. The designer who reads the API docs because she actually wants to know what’s possible. The one who drops #picky into a Slack design review without apologizing for it, because they know the small thing is the whole thing. The person who can sit between two rooms that don’t speak each other’s language and translate. Not the words. The intent. They were rare. We’d interview a hundred people and find one. They commanded a premium because the value of a translator scales with the distance between the parties, and the distance between an engineer and a designer in most companies is bigger than the distance between Calgary and the moon. 

The ampersand people weren’t better at either trade. They were the only ones in the room who saw both trades as the same problem from different chairs. They were almost mythological. We named the company after them. 

What AI actually does for the bridge person

Here is what I didn’t see coming, even though I should have. AI doesn’t pick a side. It never had to. It doesn’t just close the gap between the dev and the designer. It closes the gap inside the bridge person, the small and humiliating gap between what they could always see and what they could actually produce. The designer who knew exactly how the API should work but couldn’t write the call. She can write the call now. The dev who saw the right pixel grid but couldn’t move pixels. He can move pixels now. The ampersand always saw both ends of the bridge. Now they can walk it. If Jobs called the computer a bicycle for the mind, AI just strapped rocket engines to the sides. 

You want proof? A 23-year-old amateur mathematician named Liam Price, no PhD, no faculty appointment, no research lab, used ChatGPT to solve an open problem that had been sitting on the shelf for sixty years. Erdős Problem #1196, from primitive set theory. Sixty years. The kind of problem that gets named after the person who posed it because nobody alive could finish it. Price finished it. And then Terence Tao, the Fields Medalist, the person most mathematicians would rank as the best living mind in the discipline, verified the proof and co-authored the resulting paper. A 23-year-old with curiosity and a chatbot sat down at the same table as the greatest mathematician of his generation. Not because AI solved the problem for him. Because AI let him hold the conversation long enough to solve it himself. That’s the ampersand. That’s curiosity with tooling that doesn’t punish you for not having the right letters after your name. 

The bridge people are about to multiply. Not because the trait gets more common. That takes generations, and curiosity in two directions has never been on any roadmap I’ve ever seen. But the trait finally has tooling that doesn’t punish it for refusing to specialize. For thirty years we paid the ampersand person less because they weren’t “really” a developer or “really” a designer. We tolerated them because they made the meetings work. They are about to inherit the building. 

And the lesson runs wider than design and code, because the ampersand was never really about design and code. The ampersand is about being curious in two directions at once. It’s about being the kind of person who refuses to pick a side because they can’t stand to leave the other side ignorant. That’s not a job description. That’s a humanities education. 

Read carefully. Argue clearly. Hold a contradiction without flinching. Care about the answer even when nobody’s grading you. Do the hard thing quietly. Take care of the person next to you before you take care of yourself. Carry two trades at once and a third in reserve. Say what you mean and mean what you say. It doesn’t sound like much. It also doesn’t fit in a job code, which is why we’ve been quietly defunding it for forty years. It didn’t return on a single-year horizon, and the only people who tried to defend it sounded like they were defending themselves. 

I’ll tell you where I learned all of it, because it wasn’t school. It was my parents and the people they surrounded us with. A father who came up through the Green Berets and ran a house on the principle that you say what you mean and you mean what you say. A mother and a community who treated curiosity as a chore you didn’t get to skip. Nobody handed me a syllabus on holding a contradiction. I watched the adults around me do it at the dinner table. I graduated into the internet in 1985 and was running an ISP a decade later. That sharpened the tools. The tools came from home. 

That’s the part the system can’t replicate and won’t admit. The ampersand is mostly raised, not taught. The people who saw this moment coming were mostly the ones we wouldn’t fund. And the ones who can step into it now were mostly raised by people who didn’t need a funding line to know it mattered. 

The skill with a 25-year half-life

Framework knowledge has a half-life of about two and a half years. That’s IBM’s number for specialist technical skills, and it tracks with what the National Academy of Engineering has been saying for twenty years. By contrast, the half-life of a humanities education runs closer to twenty-five. The argument structure Aristotle taught is still the argument structure that wins. The people who can build and fix the machine will be needed for as long as there’s a machine. The play isn’t picking a side. It’s picking one discipline that ages well and one that compounds fast, and refusing to let either atrophy. 

Walk into a room where the pencils and the robots are really cooking and you can feel it through the floor. The designers sketch faster than the engineers can build. The engineers build faster than the designers can sketch. The ampersand is at the whiteboard turning the sketch into a system and the system into a sketch, and everybody in the room is operating one cognitive notch above where they could operate alone. That hum used to be the rarest sound in tech. We’re about to hear it everywhere. In classrooms, in clinics, in non-profits, in offices that have been quietly dying for a decade because the bridge person never showed up. 

Don’t mistake this for triumphalism. The reversal doesn’t make the humanities grad rich and the CS grad poor. It rearranges who has leverage, which is a different and harder problem. The CS grads who pair their craft with the human stuff, curiosity in two directions, care for the person on the other side of the screen, are going to be fine. The humanities grads who learned to type sentences but never learned to sit with a real problem until it broke? They’re going to wash out the same as anyone. The credential never saved anybody. The skill underneath the credential is the only thing that matters now. 

What’s coming is a world where you need both hands. The robot hand and the pencil hand. The technical and the human. The thing the machine can amplify and the thing the machine cannot replace. People who learned only one are about to find themselves doing half a job. People who learned both, the ampersands, the bridge people, the curious-in-two-directions people, are about to find the assignment finally suits them. 

The career advice parents need to hear in 2026

I’ll say something with an edge, because I have watched too many parents push too many kids in the wrong direction and I am out of polite ways to put it. Stop telling your daughter to pick something “practical.” There is nothing practical about training her for a job description that will be rewritten before she graduates. Tell her to chase the thing she is actually curious about, and tell her to learn the tools that scale curiosity. That is the practical answer in 2026. Everything else is nostalgia for a stability that was always a marketing slogan. 

And tell her to find the room. Find a university, a community, a circle that’s already wired for curiosity in two directions. Humanities and sciences sitting at the same table, AI amplifying both, and credentials that still translate into workforce currency on the other side. Skills, capabilities, and a degree. Not one or the other. Humble plug, because it would be dishonest not to say it: that’s exactly what we’re building at Maryville. An achievement architecture for all of them. Robots, ampersands, and pencils. AI amplifying each one’s curiosity and capabilities. We’re about to show, not tell, what that means. Stay tuned. 

The ampersand was always the symbol of the company because it was the symbol of the work. Two trades, one person, one connector character holding the whole sentence together. It was never decoration. It was the structure. 

The robots are getting better. The pencils are getting better. But the people who can hold both, who can be technical without losing their humanity, who can be human without abandoning the craft, those people are about to inherit the moment. The ones we underpaid for thirty years because we couldn’t fit them in a column. 

Strive to be a little ampersand. Not just the robot. Not just the pencil. The character in the middle that connects them. 

That’s the job now. And it’s the abstract of every job that comes after it. 

The team Phil wrote about? That’s us. Robots & Pencils has been building at the intersection of technical and human since day one. Request an AI briefing and see what that means for your organization.

About Phil Komarny

Phil Komarny, an award-winning executive, national thought leader, and Chief Future/AI Officer at Maryville University. He led Robots & Pencils as CEO in 2014 and 2015. It’s clear he never stopped thinking about what our name meant. Read more articles by Phil.

Key Takeaways

1. The “ampersand person” is someone curious in two directions at once — technical and human — and they have always been the most valuable person in the room.
2. AI doesn’t replace the bridge person. It closes the gap between what they could always see and what they could actually produce.
3. Framework knowledge has a half-life of roughly two and a half years. The skills underneath a humanities education run closer to twenty-five.
4. For thirty years we underpaid the generalist because they didn’t fit a job code. That’s about to change.
5. The practical career move in 2026 is pairing one discipline that ages well with one that compounds fast — and refusing to let either atrophy.

FAQ

What is the ampersand person? Someone who refuses to specialize in just one direction. They can sit between two rooms that don’t speak each other’s language and translate not the words, but the intent.

Why is the Robots & Pencils name significant? The name draws on C.P. Snow’s 1959 Two Cultures lecture — the idea that the sciences and humanities had drifted into separate languages. Robots represented the builders. Pencils represented the visionaries. The ampersand between them was always the point.

How does AI change things for generalists? AI closes the gap between what bridge people could always see and what they could actually produce. The designer who knew how the API should work can now write the call. The developer who saw the right pixel grid can now move pixels.

Is a humanities education still worth it? According to Phil Komarny, yes — more than ever. The argument structure Aristotle taught still wins. The credential doesn’t save anyone, but the skill underneath it compounds in a way technical specialization alone no longer does.

What should parents tell their kids about careers in an AI world? Stop pushing “something practical” defined by yesterday’s job market. Tell them to chase what they’re genuinely curious about and learn the tools that scale curiosity. That is the practical answer in 2026.

The post The Ampersand: Generalists in the AI Era appeared first on Robots & Pencils.

July 1^st has already come and gone. For most retailers and consumer brands, that date signals the final 90-day window to deploy new capabilities for Holiday 2026. By mid-October, most retail IT environments will be in code freeze. Amazon’s Fall Prime Event (Prime Big Deal Days) will have already kicked off the early holiday promotional season.  

While some pundits are debating the definition of agentic and whether it will or will not have a transformative impact, I agree with retail expert Jason “Retail Geek” Goldberg that AI is already impacting consumer behavior, and the question for businesses is where to focus so the investment is a no-regret bet — the right place to build regardless of how fast consumer AI adoption accelerates, regardless of how the competitive landscape shifts, and with a realistic chance of being in production before October. 

The research makes that answer clear. 

Where Consumers Are Already Ready for AI in Retail

Cognizant’s research, conducted with Oxford Economics across more than 8,400 consumers, maps AI comfort across three phases of the shopping journey using a Comfort Quotient score: Learn (product discovery and research), Buy (the transaction), and Use (post-purchase engagement). 

The Learn and Use phases are where consumer AI comfort is meaningfully established today. These represent the majority of your shoppers’ journey, not edge cases. And the market data confirms it’s already happening at scale. At Citi’s Global Consumer and Retail Conference in March, Jason Goldberg and Scot Wingo reported that Target saw 40% month-over-month traffic growth attributed to AI discovery tools like ChatGPT and Gemini. Amazon’s AI assistant Alexa for Shopping (formerly Rufus) is now engaging 300 million users. These are current numbers. The Learn phase is where AI is already reshaping how consumers find products, right now. 

The Use phase tells a complementary story. Consumers respond positively to products and services that “take care of themselves” — post-purchase support, order tracking, repurchase reminders, personalized engagement after the sale. The Comfort Quotient rebounds to 39 in this phase, including among consumers who are otherwise skeptical of AI. That’s a meaningful signal for where agentic AI delivers value with low resistance. 

These two phases are the no-regret focus areas. The case for building here is non-debatable: consumer readiness is established, the use cases are proven, and the ROI accrues at every stage of adoption growth. 

Why the Holiday Season Makes These AI Comfort Phases the Right Bet 

Holiday 2026 amplifies exactly the dynamics where AI in the Learn and Use phases delivers the most value. 

Discovery is highest-stakes in Q4. Consumers are actively searching for gift ideas, comparing unfamiliar products, and making purchase decisions outside their normal categories. AI-powered search, personalized recommendations, and multimodal product content are at peak value when the shopper is motivated but undecided. Convenience, which Cognizant’s research confirms as the primary driver of AI adoption ahead of price, matters most when a consumer is under time pressure. That’s November and December. 

Post-purchase is highest-volume in Q4. Order tracking inquiries, gift returns, product questions, and repeat purchase decisions all spike in November and December. Agentic AI deployed in the Use phase handles that volume, reduces service load, and turns a high-friction season into a loyalty-building moment. The brands that get this right in Holiday 2026 build the customer relationships that pay forward into 2027. 

Both phases can be scoped, built, tested, and deployed within the 90-day window between July 1 and October. They are where consumer readiness is highest, return is fastest, and the path to production is most straightforward. 

The AI Infrastructure Layer That Makes Next Holiday Season Even Better 

There’s a second dimension to the no-regret case, running on a slightly longer timeline. 

Cognizant’s research maps a third wave of change arriving by 2030: agentic purchasing, where consumer AI agents interact directly with business AI agents to orchestrate the full shopping journey. Goldberg and Wingo noted at Citi’s Global Consumer & Retail Conference that Google and OpenAI are already establishing the protocols for this — structured data standards, API interoperability, and checkout orchestration that will determine which brands surface when consumer AI agents start driving discovery decisions. 

The infrastructure that enables agentic commerce is the same infrastructure that improves Learn phase performance today: structured product data, external-facing APIs, and the connective tissue that lets your catalog show up wherever consumers are searching, whether that’s a search engine, a voice assistant, or an AI agent acting on a consumer’s behalf. 

Building this foundation now is a no-regret bet precisely because it pays off at every stage. It improves holiday 2026 performance. It positions the brand for the agentic commerce era as adoption accelerates. It is additive regardless of pace. 

For a live example of what this looks like in practice, our team recently published a piece on running a full marketing function with generative and agentic AI: How to Run a Marketing Function with Generative and Agentic AI on Amazon Quick. 

90 Days 

July 1 is this week. 

October brings two converging constraints: IT freezes lock down most retail environments ahead of peak season, and Amazon’s Fall Prime Event kicks off the early holiday promotional calendar in mid-October. The brands that have agentic AI capabilities in production for Holiday 2026 are the ones starting the work in the next few weeks, not in September. 

Almost every retail and consumer goods brand I speak with shares the same conviction: the opportunity is clear, the consumer readiness data is unambiguous, and the timing is right. The constraint is activation. How to get AI from conviction to something in production in a compressed window. 

That’s the work we do at Robots & Pencils. If your team is ready to move in the next 90 days, I’d like to be part of the conversation. Reach out on LinkedIn or directly. 

Progress beats paralysis, and in retail, the calendar is the most unforgiving proof of that. 

Saul Delage is SVP Client Partner at Robots & Pencils, focused on the Retail and Consumer Goods vertical. Robots & Pencils is an applied AI engineering partner, all in on AWS. 

The post The NO REGRET Agentic AI Focus Area for all Retailers and Consumer Brands this Holiday Season  appeared first on Robots & Pencils.

The Setup: Rebuilding Marketing on Amazon Quick

Q: You run marketing for Robots & Pencils, and, as you know, we are all in on AWS and Generative AI. How has the company influenced the way you have built out the marketing team ?

Marketing for an applied AI engineering partner comes with a built-in hypocrisy risk. If we are out telling enterprise clients to put generative and agentic AI into production, my marketing function cannot be running on willpower and a content calendar in Google Sheets. I would be like a swimming instructor who refuses to get in the pool.

When I joined the team, the first question I asked was how we can design marketing operations the same way we architect solutions for enterprise clients: AI-first, designed around outcomes, scalable without a hiring spree. I also needed a system that could accommodate personal constraints, I have Multiple Sclerosis, and I needed something that would hold up across good days and harder ones.

Amazon Quick turned out to be the answer, and my whole marketing operation lives on the platform now. Custom Agents are my specialist teammates, Spaces hold the knowledge architecture, Quick Research runs the competitive and industry intel, and Flows automate the editorial calendar. I run all of it on my own, and the work still goes out the door before the coffee gets cold.

Q: Can you say more about what you mean by “agents being your teammates”?

Most marketing leaders I talk to right now are doing the work of three people while learning a brand-new tool stack on their lunch break. AI only sharpened that pressure, because now we are also supposed to be experts in a discipline that barely existed a year ago.

My first instinct was the same as everyone else’s: start hiring. And that is more or less what I did by building and executing a hiring plan, just not the kind with headcount attached.

I now have a team that runs 24×7 and never burns out. My AI marketing agents on Quick each fill a specific role: brand strategist, competitive analyst, content specialist, operations manager, industry research analyst, and so on. Each one built for a specific function, fed a curated diet of source material, and held to a tight set of rules, the same as any new hire I would bring onto a real team.

The Architecture: Amazon Quick for Marketing, Layer by Layer

Q: Walk me through how this is structured on Quick.

Custom Agents act as named teammates for specific functions, Spaces hold the curated source material each agent is bound to, Quick Research runs the competitive and industry scans, Flows automate the editorial calendar so the system is driving the cadence, and Chat handles real-time iteration when something has to go live fast.

For example, most marketers will recognize the time sink of brand policing. I ensure every piece of content created clears a four-filter test. The filters themselves are my secret, but I can tell you that before Quick, those filters were a bottleneck. Running them by hand slowed me down and skipping them produced off-brand content. Chasing every off-brand sentence, missed citation, and freelance claim before it lands in front of the world. The most useful thing Quick does is take that off my plate by building it in. My agents only pull from sources I designate, cite only from real documents, and their constrained knowledge prevents fabrication and hallucinations. Which means we publish better, highly relevant content at the speed this industry actually moves, and the brand-policing hours I used to spend on review cycles now go into strategy.

Staying Current

Q: Robots & Pencils operates in a category where the technology itself is changing the story. How do you manage messaging consistency inside that?

Every marketing leader I know is trying to stay on the AI messaging mechanical bull. Capabilities expand, market framing shifts, and messaging changes quarter to quarter, which hits everything we produce: the website, the decks, the sell sheets, the videos, the articles. Minor adjustments are constant just to stay current, and the only thing worse than making them is not making them.

With Quick, my source of truth lives in a Space. When positioning shifts, I update the source, everything downstream reflects it, and a change that used to take weeks across a team turns around much quicker.

The cognitive load of tracking changes across a team disappears too, which matters more than most marketing leaders admit, and matters even more for the ones whose capacity moves around.

The Outcomes

Q: What does this produce in measurable business outcomes?

The numbers for 2026 alone are specific. Our website is running nearly 1.5x above the industry’s top-performing quartile. In the first half of 2026, robotsandpencils.com grew traffic 40% while pulling in a 99% new user rate, meaning the brand is consistently reaching untapped audiences. Organic search hit a 59% engagement rate, which tells you the content is attracting people who are actively looking for enterprise AI solutions. High-value pages are holding 76–79% engagement rates. Most importantly, monthly traffic volume has grown consistently every month, which is evidence of compounding growth, not a one-time spike. And the leads? We have a more than steady stream pouring in.

Robots & Pencils’ LinkedIn page organic follower growth accelerated 1,417%, and monthly impressions saw a 437% jump in just five months. On active posting days, our engagement rate runs more than 4x the industry average and well above the platform-wide median of 5.2%.

The numbers say what they say: this strategy is punching well above its weight.

The Philosophy

Q: What would you tell other functional leaders considering this approach?

When leaders ask me about agentic AI for marketing, I tell them not to start with the tool. Figure out what you want the work to produce and the rules that keep it on track first, if you can do that, picking the tool is the easy part.

My time in education marketing taught me something simple: quality output follows a quality system. The great teachers I have known build the structure, assessment frameworks, feedback loops, and scaling mechanisms. Then they teach inside that structure at a level they could not sustain otherwise.

I think this works for any function, build the environment, and produce inside it. The expertise stays yours, and the AI gives it scale.

Q: You have been candid about this system filling operational gaps. Is any of that personal for you?

I cover upwards of seven industry verticals, a full editorial calendar, enterprise-grade thought leadership, advertising, and AWS-compliant positioning across every touchpoint. It’s a lot, and I have Multiple Sclerosis which is known for cognitive issues, slower processing, and difficulty with multitasking. This system handles the parallel execution. I bring the framework for what needs to get done and 25 years of expertise to govern how it gets done.

For anyone working with a disability, this is the kind of system that lets you keep showing up at full strength when your body has other plans.

Q: Last question. What’s next?

What’s next for me is the work I came into marketing for. Amazon Quick has taken the review cycles, the brand-policing hours, and the parallel execution off my plate. The strategy, the storytelling, and the conversations are where I want to spend my time now.

A new vertical means a new Space and a new specialist teammate, a new workflow gets a Flow or Automation, and when it comes to adding headcount, I have clearer definition of the skills needed, and they are those that only a human can perform.

Marketing is one function, and every team across Robots & Pencils works this way. Engineers and delivery leads operate with their own agentic teammates grounded in their own curated systems of record. Our client-facing teams do the same. We do not recommend this approach to enterprise clients and then sneak back to the old way once everyone has left the room. This is how Robots & Pencils actually works.

One more thing. Marketing leaders working with a disability, chronic illness, neurodivergence, or caregiving responsibilities will recognize the benefit Quick offers here. Anyone running a marketing function gains from it, and some of us notice it sooner. That is the part of agentic AI I am most invested in talking about.

Want to see how Amazon Quick can transform your enterprise workflows? Request an AI Briefing today.

FAQs

Source: Amazon Quick User Guide

What is Amazon Quick?

Amazon Quick is a comprehensive, generative AI-powered business intelligence platform that makes it easy to analyze data, create visualizations, automate workflows, and collaborate across your organization. The service combines traditional business intelligence capabilities with modern AI assistance, requiring no machine learning expertise to use. You can connect to diverse data sources, create interactive dashboards, build intelligent automations, and get immediate insights through natural language conversations with AI agents.

Quick includes five integrated capabilities that work together: Amazon Quick Sight for data visualization, Amazon Quick Flows for workflow automation, Amazon Quick Automate for process optimization, Amazon Quick Index for data discovery, and Amazon Quick Research for comprehensive analysis. The platform extends beyond traditional BI by bringing AI assistance directly into your existing tools through extensions for browsers, Slack, and Microsoft Office applications. You can also build and publish interactive web applications using apps in Amazon Quick.

What is a Space in Amazon Quick?

A space in Amazon Quick is a collection of data and Quick resources scoped for a particular team or domain. You can use spaces to aggregate and organize files, dashboards, topics, knowledge bases, and application actions into a unified and customizable knowledge center for your team. Spaces integrate seamlessly with Quick agents for contextual conversations and are designed to scale across personal, team, and cross-team use cases.

Spaces allow your team to get the most relevant results from conversational agents and other AI tools inside Quick by grounding the results with only data relevant for your task or domain. Multiple people on the team can contribute to the knowledge inside a space; this reduces data silos and streamlines information discovery. Spaces also serve as a data layer for apps in Amazon Quick applications.

What is Amazon Quick Sight?

Amazon Quick Sight is a comprehensive business intelligence service that enables you to transform raw data into meaningful insights through interactive visualizations, dashboards, and reports. Whether you’re connecting to databases, preparing datasets, creating analyses, or sharing dashboards with stakeholders, Amazon Quick Sight provides the tools you need to make data-driven decisions.

What is Amazon Quick Research?

Amazon Quick Research is a feature of Amazon Quick that enables you to conduct comprehensive research by analyzing multiple data sources and generating detailed reports. Quick Research uses artificial intelligence to help you gather, analyze, and synthesize information from various sources including web search, uploaded files, connected data spaces, knowledge bases, actions, and third-party data providers.

With Quick Research, you can define research objectives, select relevant data sources, and receive AI-generated research reports with proper citations and source tracing. This helps you make informed decisions based on comprehensive analysis of available information.

What is Amazon Quick Flows?

Amazon Quick Flows is a capability within Amazon Quick that lets any user create, customize, and share workflows that automate routine tasks. You can generate flows from conversations with chat agents, describe what you need in natural language, or build them manually using the visual editor — no technical skills required. Flows can also be published to an admin-managed library and shared with other Amazon Quick users in your organization.

Each flow is a sequence of steps that can gather user input, generate AI responses from your data or the web, take actions in connected applications, and apply logic to control how steps run.

What is Amazon Quick Automate?

Amazon Quick Automate is an AI-powered application that creates sophisticated automations using natural language or documentation. Amazon Quick Automate revolutionizes enterprise workflow by transforming complex processes into intelligent, adaptive automations.

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