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.

The post How to Run a Marketing Function with Generative and Agentic AI on Amazon Quick  appeared first on Robots & Pencils.

AWS releases generative and agentic AI services faster than most enterprises can deploy them, and that gap is widening every quarter they wait. In June, the Applied AI Engineering Partner will be on the ground at three AWS Summit events, ready to sit down with enterprise and public sector leaders who are done with pilots and ready to take generative and agentic AI live.

The AWS Summit series is where enterprise technology leaders gather to see what is possible on AWS. As an AWS Advanced Tier Services Partner and one of 11 inaugural AWS Pattern Partners selected from the AWS Partner Network, Robots & Pencils is the nimble, high-velocity alternative to traditional global systems integrators. Every engagement starts with the outcome the client needs and earns it with evidence. Velocity Pods, Robots & Pencils’ atomic delivery units, are small teams of senior practitioners that take AI solutions from concept to production on Amazon Bedrock and Amazon Bedrock AgentCore in weeks, compared to 6 to 12 months with a traditional systems integrator. 

“AWS has built the full-stack agentic AI infrastructure. The technology is ready. What we do is put it to work fast,” said Adrian Bird, VP of AWS Partnership at Robots & Pencils. “We work with your teams to build the business architecture that takes enterprise AI live before the next board meeting.”

“Enterprises don’t need another aircraft carrier full of consultants in the AI era,” said Jeff Kirk, EVP of Applied AI at Robots & Pencils. “They need a speedboat, a forward deployed team small enough to be surgical, senior enough to build for scale with, and fast enough to prove ROI before the next budget cycle.”

What the Robots & Pencils Builds on AWS 

Agentic AI with AWS is the core of the work. AI agents built on Amazon Bedrock and Amazon Bedrock AgentCore that work alongside human teams in live not in a sandbox. 

Cloud Modernization gets organizations off aging infrastructure and onto a serverless, AWS-native foundation built to scale, not just to run. 

Cloud and AI App Development is where engineers and creatives build in the same room, producing cloud-native applications with generative AI in the architecture from day one. 

For enterprise and public sector teams attending any of the three summits, the Robots & Pencils team is ready to talk. 

Schedule time with the Robots & Pencils team:  

• AWS Summit Los Angeles, June 10 

• AWS Summit New York City, June 17 

• AWS Summit Washington, D.C., June 30 – July 1 

The post Robots & Pencils Hits Three AWS Summits in June with One Message: It’s Time to Launch and Scale AI appeared first on Robots & Pencils.

EXPO XXI is a short ride from the center of Warsaw, and on a May morning you can feel the conference before you see it. The queue outside was long but moving fast. Hoodies, lanyards, laptop bags. The crowd skewed more senior than you might expect at a free regional event.  

Our plan was deliberate. Show up early, split the agenda, cover more ground in parallel, regroup over coffee, and most importantly, validate our approach.  

Robots & Pencils team members in attendance, from left to right: Stanislav Makar, Rostyslav Volskyi, and Bohdan Popovych. 

Agentic AI is the AWS Headline. 

The opening keynote made one thing clear. Agentic AI is the organizing thesis for everything AWS is building in 2026. 

Three names anchored the story. Kiro, the agentic IDE that got a fresh push at re:Invent 2025, featured prominently with its spec-driven development model, sequenced task generation, and agents that produce tests alongside code. Nova 2, the model powering more of the AWS AI surface, continues its region-by-region rollout. AWS Transform, their modernization platform for mainframe, VMware, and .NET workloads, framed as the agentic path into enterprise legacy systems. 

Real customer stories on stage. Real numbers. Real screenshots. The European Sovereign Cloud and the EMEA AI Hub got dedicated time, which landed well with the Warsaw audience. The framing was consistent throughout: the shift from AI tools you prompt to AI agents that reason, plan, and act is underway. The question for builders is how you instrument, evaluate, and trust what those agents do. 

That question got a very good answer in the next session. 

The Session That Landed: AgentCore Evaluations in Production 

Right timing matters at a conference, and the AgentCore deep-dive landed at exactly the right moment. AWS spent the spring pushing AgentCore Evaluations hard. It went GA on March 31, 2026, and the Warsaw session put it directly in front of European builders. 

The plain-language version of what it does: a managed service that continuously monitors agent quality against real production traces, not just test suites. You are shipping agents. You need to know they work. Handing someone a scorecard you hand-rolled for each project is not a sustainable answer. This is. 

The built-in evaluators cover what matters in production: 

• Correctness. Did the agent get the answer right? 

• Helpfulness. Was the response useful to the person asking? 

• Tool selection accuracy. Did the agent pick the right tool for the step? 

• Safety. Did anything in the output violate policy? 

• Goal success rate. Did the multi-step task complete? 

• Context relevance. Did the retrieved context match the question? 

On top of those you can configure custom evaluators. LLM-as-judge with your own prompt and model, or code-based evaluators running on Lambda. The same framework handles hallucination detection and JSON schema validation without forcing two different toolchains. 

The detail that made us lean forward: full OpenTelemetry compatibility. The evaluator scores flow into existing dashboards alongside session count, latency, token usage, and error rates. You can alert on agent quality the same way you alert on a CPU spike. 

For anyone building agents on behalf of enterprise customers, this solves the credibility problem. “How do you know it works in production” is no longer a hand-waving moment. 

The Best Conversation Happened at the Espresso Machine 

One of the more useful exchanges of the day started while waiting for coffee. 

AWS set up a cloud-ordered espresso bar on the expo floor. You scanned a QR code, placed your order in a small web app, and the espresso machine queued it. When the drink was ready, the screen showed your name. No line. No barista small talk. Beautifully on-brand for a cloud event, and genuinely better than the alternative. 

While we waited, a conversation started with a Senior Solutions Architect at AWS. It turned into one of the most useful exchanges of the day. The topic was whether Lambda is a credible runtime for agentic workflows. The honest answer is: it depends on whether you have state. 

An agent is not a request and a response. It is a long, branching workflow with LLM calls, tool invocations, and occasional human-in-the-loop steps. Lambda durable functions, which AWS shipped in late 2025 and has been shaping for agentic use cases since, address this directly. Each LLM call and each tool invocation becomes a checkpointed step inside a single Lambda. If execution times out mid-loop, the next invocation replays from the last checkpoint and skips completed steps. No Step Functions wiring. No custom state store. No DIY replay logic. The orchestration lives in the function code, in the language you already use. 

The Java SDK went GA in April 2026. Durable functions are now available in sixteen additional regions. 

The Best Hour of the Day: Knowledge Graphs 

Two talks on knowledge graphs stood out as the strongest technical content of the summit. The first was delivered by Dmytro Romantsov, Senior SRE at Miro, on their internal AI agent built over an organizational graph. The talk was technically dense and honest: he walked through what failed before the team settled on a graph-backed architecture, what the graph actually contains, how updates flow into it, and where the approach delivers measurably better results than the pre-graph baseline. 

After the session, we walked over to talk to him. Small-world moment: we share a first language, switched off English immediately, and the conversation opened up. The core thesis from both the talk and the follow-up conversation was consistent. Enterprise AI agents are only as good as the organizational knowledge they can reason over. A graph gives that knowledge structure, updateability, and query depth that flat retrieval cannot match. That is not a new idea, but watching it validated independently at Miro’s scale makes the argument more concrete. 

The second strong graph talk came from an SLB engineer in DEV207, on context graphs for explainable AI agents. The framing that stuck: the difference between a state clock and an event clock. Most pipelines today reflect the current state of a system. A context graph that also captures decision events can answer “why did this happen, and in what order.” That is the kind of explainability enterprise buyers are starting to require as agents move from pilot to live. 

Asking Honest Questions About AWS Transform 

The AWS Transform booth was busy. The team arrived with a direct question about IBM RPG support and walked through the answer methodically with a Solutions Architect for Migration and Modernization at AWS. 

The most telling moment was watching an AWS specialist type the same question into their own tool in front of us. The answer came back: yes, with limitations, followed by pages of caveats. Informative in its own way. 

The bottom line is that AWS Transform is production-grade for COBOL, Java-to-JavaScript migrations, VMware modernization, and mainframe workloads. RPG support is real but not ready for complex production use cases. We left with clarity on where the tool genuinely shines and where the right path is a combination of other tools and hand-rolled pipelines. That kind of honest answer is the second-best outcome at a conference. It tells you your reasoning was sound. 

The VMware migration angle, by contrast, is genuinely strong. Broadcom’s license changes are creating real urgency for customers running on VMware infrastructure. Worth flagging for relevant engagements. 

The Compute Thesis: AWS is Sizing Infrastructure for Self-Managed AI 

A theme ran underneath the agentic-AI headline all day: AWS is provisioning compute to match the shape of AI demand, and the demand right now for these kinds of workloads is high. 

Two sessions made the same point from opposite ends of the price spectrum. Comarch walked through a real migration from x86 to AWS Graviton-based instances, with meaningful cost reductions and measured performance gains. The honest part of their talk: Graviton is not a flag flip. If you have native code, JNI bindings, or JIT-tuned hotspots, you pay for the migration before you see the savings. 

On the other end of the spectrum: Meta’s agreement to deploy AWS Graviton processors at scale, starting with tens of millions of Graviton cores, announced ten days before the summit and explicitly framed around CPU-intensive agentic AI workloads — real-time reasoning, code generation, and multi-step task orchestration. 

For Robots & Pencils, this opens a third option alongside Bedrock and direct provider APIs. For clients with data-residency constraints, predictable high-volume workloads, or smaller open-weight models where managed-API margins make self-managed attractive, the playbook is now well-documented and accessible. Independent benchmarks on Llama 3.1 8B have Graviton4 delivering roughly 2x the tokens per dollar of comparable x86 options for that model class. 

A Practitioner’s Checklist for 2026 

The session that generated the most useful signal for client-facing conversations was DEV209, delivered by Tomasz Dudek, Data and AI Team Lead at Chaos Gears and an AWS Machine Learning Hero. The premise was simple: AI has been mainstream for over three years. He has watched hundreds of Amazon Bedrock projects pass through his hands. Most near-failures trace back to a small set of repeatable mistakes. 

The talk was the inverse of a vendor pitch. Here is exactly how teams stall before the first line of code. Here is what to do instead. He closed with 13 numbered tips for approaching AI projects in 2026. The final line: “Have evals, really.” 

It was good to hear a practitioner at that level land on the same conclusions we have been operating on. The teams doing this work at scale are converging on the same principles, and the list mapped closely to how we already approach agent quality on client engagements. Confirmation from that angle is worth having. 

The Parts That Were Just Fun 

Not everything at a summit is a session worth writing home about. But a few moments, in addition to the Serverlesspresso bar, which was cool enough to warrant a second mention, stood out for the right reasons. 

The AWS Drive Your Data Formula 1 simulator was exactly what it looked like: two Fanatec rigs, full wraparound LED screens, a Canada time-trial, and a results board you could compete on. The pitch underneath was real telemetry and lap analytics. The booth’s job was to draw a crowd, and it absolutely did. The team took turns. 

And the Ukrainian-speaking community was well-represented at this summit. Several familiar-sounding conversations happened in unexpected corners of the expo. That part mattered. 

What the Day Confirmed 

The most useful thing a conference can do is sharpen your picture of where the tools are today versus where they are heading. Warsaw 2026 did that well. 

Agentic AI is no longer a roadmap commitment from AWS. It is the organizing logic of everything they showed. Agent evaluation infrastructure is production-ready and instrumented the way mature engineering teams expect. The compute story has matured to a point where self-hosting is a genuine option for the right workloads, not just a theoretical one. Knowledge graphs as a foundation for enterprise AI agents are getting independent validation at scale. And the practitioners who have been doing this work longest are converging on the same principles around evaluation, quality gates, and shipping agents that are honest about what they know. 

None of that surprised us. All of it was good to see confirmed. 

Warsaw 2026 delivered real technical depth on agentic AI, agent evaluation, and knowledge graphs. The team went in with specific questions and came back with sharper answers, a few useful new contacts, and a strong argument for cloud-ordered coffee at the next internal engineering day. 

Robots & Pencils is an AWS Advanced Tier Services Partner and AWS Pattern Partner. Request an AI Briefing today. 

Written by Bohdan Popovych: Robots & Pencils Ukraine Engineering Manager, Rostyslav Volskyi: AWS Certified Solutions Architect and Amazon Web Services Developer, and Stanislav Makar: AWS Certified Solutions Architect – Professional.  

The post AWS Summit Warsaw 2026: What We Saw, Who We Met, and What It Confirmed  appeared first on Robots & Pencils.

The Problem We Brought In 

We brought a live use case from one of our enterprise customers, a regulated utility dealing with alarm overload, aging infrastructure they must migrate off by 2028, and the steady departure of the asset experts who know how all of it really works. 

What We Built (And What Surprised Me!) 

By the end of the day we had a working end-to-end agentic workflow that includes a dashboard pulling device telemetry into one view, an agent that triages incoming alerts and recommends what to do about them, and a knowledge base that captures the kind of expertise that usually walks out the door when someone retires. It’s nowhere near production, but it’s enough that we are ready to sit down with the customer next week and have a concrete concept discussion instead of a whiteboard one. That’s the part that surprised me most. 

We were also lucky enough to be recognized as one of the winning partners on the day, which was a nice bonus. 

A Few Thanks 

A few thanks are in order. Naresh Rajaram, Sr. Partner Solutions Architect at AWS, ran a genuinely well-organized event. Every detail was thought through. Neal Cauley’s framing of where Amazon Quick is heading was probably the most useful 30 minutes of the day for me, and it connected back to what Rima Olinger, World Wide Director Data & AI GTM – Amazon Quick, has been sharing publicly about how Amazon itself is using the product internally. Worth reading if you haven’t. Thanks also to the AWS team for inviting us and to the Quick specialists who sat at our table and helped us push further than we would have on our own. 

Looking forward to the next one. 

Robots & Pencils is an AWS Advanced Tier Services Partner and AWS Pattern Partner. Request an AI Briefing today.  

About the Author 

Adrian Bird is Vice President of AWS Partnership at Robots & Pencils, where he leads the company’s AWS Partner strategy and execution, expanding joint customer engagement, and strengthening alignment with AWS teams. Connect with Adrian.

The post We Took a Real Problem into the Amazon Quick Hackathon. It Delivered. appeared first on Robots & Pencils.

Robots & Pencils, an applied AI engineering partner known for high-velocity delivery and measurable business outcomes, published The Fault Line, a three-part series examining the organizational failures keeping energy AI trapped in perpetual pilot mode. 

Forty percent of utility control rooms will deploy AI-driven operators by 2027, according to Gartner. Yet fewer than seven percent of energy organizations have gone live with even one AI use case, according to IDC and AWS research. The gap between investment and execution continues to widen across the sector. 

The Fault Line argues the problem lives in three specific organizational breakdowns that repeatedly prevent AI from reaching production environments and generating operational learning at scale. Scott Young, EVP of Growth and Strategic Alliances at Robots & Pencils, wrote the series for the energy executive who has approved the budget, built the pilot, and is still waiting for AI to run. 

“Energy executives are moving faster through decisive action that turns AI investment into operational advantage,” said Young. “Every quarter spent in evaluation is a quarter of compounding operational learning moving somewhere else. That is the fault line. And it is solvable.” 

Three Articles. Three Failures. One Compounding Reality. 

Part 1 – Going Live with Energy AI Starts with One Decision. The applications energy executives are waiting on are already ready to deploy. They have been for years. The first article examines the one thing standing between investment and results, and it is not technology. 

Part 2 – Energy AI Operator Trust Is Earned by Design. When AI stalls in the control room, the default explanation is operator resistance. The second article argues that explanation is aimed at the wrong problem entirely and that the organizations making the most progress stopped trying to manage adoption and started doing something else. 

Part 3 – The Energy AI Architecture Decision That Outlasts Every Tool. Most energy organizations are not building AI. They are accumulating it. The third article names the difference between a collection of tools that cannot learn from each other and an architecture that compounds and explains why no one selling AI tools has a financial incentive to close that gap. 

Why This Matters Now 

Investment, urgency, and operational pressure are converging quickly across the industry. The DOE’s Genesis Mission mobilized $293 million to advance AI in grid operations. ERCOT launched a dedicated Enterprise Data and AI organization in January 2026. At the same time, many organizations are adding AI systems faster than they are building the operational foundations required to scale them effectively. 

The Fault Line identifies three areas where that gap consistently appears including executive decision velocity, operator-centered system design, and architectures capable of compounding intelligence across the enterprise. The series also addresses the regulatory and operational realities utility leaders face while advancing AI initiatives within NERC CIP environments. 

Each article stands on its own. Together, the series presents a clear argument for how energy organizations move from isolated pilots to operational AI systems that improve through live deployment. 

“The energy sector is entering a period where AI advantage compounds faster than most executives expect,” Young said. “The organizations deploying now will be operating systems shaped by thousands of hours of real-world learning while others are still refining pilots. The opportunity belongs to the organizations willing to move.” 

Read the Series 

The Fault Line is available now at robotsandpencils.com. Energy executives interested in accelerating AI deployment and operational readiness can request an AI Briefing. 

The post Every Energy AI Initiative Stalls in the Same Three Places. Robots & Pencils Names Them.  appeared first on Robots & Pencils.

Part 1: Make the Decision | Part 2: Fix the Design | Part 3: Build the Architecture 

The energy executives I talk to have already committed to generative and agentic AI. The budgets are approved. The strategic plans name it. What most have not committed to yet is treating that deployment as an operational decision rather than an ongoing evaluation. That distinction is the entire ballgame. 

Gartner projects that 40 percent of utility control rooms will deploy AI-driven operators by 2027. Nearly all energy CIOs plan to increase AI investment, at an average spending increase of 38 percent. The DOE’s Genesis Mission mobilized $293 million targeting AI’s role specifically in grid operations and reliability. The conditions for large-scale energy AI deployment have never been more aligned. 

An IDC and Amazon Web Services (AWS) study of more than 900 organizations found that fewer than 7 percent have reached full production with even one AI use case. 

The standard explanation is that energy is a uniquely complex operating environment. Legacy systems, fragmented data, strict regulation, and safety-critical infrastructure are real constraints. Energy leaders reach for them first when AI stalls. They are the setting, not the cause. 

The actual reason is less comfortable. The applications energy leaders want most are already ready to deploy. Most organizations are waiting for a technology problem to solve when the problem is organizational. 

The Energy AI Readiness Gap Nobody Is Naming 

According to the Federation of American Scientists’ assessment of the Department of Energy’s priority AI applications, nearly half are high-impact and ready to deploy today. Operations and reliability use cases score 3.6 out of 5.0 on deployment readiness, the highest category in the entire assessment. 

The most urgently needed applications are also the most architecturally mature. 

That creates a specific kind of organizational trap. When technology readiness runs ahead of organizational readiness, leaders rarely recognize the gap for what it is. An initiative stalls, and the natural assumption is that something technical still needs improvement. The model needs more training data. The data environment needs more work. The pilot needs another quarter before it can prove itself. 

What actually needs improvement is what we call the decision architecture gap. Most energy organizations have not built the organizational capacity to evaluate, commit to, and scale AI applications based on evidence of operational value rather than proof of technical completion. 

What the Data Is Already Telling You 

Energy companies already have the data. They are waiting on the decision to act on it. 

NREL’s Open Energy Data Initiative hosts 2.6 petabytes of data across more than 2,000 datasets from 227 providers. Utilities already hold enormous volumes of AMI telemetry, SCADA signals, outage history, maintenance logs, and weather correlations. The question is not whether useful data exists. The question is whether it is being treated as institutional memory or as archived history. 

These are not the same thing. Archived history answers questions when asked. Institutional memory learns continuously, surfacing patterns, updating predictions, and sharpening with every new cycle of operational data. We call this the institutional memory framework. The architectural commitment to treat operational data as a living learning system rather than a reference archive is what separates organizations that compound AI advantage from those that accumulate AI cost. 

The data foundation is already there. The decision about what to build on it is the only variable left. 

The Compounding Cost of the Wait 

The energy sector is entering a period where AI advantage compounds. Organizations that go live now will be running systems that have learned through thousands of hours of real operating conditions by the time their competitors are still refining pilots. 

Grid operations, reliability, and predictive maintenance are the applications energy leaders typically pursue first. They are also the ones that compound most sharply with continuous learning. A predictive maintenance system that has processed two years of real failure data across a fleet of transformers is qualitatively different from a system that has processed none. That gap does not close when the second organization eventually decides to start. It widens. 

This is the real cost of treating AI deployment as a technology problem to be solved rather than an operational commitment to be made. The loss is not a single delayed quarter. It is the accumulated learning gap that grows while organizations wait for a breakthrough that is not coming. 

Where the Decision Lives 

The energy leaders making the most meaningful progress on AI are the ones who answered a harder question. Which operational outcomes matter enough to organize the entire effort around? 

The starting point is simple. Grid load forecasting, AMI analytics, outage prediction, and field operations automation are all deployable today as agentic AI teammates that act on operational data utilities already own, execute decisions, and learn from every cycle. They are the foundation that makes every more complex application possible because each one builds the organizational infrastructure for learning, not just for experimenting. 

The right question for energy executives is not whether to invest in AI. That investment is already moving. The right question is whether the organization is built to learn from what it deploys, or whether each initiative will generate insight for one team instead of compounding advantage across the enterprise. 

Going live with AI in energy begins with a decision about what the organization is building toward and the commitment to treat every deployment as a step in that direction rather than a standalone test of the technology. 

That decision is available right now. The technology has been ready for a while. 

Building AI that operators will actually use requires a different kind of design than most energy organizations are attempting. Read Part 2: “Energy Operator Trust is Earned By Design”.

About the Author 

Scott Young is EVP of Growth and Strategic Alliances at Robots & Pencils, where he works with energy executives to move from decision to live. Connect with Scott on LinkedIn.  

Key Takeaways 

1. The AI applications energy leaders want most score highest on deployment readiness. The gap between investment and going live is organizational, not technological. 

2. Energy companies already have the data. Most are treating rich operational data as archived history rather than institutional memory. That distinction determines whether AI compounds or stalls. 

3. Energy companies already have the data. The decision about what to build on it is the only variable left. Organizations that make that decision and treat every deployment as a step toward compounding intelligence rather than a standalone technical test are the ones that go live and stay live. 

4. AI advantage in energy compounds over time. Organizations that go live now will hold a learning gap over later movers that closes only with years of real operational data, not with a better pilot program. 

5. The right leadership question is not whether AI is ready. It is whether the organization is built to learn from every deployment rather than evaluate each one in isolation. 

FAQs 

What does organizational readiness for AI mean in energy? 

It means the organization has defined which operational outcomes matter most, built the data infrastructure to support continuous learning against those outcomes, and established the decision process to evaluate and scale AI based on operational evidence rather than technical completion. 

Why do so many energy AI initiatives stall after a successful pilot? 

Pilots succeed at the local level because they are designed to prove technical performance. They stall at scale because scaling requires organizational infrastructure — shared data foundations, clear outcome definitions, and the governance to move from proof to live. Most organizations have not built those yet. 

What is the difference between archived data and institutional memory for AI? 

Archived data answers questions when asked. Institutional memory learns continuously, surfacing patterns, sharpening predictions, and improving with every cycle of operational data. The distinction determines whether AI compounds across the enterprise or produces isolated results for individual teams. 

How do utilities close the gap between AI pilots and live deployment? 

The fastest path from decision to live is standardizing the data foundation before scaling the AI system. Organizations that treat operational data as a shared institutional asset rather than system-specific input compress deployment timelines significantly and avoid the fragmentation that keeps most pilots from going live. 

How long does it actually take to go live with energy AI? 

It depends almost entirely on data infrastructure readiness, not model complexity. Organizations that have standardized their data foundations and committed to treating operational data as institutional memory have gone live with AI in 90 to 120 days. Organizations that treat each deployment as a custom integration build take two to three times as long and often stall before going live. 

Which energy AI applications are ready to deploy today? 

Operations and reliability use cases score highest on deployment readiness across the DOE’s priority applications. Grid load forecasting, AMI analytics, outage prediction, demand response optimization, and field operations automation are all deployable now using data utilities already collect. The barrier is organizational commitment, not technology availability. 

What is the cost of waiting to deploy AI in energy? 

The primary cost is the compounding learning gap. AI systems improve through real operational data. Organizations that go live now will be running materially smarter systems in two years than organizations that delay. That gap widens with time and does not close simply by starting later with better technology. 

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