I build infrastructure that replaces expensive unicorn hires.
For 10 years, I helped companies stop wasting money on “Data Scientists” who couldn’t scale. Instead, I built data infrastructure and analytics systems that actually worked—predictable, scalable, and way cheaper than hiring.
I was Director of Analytics Engineering (before Analytics Engineering was a thing), co-founded multiple companies, and published articles on why systems always beat individuals at scale. My 2019 article calling out the Data Scientist hype? Turned out to be right.
Now I’m applying the exact same thinking to content production.
At Mira Studios, we’re building content production infrastructure that replaces the need for full-time “content creators.”
Companies are making the same mistake with content that they made with data in 2015: hiring expensive unicorns expecting magic. One person who’s supposed to shoot, edit, strategize, write, design, and somehow make you go viral on TikTok.
It doesn’t work. It didn’t work with data, and it won’t work with content.

Here’s what does work:
→ Production systems, not individual creators
→ Batch processing (30 videos in 2 days, not 2 per week)
→ Professional infrastructure (studio + equipment + editing pipeline)
→ Measured efficiency (cost per asset, utilization rates, ROI)

The math is simple:
	•	Content creator salary: €50k-65k/year
	•	Mira Studios production system: €42k/year
	•	Output: 50% more content, better quality, zero risk if someone quits

I apply data engineering principles to content production: batch processing, pipeline optimization, throughput metrics. Most studios talk about “creativity.” I talk about deliverables per production day and cost per finished asset.

If you’re hiring a “content creator” right now, we should talk first.
I’ve seen this movie before. I know how it ends. Let me show you the alternative.

𝐔𝐬𝐞𝐫 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐡𝐚𝐬𝐧'𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐢𝐧 30 𝐲𝐞𝐚𝐫𝐬. 𝐖𝐞 𝐣𝐮𝐬𝐭 𝐠𝐨𝐭 𝐭𝐡𝐞 𝐟𝐢𝐫𝐞𝐩𝐨𝐰𝐞𝐫 𝐭𝐨 𝐟𝐢𝐱 𝐭𝐡𝐚𝐭.

Thrilled to share :) that Koji has been awarded an ElevenLabs Startup Grant
12 𝘮𝘰𝘯𝘵𝘩𝘴 𝘰𝘧 𝘢𝘤𝘤𝘦𝘴𝘴 𝘵𝘰 𝘵𝘩𝘦 𝘸𝘰𝘳𝘭𝘥'𝘴 𝘭𝘦𝘢𝘥𝘪𝘯𝘨 𝘷𝘰𝘪𝘤𝘦 𝘈𝘐 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮.

But here's why this actually matters:

Traditional research is broken. 
→ 8-12 week cycles 
→ €60-80K annual cost for a dedicated researcher 
→ Survey fatigue killing response rates 
→ Only the loudest voices get heard

The result? Customer understanding has become a privilege, reserved for companies with six-figure research budgets.

We're building Koji to change that.

AI-powered conversational interviews that scale. Not surveys. Not chatbots. Real conversations that capture what customers actually think, and how they feel.

This grant accelerates everything.
With ElevenLabs' voice technology, we can make these conversations even more natural, more human, more accessible. Research that meets people where they are.

This is research democratization in action.

Small team. Clear mission. Relentless execution.

2026 is the year customer research stops being a bottleneck and starts being a superpower for every team.

Grateful to ElevenLabs for believing in what we're building.

Now back to work.

Wesley Nitikromo

GTM @ Koji | Founder @ Mira Studios | Former Analytics Engineering Director | Data & Fintech Expert | Angel Investor

I’ve taken 50+ investor calls. Maybe 5 are going to lead somewhere, IYKYK 🔥
The other 45 were courtesy calls disguised as interest.

You end up being dealflow. Or “getting acquainted for a later stage.”

Translation: not now, but we want to keep you warm in case you blow up and we missed it.

I get it. VCs need pipeline. They need to see enough companies to pattern match. They need to stay close to markets before they’re ready to deploy.

But let’s be honest about what this costs.

For founders: 200+ hours of prep, pitching, and follow-ups that lead nowhere. Time that could’ve gone into building, selling, hiring.

For VCs: hundreds of 30-minute calls to find the 3 deals a year they’ll actually write a check for. Most of those calls could’ve been an email. Or less.

Both sides lose. And both sides know it.

So here’s a thought:

What if there was a layer in between?

An AI-powered conversation that captures how a founder thinks, what they’ve built, why they’re building it, and where they’re going. Something a VC could review async before deciding if a call is worth both their time.

And for founders: a way to share your story once, deeply, and let it filter for real interest instead of polite interest.

Not a pitch deck. Not a cold intro. A conversation that travels.

This isn’t about replacing human connection. The best investor relationships are deeply personal, just have a look at Boardy for example. 

It’s about earning that first real conversation faster. By filtering out the 45 to find the 5. 

That’s one of the use cases we’re exploring with Koji AI-native Interviewer.

Because fundraising shouldn’t be a numbers game. It should be based on a great fit.

No alternative text description for this image

Yesterday I went to a ProductTank Meetup with 130 people from the Product Community, at Ingka Group | IKEA. 

They showcased how they combine Brainstorming with AI, AI-Interviewers, and next-level Prototyping into real potential value for IKEA. 

Someone from the audience who was also working at IKEA raised that this is potentially a very costly experiment, and there was no clear insight into the cost/benefits.

Next, Wolters Kluwer presented how they re-aligned their offering to the GenAI age, which was very insightful.

I made me think about how many People in product actually care about customers and feedback, so I did a little search. 

As you can see below in the picture, the ratio between people in Product and UX represents a big gap in most companies.

Luckily, both Wolters Kluwer and IKEA seem to understand this.

It raises the question: Should we increase the headcount of UX Researchers to improve this ratio, or should we fundamentally rethink how we do it?

Stop calling 2026 the year of agentic AI. Agency starts with asking better questions.

This year I’m building at the intersection of conversations and creativity:

→ Koji where I lead GTM for an AI-powered customer research platform 🚀

→ Mira Studios - my content studios helping brands and creators find their voice 🎙️

Different domains. Same conviction.

Here’s what I’ve learned working with AI every day:

The founders who win aren’t the ones who automate the most. 🤖

They’re the ones who ask better questions.

AI didn’t replace my thinking. It sharpened it.

It didn’t give me answers. It gave me better problems to solve.

The best GTM engineers I know use agentic AI to stress-test positioning, not write it for them. 👻

The best product managers use it to surface patterns in customer feedback, not to skip reading it. 📄

The best founders use it to think two moves ahead, not to avoid thinking altogether.

This is the real unlock:

Agentic AI as an extension of your creativity and business acumen, not a substitute for it.

At Koji, we’re not building AI that replaces researchers. 

We’re building agentic conversations that help teams truly understand their customers.

At Mira Studios, I’m not using AI to churn content. I’m using it to find the story that actually matters.

The common thread? Human agency, amplified. 🚀🚀🚀

2026 will reward the people who understand the difference between delegating to AI and collaborating with it.

So here’s to a year of better questions, deeper conversations, and work that actually means something.

Zullen we gewoon terug naar Nederlands op LinkedIn, het is niet alsof mn Amerikaanse connecties nu al wakker zijn..

50% van de impressies van mn posts komen uit Amsterdam.

Wanneer heb je voor jezelf even gekeken toen je weer een AI gegenereerde post eruit stuurde, wat jou doelgroep/bereik is?

We zijn hier met zn allen OpenAI en Gemini aan het sponsoren met deze AI meuk, en LinkedIn laat je niet eens meedelen met de opbrengsten 🤣

Wat zijn de Europese alternatieven? Wie maakt me los?

#EUFirst #NLtech #FDT

Surveys have a 12% response rate.
We keep using them anyway 😂 Whyyyy?!?

Because they’re easy to send.

Because they’re cheap to scale.

Because “we need data” and a survey feels like doing something.

But here’s what a 12% response rate actually means:

88% of your customers stayed silent 🥸

The insights you’re acting on come from the most motivated, most opinionated, or most bored 12%.

That’s not a representative sample. 

That’s survivorship bias dressed up as research.

And yet. We build roadmaps on it. We craft messaging around it. We make million-dollar decisions based on what a tiny slice of customers bothered to click through.

The problem isn’t the survey. It’s the format.
Nobody wants to fill out a form. But people will talk.

Give them a conversation instead of a questionnaire and something shifts. 

They open up 🤩

They explain the “why” behind the answer. They tell you things they didn’t even know they thought.

That’s what we’re building at Koji User Research. AI-powered interviews that feel like conversations, not checkboxes.

Not because surveys are evil. But because your customers deserve to be heard, not processed.

2026 is the year teams stop optimising response rates and start optimising for understanding.

Which side are you on?

Ontzettend gaaf om met Josefine Ezinga van Asdonk aan de slag te gaan 🚀 
Ben jij er klaar voor om je videos in De Koepel in Haarlem op te nemen, met distributie naar een miljoenenpubliek via Nieuws.nl neem dan snel een kijkje 👀

Filmdirector- External Press Kit Director (V)Nieuws.nl

View Josefine Ezinga van Asdonk’s profile

Al jarenlang krijg ik de vraag of ik als regisseur kan helpen bij het maken van video’s. Vaak was het budget een struikelblok. Die drempel halen we nu weg.
Je kunt tóch aan de slag, professioneel, schaalbaar en op jouw tempo.
En het bereik? Nieuws.nl behaalt miljoenen views per maand.
Het eindresultaat kan worden geplaatst op ons nieuwskanaal, inclusief een branded content artikel dat jouw verhaal versterkt. 

Samen met Wesley Nitikromo van Mirastudio's en ons team o.a. bestaande uit geluid expert Dave Frederking maken we voor jou content voor een heel jaar!
2026 staat in het teken van video, zichtbaarheid en authenticiteit.
Wil jij daar nu al op voorsorteren? Dan is dit het moment.

Doe jij nog aan ouderwetse surveys? Koji AI-native Interviewer is het moderne alternatief, waarmee je klanten echt leert kennen.
Nu volledig custom te integreren met onze Headless API 🚀🚀🚀

Wil je meer weten, stuur me een dm ;)

NYC just created a government role that most companies still don’t have.

Mayor Mamdani’s new Office of Mass Engagement has one mandate: stop listening only to the “wealthy and well-connected” and start reaching everyday New Yorkers where they are.

Their approach?

→ Not town halls where only the loudest voices win
→ Not surveys that get ignored
→ But genuine, ongoing conversations with transparent feedback loops

Sound familiar? It should.

This is exactly what research democratization looks like, and it’s now happening in government before most companies figure it out.

The uncomfortable truth: 
your customers aren’t being heard either.

Traditional research is slow, expensive, and often captures only the most motivated voices. 

The rest? Silent. Invisible. Churned.

At Koji, we believe every customer deserves to be understood — not just surveyed.

That means:
• Conversations, not questionnaires
• Emotional context, not just NPS scores
• Closing the loop, not just collecting data points

When NYC says they want to “demystify government so all New Yorkers can be active participants”, that’s the same shift we’re driving for product and GTM teams.

Research shouldn’t be a privilege. 

Understanding your customers shouldn’t require a six-figure budget.

The future of engagement is conversational, inclusive, and continuous.

Government is catching on. Is your company?

I just got interviewed by an AI. 
It was uncomfortably good (and fun).

2026 is supposed to be the year of agentic AI. 

Everyone’s talking about agents that act, automate, execute 🤖 

But what about agents that listen?

There’s a reason the smartest people in product and growth are paying attention to AI-powered research right now. 

The category is heating up. And for good reason 🔥

Traditional research is too slow for how fast teams need to move. 

The companies that figure out continuous customer understanding will outpace everyone else.

We’re building Koji User Research to solve exactly that. 

And last week I decided to eat my own dog food.

I sat down with our AI interviewer. Not as a founder. As a participant.

Here’s what surprised me:

It didn’t rush. It followed up on things I said in passing. 

It asked “why” more than I expected. And it caught something I hadn’t even articulated to myself yet 🤔

That’s when it clicked.

The best agentic AI in 2026 won’t just do things faster. It will understand things deeper.

Surveys don’t do that. Forms don’t do that. Even most human interviewers don’t do that consistently.

But a well-designed AI conversation? It can.

We called the experience “Researching the Researchers” because that’s exactly what it is. 

The team that builds customer research tools, getting researched.

If you’re exploring this space, I’d love to hear what you think.

Takes 10 minutes. No signup. Just a conversation.

PMs talk to 5 customers a month. Then spend the other 25 days defending decisions based on those 5 conversations.

Sound familiar?

You did the research. You took the calls. You wrote up the notes. You synthesized the insights.

And then someone in the room says: “But that’s only 5 people.”

They’re not wrong.

5 conversations is not statistical significance. It’s not a mandate. It’s a starting point dressed up as a conclusion.

But here’s the thing. You know it too.

You’re not lazy. You’re not cutting corners. You’re stuck in a system that forces you to choose between depth and volume.

Deep customer conversations take time. Scheduling takes time. Synthesis takes time. There are only so many hours.

So you do 5. Maybe 10 if you’re lucky. And you hope it’s enough.

Then you walk into the room and defend a roadmap based on what is essentially a very educated guess.

This is the PM credibility gap.

And it’s not a skills problem. It’s an infrastructure problem.

What if you could run 50 customer conversations this month instead of 5? Without hiring a research team. Without blowing your budget. Without living in Calendly.

That’s the unlock.

Not replacing your judgment. Giving you the evidence to back it up.

Your intuition is probably right. Now imagine having the data to prove it.

𝐖𝐞’𝐯𝐞 𝐬𝐞𝐞𝐧 𝐭𝐡𝐢𝐬 𝐛𝐞𝐟𝐨𝐫𝐞. 𝐂𝐥𝐨𝐮𝐝 𝐝𝐢𝐝 𝐢𝐭. 𝐃𝐚𝐭𝐚 𝐝𝐢𝐝 𝐢𝐭. 𝐍𝐨𝐰 𝐀𝐈 𝐢𝐬 𝐝𝐨𝐢𝐧𝐠 𝐢𝐭. 𝐀𝐧𝐝 𝐦𝐨𝐬𝐭 𝐭𝐞𝐚𝐦𝐬 𝐚𝐫𝐞 𝐦𝐚𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐦𝐢𝐬𝐭𝐚𝐤𝐞.

Let me explain.

In the early cloud days, everyone threw everything at AWS. No structure. No strategy. Just “put it in the cloud.”

Then the smart teams figured out there were layers. Infrastructure. Platform. Application. Each with different rules, different tools, different skills.

The same thing happened with data.

First everyone just “did analytics.” Dashboards everywhere. Data chaos.

Then the modern data stack emerged. Ingestion. Transformation. Orchestration. Suddenly there was a clear architecture. And the teams who understood the layers outpaced everyone else.

I rode that wave. I watched companies go from data chaos to data clarity in months once they understood the stack.

Now AI is at the same inflection point.

Most teams are still in the “throw everything at ChatGPT” phase. Every problem, one solution. Prompt and pray.

But the stack is splitting. And if you look closely, three distinct layers are emerging:

𝐋𝐚𝐲𝐞𝐫 1: 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠

Your ChatGPT, your Claude, your Gemini. Great for thinking, brainstorming, writing. Brilliant generalists. But generalists nonetheless.

𝐋𝐚𝐲𝐞𝐫 2: 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐚𝐠𝐞𝐧𝐭𝐬

AI that’s built for one job and does it exceptionally. Not an assistant you prompt. A co-worker you delegate to. It has context, memory, and domain expertise you’d never fit in a prompt. Think Koji User Research 👀 

𝐋𝐚𝐲𝐞𝐫 3: 𝐂𝐮𝐬𝐭𝐨𝐦 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬

AI orchestrated for your specific processes. Your data, your logic, your systems. Stitched together to run exactly how your business operates.

Most people are stuck on layer 1, trying to make it do layer 2 and 3 work.

That’s like running your entire company on spreadsheets because Excel technically “can” do anything.

The competitive advantage in 2026 goes to the teams who understand which layer to use for what.

General thinking? Layer 1.

Customer research at scale? Layer 2.

Your proprietary sales workflow? Layer 3.

Business cycles have always rewarded the same pattern: those who adapt to the shifts during expansion become the market leaders of the next cycle.

The architecture is emerging. The winners are already building on it.

Which layer are you building on?

ps. In this picture I was getting a masterclass in businesses cycles from someone who managed to navigate business cycle for almost half a century.

𝐇𝐨𝐭 𝐭𝐚𝐤𝐞: 80% 𝐨𝐟 𝐕𝐂 𝐢𝐧𝐭𝐫𝐨 𝐜𝐚𝐥𝐥𝐬 𝐬𝐡𝐨𝐮𝐥𝐝𝐧’𝐭 𝐞𝐱𝐢𝐬𝐭.

Not because founders aren’t worth talking to. 
But because both sides already know it’s not a fit within 10 minutes.

The VC needed metrics you don’t have yet.

The founder needed a lead check, not a seed investor.

The term expectations were miles apart.

Nobody asked until minute 47.

Here’s what’s wild:

VCs have AI for deal sourcing. AI for market mapping. AI for competitive analysis. AI for portfolio support.

𝐁𝐮𝐭 𝐭𝐡𝐞 𝐨𝐧𝐞 𝐭𝐡𝐢𝐧𝐠 𝐭𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐬𝐭, 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐭𝐨 𝐟𝐨𝐮𝐧𝐝𝐞𝐫𝐬, 𝐢𝐬 𝐬𝐭𝐢𝐥𝐥 𝐚 𝐜𝐚𝐥𝐞𝐧𝐝𝐚𝐫-𝐬𝐭𝐮𝐟𝐟𝐞𝐝 𝐠𝐮𝐞𝐬𝐬𝐢𝐧𝐠 𝐠𝐚𝐦𝐞.

What if the first filter wasn’t a 30-minute call? 
What if it was a 5-minute conversation that surfaced the real questions upfront?

Things like:

→ “Can we connect to your Stripe to understand revenue patterns?”
→ “Here’s our standard term sheet. What’s your reaction?”
→ “What matters more to you right now: capital or our network?”
→ “What would make you walk away from this deal?”

Not a form. A conversation. One that gives founders a real sense of the fund’s style and gives VCs signal before burning an hour.

The result: fewer dead-end calls. Faster conviction on both sides. Founders spend time with VCs who are actually aligned.

This is what we’re building at Koji. 
Conversational research that filters for fit before anyone opens a calendar.

Works for customer discovery. Works for buyer intelligence. Turns out it works for VC scouting too.

What would you want to know before taking your next intro call?

Raphael Neuhaus, thanks for the inspiration 👋

If AI can answer any question, what happens to the dashboard?

I’ve been thinking about this a lot lately.

We spent the last decade building the modern data stack. Ingestion, transformation, warehousing, visualization. Billions invested. 

Entire careers built around making dashboards look good.

And now we have AI that can query data conversationally. No SQL. No filters. No clicking through 47 tabs to find one number.
Just ask.

So what happens to Tableau? To Looker? To the BI analyst role?

A few possibilities:

→ Dashboards become the “source of truth” layer, AI becomes the interface

→ BI tools evolve into AI-native platforms (some already are)

→ The whole category gets absorbed into the LLM layer

→ Nothing changes because enterprises move slow

Honestly? I don’t know the answer.
So we’re running a study to find out.

How are you using BI tools today? How do you think AI will change that? What would you never trust AI to answer?

Takes 5 minutes. It’s a conversation, not a survey.

I’ll share the results with everyone who participates.

select * from human where knowledge = ‘key’;

Everyone is building AI to write content for humans. We are flipping the script. We use AI to extract content FROM humans.

The internet is currently drowning in synthetic noise.

Founders and PMs are racing to find the perfect prompt to make their AI sound "more human." They are trying to fake authenticity at scale.

We looked at that race and decided to run the other way.

You don't need AI to generate more ideas for you. 

You already have the ideas. Your customers already have the insights. They are just dying in forgotten Zoom recordings, messy notion docs, and lost slack threads.

The problem isn't a lack of content. It’s a leak of wisdom.

So we built Koji differently.

We didn't build a generator. We built a refinery.

Instead of prompting an LLM to hallucinate a blog post, we let your customers speak freely. Then, we use AI to listen, structure, and refine their actual words into usable strategy.

In a world of infinite generated noise, the only scarcity left is genuine human insight. The goal of AI shouldn't be to replace the thinker. It should be to amplify the thought.

Flipping the script - using AI to generate content from humans instead of AI generating content for humans

The most expensive decisions in your company are based on the thinnest evidence.

Pricing strategy? Based on 8 customer calls from last quarter.

Roadmap priorities? Based on what the loudest customers asked for.

Market positioning? Based on a competitor analysis and gut feel.

Go-to-market motion? Based on what worked at your last company.

This isn’t a criticism. It’s the reality of operating under uncertainty.

But here’s what keeps me up at night:

The cost of a wrong product bet isn’t the dev hours. It’s the 18 months of opportunity cost while the market moved on.

The cost of a wrong positioning isn’t the campaign budget. It’s the thousands of prospects who tuned out and won’t tune back in.

The cost of misunderstanding churn isn’t the lost revenue. It’s the pattern you’ll repeat because you never really understood why they left.

Executive teams obsess over financial data. Operational data. Market data.

But customer understanding? That’s still treated like a “nice to have.” Something product handles. Something that lives in a research deck from six months ago.

The companies that win in 2026 won’t just have better data. They’ll have better signal on the question that actually matters:

Why do people buy, stay, or leave?

Not what they clicked. Not what they rated. Why.

Not just more data. Deeper understanding. At the speed your decisions demand.

Als je deze hook met 177.782 impressies interessant genoeg vind, maar je begrijpt helemaal niet wat ik probeer te zeggen. 

Klik dan vooral. Open de post en lees hem. Voor wie het nog niet wist, LinkedIn heeft bij iedere post een vertaalknop.

Waarom blijven we posten in het Engels?

Waarom blijven we glad gepolijste en gegenereerde troep versturen?

Reageer een beetje meer in de comments van een ander en ga een gesprek aan.

Koop geen likes, wees jezelf.

Maar in welke taal dan? Whatever floats your boat 🙌

graphical user interface, text, application

Amazed by what this team has been able to ship, this grant will speed up things even more 🚀

Scale Customer Discovery with AI | Founder @ Koji | AI & Product Leader

We've been granted 33M credits to power our AI voice capabilities. 🎉

This is a game-changer for how businesses collect customer feedback.

Here's what this means for product teams and researchers:
👉 Instead of reading through endless survey responses, you'll soon be able to have natural voice conversations with your users. 
👉 Our AI conducts the interviews, asks follow-up questions, and delivers insights — all while sounding remarkably human thanks to ElevenLabs' voice technology.

We're joining a portfolio of AI-first companies that ElevenLabs has chosen to support in transforming how people interact with technology. 

Their belief in Koji's vision to make user research 10x faster validates what we've been building.

Big thanks to the ElevenLabs team for backing us. 

And to all the product managers and founders who've been testing Koji with us — this accelerates everything we're building for you.

More announcements coming soon 🚀

The GTM Engineer proved one thing: 𝐬𝐢𝐥𝐨𝐬 𝐚𝐫𝐞 𝐝𝐲𝐢𝐧𝐠. 
Customer research is next.

Rewind 10 years.

Data lived in dashboards no one understood. Analysts spoke SQL. Marketers spoke pipeline. Finance spoke spreadsheets. No one spoke the same language.

Then the Analytics Engineer emerged 🚀 

A hybrid role that sat between data engineering and business intelligence. Someone who could build the models AND translate them into decisions. The silo-breaker.

Fast forward to today.

GTM teams faced the same problem. Marketing, Sales, RevOps, Product all working toward "growth" but rarely aligned. Handoffs everywhere. Insights lost in translation.

Enter the GTM Engineer 🚀 

Someone who automates workflows, connects systems, and turns go-to-market from a relay race into a single motion. Another silo down.

But here's what I keep thinking about:

𝐖𝐡𝐨 𝐨𝐰𝐧𝐬 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞?

Not the researcher who runs quarterly studies. Not the PM who talks to 5 users a month. Not the sales rep who logs call notes that no one reads.
I'm talking about someone responsible for:

→ Continuously validating and evolving the ICP 
→ Aligning buyer intent with product capabilities 
→ Feeding customer intelligence into every GTM motion, in real time

Call it a Customer Discovery Engineer. A Buyer Intelligence Lead. Whatever.

The point is: this role doesn't exist yet. But it should.

Because right now, customer understanding is still stuck in silos. 
Research lives in a Notion doc. Win/loss analysis happens once a quarter. Voice of the customer is anecdotal at best.

The GTM Engineer connected systems. The next role will connect understanding.

That's what we're building toward at Koji, infrastructure for continuous customer discovery that the whole org can actually use.

What do you think this role will be called this time?

GTM Stack "WHY" Problem

In 2026 we will go from prompting ourselves to handing over more and more of what we do to agents. Are you going to be more productive by delegating your to-do list to agents?

How prevalent is agentic commerce today?

We spoke with payments strategist Dwayne Gefferie, who provided his insight on where we are today and how he expects agentic commerce to develop in the future.

Learn more about the impact that agentic commerce can have for businesses and consumers: https://bit.ly/4oYbWkW

#AI #AgenticCommerce #Worldpay

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Wesley Nitikromo's Linkedin Analytics