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September 21, 2026

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September 21, 2026

Martin Mato, Principal Software Engineer at Kaizen Softworks

Martin Mato

Sci-Fi addict

Principal Software Engineer

AI

AI

Project Management

Project Management

When PMs can ship code, what changes for Engineering?

Published on

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September 21, 2026

Last updated on

·

September 21, 2026

Time to read

·

12

Martin Mato, Principal Software Engineer at Kaizen Softworks

Martin Mato

Principal Software Engineer

AI has changed what Product Managers can do.

A PM can now go from an idea to working software in hours. They can build a flow, put it in front of a customer, learn from it, change it, and test again without waiting for every iteration to go through Engineering.

That creates an opportunity for product teams. It also creates a new challenge.

Just because a PM can build something doesn’t mean that thing is ready to become production software.

If we don’t rethink how Product and Engineering work together, faster prototyping can become more code for Engineering to untangle, more unclear ownership, and more pressure to turn experiments into production features.

The answer is to separate exploration from construction.

PMs and engineers are solving different problems

During product discovery, the PM is trying to answer: Should we build this?

That means testing assumptions, changing direction quickly, throwing things away, and getting something real enough in front of a customer to learn from it.

Engineering is solving a different problem: How do we build this correctly?

That means thinking about architecture, security, maintainability, performance, edge cases, and everything else required for software that has to live in production.

Both need AI. But they don’t need the same working conditions.

Exploration benefits from speed, autonomy, and low friction. Construction needs stronger guarantees and guardrails. Trying to optimize the same environment for both creates tension.

So instead of asking how PMs can safely contribute code to the production codebase, there’s a more useful question: What if PMs had their own space to build and validate ideas?

Give PMs a safe place to explore

A PM working with an AI coding agent can build functional versions of new ideas. Not a Figma screen. Not a ticket describing what something might do.

Working software that can be used to test the product experience. The important part is that this environment is separate from production.

That boundary gives the PM freedom to experiment without requiring the same controls we’d expect from production software. The environment can be designed so experiments can’t affect the live product or access things they shouldn’t.

Now the PM’s workflow can look more like:

Idea → build → test with users → learn → iterate

Engineering doesn’t need to be pulled into every cycle.  Engineering still matters. It  gets brought in once Product has stronger evidence about what’s worth building.

The prototype shouldn’t be the handoff

This is where things can go wrong.

If a PM spends two days building something with AI and then gives the repository to Engineering saying, “It mostly works, can you finish it?”, we haven’t improved the product development process. We may have just moved the mess downstream.

The prototype should help answer product questions. It shouldn’t make technical decisions on Engineering’s behalf.

What Engineering needs from exploration is intent.

  • What does the feature need to do?
  • How should it behave?
  • What did we learn from customers?
  • What happens in the important edge cases?
  • How will we know when the production version works as intended?

That becomes the handoff.

At Kaizen, we’ve been exploring a model where the bridge between the two cycles is a behavioral specification and a test plan, supported by the working prototype as a reference. The implementation itself stays behind.

What crosses into construction is the spec: a behavioral description, a test plan, and a link to the prototype as reference. The prototype itself stays where it was built.

In simple terms:

Product owns the what. Engineering owns the how.

That distinction matters even more now that AI makes it so easy for both sides to generate code.

What the workflow could look like

A PM starts with a product hypothesis. Instead of immediately turning it into a backlog item, they use AI to build enough of the experience to test it. They put it in front of users. They discover that part of the original idea was wrong. So they change it. They test again.

Once the problem and desired behavior are clear enough, the PM closes the exploration cycle with a clear specification and acceptance criteria.

Engineering then starts from that understanding, not from the PM’s experimental code only. They decide how the feature fits into the architecture, how it should be implemented, what needs to be verified, and how it reaches production.

The result is two parallel forms of autonomy:

  • PMs don’t need Engineering for every experiment.
  • Engineers don’t inherit implementation decisions from every experiment.

More autonomy doesn’t have to mean less ownership.

AI can make discovery faster

A lot of the conversation around AI in software teams is still about developer productivity. How much faster can we write code? How much implementation can an agent take on?

Those questions matter. But for Product, there may be an even bigger opportunity upstream. AI can shorten the distance between having an idea and learning whether that idea is any good.

That changes what PMs can bring to an engineering team. Instead of: “We think customers want this.” They can increasingly say: “We tested this behavior with customers. Here’s what worked, what didn’t, and exactly what we now need the product to do.”

That’s a much better starting point for building production software.

If you have a prototype and want to take the idea into production, we can help you determine what needs to happen next.

AI has changed what Product Managers can do.

A PM can now go from an idea to working software in hours. They can build a flow, put it in front of a customer, learn from it, change it, and test again without waiting for every iteration to go through Engineering.

That creates an opportunity for product teams. It also creates a new challenge.

Just because a PM can build something doesn’t mean that thing is ready to become production software.

If we don’t rethink how Product and Engineering work together, faster prototyping can become more code for Engineering to untangle, more unclear ownership, and more pressure to turn experiments into production features.

The answer is to separate exploration from construction.

PMs and engineers are solving different problems

During product discovery, the PM is trying to answer: Should we build this?

That means testing assumptions, changing direction quickly, throwing things away, and getting something real enough in front of a customer to learn from it.

Engineering is solving a different problem: How do we build this correctly?

That means thinking about architecture, security, maintainability, performance, edge cases, and everything else required for software that has to live in production.

Both need AI. But they don’t need the same working conditions.

Exploration benefits from speed, autonomy, and low friction. Construction needs stronger guarantees and guardrails. Trying to optimize the same environment for both creates tension.

So instead of asking how PMs can safely contribute code to the production codebase, there’s a more useful question: What if PMs had their own space to build and validate ideas?

Give PMs a safe place to explore

A PM working with an AI coding agent can build functional versions of new ideas. Not a Figma screen. Not a ticket describing what something might do.

Working software that can be used to test the product experience. The important part is that this environment is separate from production.

That boundary gives the PM freedom to experiment without requiring the same controls we’d expect from production software. The environment can be designed so experiments can’t affect the live product or access things they shouldn’t.

Now the PM’s workflow can look more like:

Idea → build → test with users → learn → iterate

Engineering doesn’t need to be pulled into every cycle.  Engineering still matters. It  gets brought in once Product has stronger evidence about what’s worth building.

The prototype shouldn’t be the handoff

This is where things can go wrong.

If a PM spends two days building something with AI and then gives the repository to Engineering saying, “It mostly works, can you finish it?”, we haven’t improved the product development process. We may have just moved the mess downstream.

The prototype should help answer product questions. It shouldn’t make technical decisions on Engineering’s behalf.

What Engineering needs from exploration is intent.

  • What does the feature need to do?
  • How should it behave?
  • What did we learn from customers?
  • What happens in the important edge cases?
  • How will we know when the production version works as intended?

That becomes the handoff.

At Kaizen, we’ve been exploring a model where the bridge between the two cycles is a behavioral specification and a test plan, supported by the working prototype as a reference. The implementation itself stays behind.

What crosses into construction is the spec: a behavioral description, a test plan, and a link to the prototype as reference. The prototype itself stays where it was built.

In simple terms:

Product owns the what. Engineering owns the how.

That distinction matters even more now that AI makes it so easy for both sides to generate code.

What the workflow could look like

A PM starts with a product hypothesis. Instead of immediately turning it into a backlog item, they use AI to build enough of the experience to test it. They put it in front of users. They discover that part of the original idea was wrong. So they change it. They test again.

Once the problem and desired behavior are clear enough, the PM closes the exploration cycle with a clear specification and acceptance criteria.

Engineering then starts from that understanding, not from the PM’s experimental code only. They decide how the feature fits into the architecture, how it should be implemented, what needs to be verified, and how it reaches production.

The result is two parallel forms of autonomy:

  • PMs don’t need Engineering for every experiment.
  • Engineers don’t inherit implementation decisions from every experiment.

More autonomy doesn’t have to mean less ownership.

AI can make discovery faster

A lot of the conversation around AI in software teams is still about developer productivity. How much faster can we write code? How much implementation can an agent take on?

Those questions matter. But for Product, there may be an even bigger opportunity upstream. AI can shorten the distance between having an idea and learning whether that idea is any good.

That changes what PMs can bring to an engineering team. Instead of: “We think customers want this.” They can increasingly say: “We tested this behavior with customers. Here’s what worked, what didn’t, and exactly what we now need the product to do.”

That’s a much better starting point for building production software.

If you have a prototype and want to take the idea into production, we can help you determine what needs to happen next.

Common questions

Does Engineering inherit the prototype's technical debt?

No, and that's the point of the separation. Engineering starts from the behavioral specification and acceptance criteria, not from the exploration repository. The prototype stays as a reference for how the experience should feel, not as a starting codebase.

What happens when the prototype already works and the business wants to ship it?

This is the most common pressure, and it has to be settled before it shows up rather than in the moment. Agree upfront that the prototype is not a release candidate and that the deliverable of exploration is the specification. Without that agreement, the model breaks the first time something is urgent.

Doesn't writing a behavioral spec just add work for the PM?

Most of the drafting can be automated. The coding agent already has the exploration session (what was built, what changed after user feedback, and which flows ended up working), so it can produce the first version of the behavioral spec and acceptance criteria. What stays with the PM is the judgment: confirming what the tests proved, correcting what the agent inferred, and deciding which behaviors are non-negotiable. Reviewing a draft is a much smaller task than writing a document from scratch.

1
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Catalyst 26 was Partnership Leaders' fifth annual conference for partnership, ecosystem, and go-to-market professionals. It took place August 25 and 26, 2026, at the Marriott Hotel at the Brooklyn Bridge in New York, with more than 1,000 attendees and 70-plus speakers from companies including Anthropic, OpenAI, Google, Microsoft, IBM, BCG, and Siemens.

Dates August 25–26, 2026
Location Marriott Hotel at the Brooklyn Bridge, Brooklyn, NY
Edition 5th annual
Attendees 1,000+ partnership, ecosystem, and GTM professionals
Speakers 70+, including people from Anthropic, OpenAI, Google, Microsoft, IBM, BCG, and Siemens.
Price $849 early bird, rising to $999, then $1,999

Who Catalyst events are for

Catalyst brought together people building and running partner programs across SaaS, AI, consulting, systems integration, agencies, and major cloud platforms.

Attendees included executives leading partnership organizations, and people working directly in alliances, partner sales, marketing, operations, strategy, and enablement.

What Catalyst 26 is like

You can look at the agenda before a conference and have a pretty good idea of what you'll find. Being there is different.

This year's theme was "Navigating Frontier Ecosystems". Anthropic's Head of Partnerships and one of OpenAI's partner program leads appeared on the same agenda as people from Oracle, Siemens, IBM, and BCG, companies that have run formal partner programs for two decades.

That mix was one of the most interesting parts of the conference. Newer AI companies were discussing partner tiers, co-selling, and joint delivery alongside companies where those models have been part of their business for years.

What Catalyst 26 covered

Catalyst 26 split its sessions into eight pillars:

  • Advancing Organizational Maturity: turning partnerships into something measured and repeatable instead of one founder doing favors for another.
  • Become a Strategic Partner: getting partnerships involved when product and business decisions are made, not told about them afterward.
  • Frontier Partner Experience: adapting partner programs as AI changes how companies build and integrate products.
  • Path to CPO: career sessions for people aiming to lead partnerships at the executive level.
  • Co-Build: two companies building something together.
  • Co-Market: two companies running a campaign together.
  • Co-Sell: two sales teams working the same deal.
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Catalyst 26 sessions

Day 1 Keynote

The Day 1 keynote brought together Partnership Leaders’ CEO Asher Mathew, Tribe AI’s Co-founder & CEO Jaclyn Rice Nelson, Anthropic’s Head of Partnerships Phil Samenuk, and Boomi’s Chairman & CEO Steve Lucas.

Their discussion focused on how companies are relying on partners to build, sell, and deliver products across AI, cloud, and enterprise software. A few points stood out:

  • More companies have dedicated partner teams now, which means a generic, one-size-fits-all partner program doesn't cut it anymore. Partners show up when the program fits how they work.
  • New AI products and cloud services are shipping so fast that a partner program can't just get set once and left alone. Incentives, support, and how you work together need regular updates.
  • Partnerships also came up as a way to access data a company couldn’t reach on its own, whether that meant getting access to it, combining it, or putting it to use.
  • AI doesn't change the basics of a good partnership. Account planning, clear ownership, and relationships built over time still matter most.

Day 2 Keynote

The Day 2 keynote featured Ramp’s Lead Economist Ara Kharazian, Eliza’s Founder Brian Benedict, Siemens’ EVP Global Partner Ecosystem Dion Smith, and Oracle’s SVP, Partner Sales & Operations Strategy Leah Yomtovian.

A few points stood out:

  • The spending data told a slower story than expected: AI adoption is mostly going toward productivity gains and task automation, not some overnight shift.
  • Siemens is in the middle of folding more than 68,000 partners and roughly 200 separate programs into a single global one, mainly to make it easier to coordinate across IT and operational technology.
  • Oracle's approach is a running "listening tour": every partner gets the same baseline benefits, then incentives and credits get layered based on the type of partner and how they work with Oracle.
  • There was also talk of a newer kind of service team: bring in engineers, turn AI requirements into working products, and reuse delivery methods that already work instead of starting from scratch each time.

Next Catalyst events

The date and location of Catalyst 27 hasn’t been announced yet. In the meantime, you can check out the half-day Catalyst Summits in different cities:

  • October 20, 2026 - Seattle
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Check Partnership Leaders’ events page for updates.

·

Aug 26, 2026

Why adding people doesn't always fix a struggling team

Learn when a software team should hire, wait, reorganize, or build skills internally, and how to tell which option will actually help.

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When a client asks to hire someone new, a common reaction is to open a search. There's more work, more pressure, and new features to build. It seems like the obvious thing to do.

But in our experience working with software development teams, the problem often isn't a lack of people. The problem is knowledge concentrated in too few people, unclear team roles, slow onboarding, or temporary demand.

The question worth asking isn't who can fill the position, but what would help the team work better. That points to one of three answers: hire, don't hire, or build the capability from within. Figuring out which one applies, and why, is the real work before opening a search.

What you should ask before assuming you need someone new

Hiring works when three conditions are met: the need will last, no one on the team has the capacity to take it on, and the team can onboard someone well. That last condition is easy to overlook. A team can have a real, lasting gap and still not be ready to bring someone in if no one has the time to guide them.

The risk comes from jumping straight from "there's more work" to "we need someone" without checking what's causing the pressure. It's easy to turn a request into a list of requirements (X years of experience, a specific technology, advanced English) and start the search. The real cause is often something else: a project that grew too fast, a tech lead with no time to onboard new hires, processes that stopped scaling, or a team that lost key people and needs to recover knowledge before adding headcount.

That's why, before thinking about who could fill the role, we ask these questions:

  • What outcome is the client trying to achieve?
  • What's happening on that team today?
  • What specific problem is this hire meant to solve?
  • Does adding a person solve that problem?
  • Is there someone on the team who could take this on?
  • Are there other, less obvious alternatives?

When the answers confirm the need will last, the current team can't cover it, and the team has the capacity to onboard someone, hiring is the right call: opening the search fills a gap the team can't close internally.

Does the problem need someone new to fix it?

Not hiring is the right call when the problem behind the request is temporary, or when it will resolve before the new hire finishes onboarding. Recommending against a hire may sound unusual for a company that offers staff augmentation, but our job as a strategic partner is not to maximize every opportunity but to recommend the best decision for the client. Depending on what's actually going on, the fix can look like:

  • An internal rotation: moving someone with spare capacity into the gap.
  • Reorganizing responsibilities across the team instead of adding a seat.
  • Hiring a different profile than the one originally requested.
  • Combining two roles into one instead of opening two searches.
  • Waiting a few weeks, when the project context is about to change on its own.

Is a temporary increase in workload a good reason to hire?

This happened on a project with a long onboarding period. The initial request seemed clear: hire a mid-level developer. There was work and budget available. But when we spoke with the team, we found that the workload increased because one team member had been temporarily reassigned to another sub-team. Before moving forward, we considered what would happen when that person came back.

The client's system was complex: any new hire needed several months to understand the business, the architecture, and the platform before they could contribute independently.

The problem justifying the hire was going to disappear, but the new hire wouldn't. By the time that person had enough context, the need that started the search would no longer exist.

We recommended against moving forward, even though there was budget to add someone. The client avoided an unnecessary hire and months of onboarding for a problem that was already resolving itself. Sometimes the best answer is to wait a few weeks; other times, it's reorganizing the team or developing internal talent.

How can you build team capability without hiring?

Build capability internally when the team already has product context but lacks a specific skill. Developing that skill internally can be faster than waiting for someone new to reach the same level of context.

More people doesn't always mean more capacity. Onboarding a new hire takes time from the people already on the team: explaining the business and the architecture, reviewing their work, and building trust. That's why, during the first few weeks, a team can become less productive while it onboards someone new. Complex projects can include years of technical decisions and undocumented knowledge. New hires still need time to learn that context.

Should you hire a specialist or train someone on your team?

A client needed a senior SQL Server specialist. That niche skill set made the role difficult and expensive to fill. We started the search and interviewed candidates, but the deeper issue became clear quickly: the real challenge on the project wasn't SQL Server. It was understanding a product shaped by years of evolution, multiple applications, and complex business logic.

The right person to develop that expertise was already on the team. Instead of hiring someone with deep SQL Server expertise, the client supported that team member in building the SQL Server skills the project needed. That person had business knowledge, motivation, and a much shorter learning curve than an external hire would have had. An outside specialist provided targeted support when needed.

The team gained SQL Server expertise without losing months waiting for a new hire to learn the product first. The person who took on SQL Server gained a valuable new skill without stepping away from the other work they were doing on the project.

A team's capacity depends on how its people complement each other, what knowledge they share, and what autonomy they've developed, not just on headcount. A team of ten people who are aligned, with shared context and autonomy, can generate more value than a team of fifteen where much of the time goes into onboarding new hires.

Should you hire, wait, or develop the skill internally?

Scenario Signal What to do
Hire The need will last, no one on the team can cover it, and the team can onboard someone well Open the search for a clearly defined role
Don't hire The problem is temporary or resolves before onboarding finishes Wait, reorganize the team, or cover the gap another way
Build internal capability Missing specific expertise, not people; someone already has the business context Develop the skill internally, with targeted outside support if needed

What questions do we ask first?

  1. What specific problem are we trying to solve?
  2. Will the need still exist after the person has been hired and onboarded?
  3. Is there someone on the team who could cover it?
  4. Do we have the capacity to onboard someone well?
  5. Is the problem a lack of people, or is it caused by unclear roles, missing product knowledge, slow onboarding, or a temporary increase in workload?
  6. What impact will this hire have six months from now?
  7. If we couldn't hire today, what other option would we explore?
  8. What higher-priority work would someone on the team have to stop doing to cover this need?

Wait to open a search when the team can't define the problem, confirm the need will last, or support onboarding. Clarify those points first.

If you're weighing this decision with your own team, let's talk about whether to hire, reorganize, or develop someone already on the team.

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