Kaizen Teams

Dropdown

Table of Contents

Time to read

·

12

Published on

·

June 10, 2025

Last updated on

·

May 26, 2026

Mathias Talon, Head of Strategic Partnerships at Kaizen Softworks

Mathias Talon

Actual farmer

Head of Strategic Partnerships

AI

AI

Using AI to Speed Up Development and Meet Project Deadlines

Published on

·

May 27, 2026

Last updated on

·

May 26, 2026

Time to read

·

12

Mathias Talon, Head of Strategic Partnerships at Kaizen Softworks

Mathias Talon

Head of Strategic Partnerships

AI-powered development tools are no longer curiosities, they’ve become valuable tools in high-stakes software projects. But while the promise of velocity is real, without structure it can also magnify risks. 

In this post, we share a real-world story of how we used AI to meet an impossible software migration deadline. It’s a case study in trade-offs: what happens when you prioritize velocity above all else, and how we later found a more sustainable balance.

Starting Point: Legacy Code, an Impossible Timeline

Picture this: a core business web application, used daily by thousands, built on outdated tech and riddled with security issues. That was our starting point.

This platform was massive: highly modular, deeply entangled, and heavily customized for each client. Migrating it to modern technologies wasn't just a nice-to-have; it was a necessity as technical debt was increasing. Engineers initially estimated 18 months for the job, but leadership unilaterally slashed the deadline to 12 months. No justification. Just pressure.

Our team of four Kaizen developers, working alongside two third-party vendor engineers and our client's internal product team, was asked to make it happen. From the start, it was clear: the numbers didn't add up.

Growing Pressure and Shrinking Options

Progress was steady, but the gap between effort and scope was too big. As weeks passed, pressure grew. Everyone on the ground could see what was coming: we weren't going to make it on time.

The challenge wasn’t just technical. Our client operates in a deeply hierarchical and bureaucratic environment. Technical realities that were obvious to us on the ground often had zero visibility to the top-level decision-makers. 

So we started exploring every possible option: adding more developers to the team, or even shrinking the scope of the migration to launch an MVP faster. 

It was clear something had to give, but getting that message through the layers of management was incredibly difficult. None were greenlit. We needed a new approach, and fast.

The AI Spark: From Skepticism to Experimentation

Around this time, new AI-powered code generation tools were gaining traction, like Windsurf, a fork of Visual Studio Code powered by an autonomous agent named Cascade. These tools could generate code using natural language prompts, and they could do it fast.

We saw an opportunity. Within Kaizen, our Innovation Hub—a dedicated group of engineers from various projects—had already begun experimenting with different code generation tools, including Windsurf. Their mission was to explore cutting-edge tech, drive innovation, and ultimately enhance the value we deliver to our clients.

So, we pitched it to the client. Their first reaction was a hard "no." Security and privacy were their main concerns. They feared code exposure or leaks, and worried their data might be used to train public AI models.

To address this, our team proposed a controlled experiment using an internal, locally hosted AI model, like a "KaizenGPT." This guaranteed no client data would ever leave our servers. It was slower than commercial models, but it built crucial trust.

After seeing positive early results (all on test projects, without using actual client code), our client began to soften. That's when we introduced a more robust setup: Windsurf, paired with paid access to enterprise-grade models. These licensed models offered stronger privacy controls, encryption, and data usage guarantees (backed by certifications) that free versions simply don't provide. That added layer of security and compliance made all the difference. We finally got the green light to start using AI responsibly.

The AI Boost Sprint: Speed at All Costs?

Once the use of AI in their codebase was approved, what followed was a direct, almost chaotic directive: "For two weeks, no meetings, no usual processes, just code! Use AI and push as hard as you can."

At Kaizen, we knew cutting corners on processes wasn’t a sustainable approach. But we also saw the cold, hard truth: at that moment, velocity was the biggest threat to the entire project. We made a deliberate choice to dive into this experiment, fully aware of the risks, because we believed the potential gains were worth exploring.

This sprint became our real-world test: How much acceleration could AI truly bring to our workflow? Could those speed gains actually outweigh serious concerns about code quality, long-term maintainability, and even our team's well-being?

And we did it. Two weeks of intense, AI-driven code generation. We saw incredible speed, yes, but it came at the cost of many things: our team's processes, and code quality, which became poorly defined as everyone adopted AI in their own way, searching for the "optimal" method.

We made tremendous progress, but we also introduced inconsistencies in standards, code style, quality, and even team communication. Many aspects suffered in the race to move forward with AI.

But this wasn't an AI problem; it was a project context problem. The directive was simple: speed, speed, and let's see how far AI can take us to determine if it's worth continuing.

The Search for Balance: Not Everything is Speed

The plan was never to keep running at unsustainable speed. Once the sprint ended, we reviewed the outcomes and pivoted back to our normal processes, with one key difference: AI was now part of them.

So the challenge shifted: how should we actually use AI in a sustainable way?

We started defining standards and best practices. Over time, we discovered how to optimize our approach, bring order to the chaos, and find the right balance between AI assistance and manual work.

AI-powered development tools are no longer curiosities, they’ve become valuable tools in high-stakes software projects. But while the promise of velocity is real, without structure it can also magnify risks. 

In this post, we share a real-world story of how we used AI to meet an impossible software migration deadline. It’s a case study in trade-offs: what happens when you prioritize velocity above all else, and how we later found a more sustainable balance.

Starting Point: Legacy Code, an Impossible Timeline

Picture this: a core business web application, used daily by thousands, built on outdated tech and riddled with security issues. That was our starting point.

This platform was massive: highly modular, deeply entangled, and heavily customized for each client. Migrating it to modern technologies wasn't just a nice-to-have; it was a necessity as technical debt was increasing. Engineers initially estimated 18 months for the job, but leadership unilaterally slashed the deadline to 12 months. No justification. Just pressure.

Our team of four Kaizen developers, working alongside two third-party vendor engineers and our client's internal product team, was asked to make it happen. From the start, it was clear: the numbers didn't add up.

Growing Pressure and Shrinking Options

Progress was steady, but the gap between effort and scope was too big. As weeks passed, pressure grew. Everyone on the ground could see what was coming: we weren't going to make it on time.

The challenge wasn’t just technical. Our client operates in a deeply hierarchical and bureaucratic environment. Technical realities that were obvious to us on the ground often had zero visibility to the top-level decision-makers. 

So we started exploring every possible option: adding more developers to the team, or even shrinking the scope of the migration to launch an MVP faster. 

It was clear something had to give, but getting that message through the layers of management was incredibly difficult. None were greenlit. We needed a new approach, and fast.

The AI Spark: From Skepticism to Experimentation

Around this time, new AI-powered code generation tools were gaining traction, like Windsurf, a fork of Visual Studio Code powered by an autonomous agent named Cascade. These tools could generate code using natural language prompts, and they could do it fast.

We saw an opportunity. Within Kaizen, our Innovation Hub—a dedicated group of engineers from various projects—had already begun experimenting with different code generation tools, including Windsurf. Their mission was to explore cutting-edge tech, drive innovation, and ultimately enhance the value we deliver to our clients.

So, we pitched it to the client. Their first reaction was a hard "no." Security and privacy were their main concerns. They feared code exposure or leaks, and worried their data might be used to train public AI models.

To address this, our team proposed a controlled experiment using an internal, locally hosted AI model, like a "KaizenGPT." This guaranteed no client data would ever leave our servers. It was slower than commercial models, but it built crucial trust.

After seeing positive early results (all on test projects, without using actual client code), our client began to soften. That's when we introduced a more robust setup: Windsurf, paired with paid access to enterprise-grade models. These licensed models offered stronger privacy controls, encryption, and data usage guarantees (backed by certifications) that free versions simply don't provide. That added layer of security and compliance made all the difference. We finally got the green light to start using AI responsibly.

The AI Boost Sprint: Speed at All Costs?

Once the use of AI in their codebase was approved, what followed was a direct, almost chaotic directive: "For two weeks, no meetings, no usual processes, just code! Use AI and push as hard as you can."

At Kaizen, we knew cutting corners on processes wasn’t a sustainable approach. But we also saw the cold, hard truth: at that moment, velocity was the biggest threat to the entire project. We made a deliberate choice to dive into this experiment, fully aware of the risks, because we believed the potential gains were worth exploring.

This sprint became our real-world test: How much acceleration could AI truly bring to our workflow? Could those speed gains actually outweigh serious concerns about code quality, long-term maintainability, and even our team's well-being?

And we did it. Two weeks of intense, AI-driven code generation. We saw incredible speed, yes, but it came at the cost of many things: our team's processes, and code quality, which became poorly defined as everyone adopted AI in their own way, searching for the "optimal" method.

We made tremendous progress, but we also introduced inconsistencies in standards, code style, quality, and even team communication. Many aspects suffered in the race to move forward with AI.

But this wasn't an AI problem; it was a project context problem. The directive was simple: speed, speed, and let's see how far AI can take us to determine if it's worth continuing.

The Search for Balance: Not Everything is Speed

The plan was never to keep running at unsustainable speed. Once the sprint ended, we reviewed the outcomes and pivoted back to our normal processes, with one key difference: AI was now part of them.

So the challenge shifted: how should we actually use AI in a sustainable way?

We started defining standards and best practices. Over time, we discovered how to optimize our approach, bring order to the chaos, and find the right balance between AI assistance and manual work.

Related Articles

View all articles

·

Aug 28, 2026

About Catalyst 26: Partnerships & Ecosystem Conference

Everything to know about Catalyst 26: dates, price, who attends, both keynote recaps, and when the next Catalyst event is.

12 read time

Read more

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.
  • Co-Serve: two companies delivering the same engagement to a client.

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
  • October 27, 2026 - Chicago
  • October 2026 - Los Angeles
  • December 2026 - Singapore

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.

12 read time

Read more

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.

llms.txt