Across every private equity PortCo, value-creation plans revolve around the same pillars: Expanding revenue, reducing churn, increasing efficiency, and compressing time-to-value.

Among all the levers PE firms pull, there’s one that should be able to move EBITDA faster than almost anything else – Integration.

With AI coding becoming more mature, AI-led integration is expected to become the iPaaS alternative, finally delivering speed and predictability to these projects that have long been manual and overly complicated.

How Integrations Unlock EBITDA

Though integration is a technical topic, it determines how fast a PortCo can recognize revenue, win deals, retain accounts, and enter new markets.

1. Integrations expand partner ecosystems, leading to bigger TAM

Being integrated into established ecosystems such as Salesforce, Epic, or NetSuite immediately opens a new customer acquisition channel. These ecosystems act as trusted marketplaces, where customers actively search for vetted, compatible solutions. Across many mid-market SaaS companies, ecosystem integrations consistently lead to higher-quality inbound, shorter sales cycles, and access to verticals that would otherwise require significant GTM investment.

According to salesforce, 84% of sales pros say partner selling has a bigger impact on revenue than a year ago.

2. Integrations remove onboarding friction and accelerate revenue recognition

It’s common to see contracts signed but revenue stuck in implementation, customers delaying rollout because “we need X integration first,” and multi-stakeholder teams refusing to adopt a tool that forces duplicate workflows. This is especially common for mid-market SaaS selling into ERP-heavy industries. That delay pushes out go-live dates and directly postpones revenue recognition.

Across many PortCos, this is one of the most consistent yet least visible killers of value creation. Deals are won, but revenue doesn’t hit the P&L for months because teams are waiting on integration work that should have been done upfront.

**3. Integrations Scale With Product **

As a product grows, it inevitably enters more of the customer’s workflow. Each additional module (analytics, scheduling, billing, reporting, onboarding, compliance) creates new expectations for data exchange with existing systems. A wider product surface means more touchpoints where the customer needs information to flow in and out.

Without integrations, product expansion slows growth instead of accelerating it. With integrations, each new capability compounds value by connecting to the systems customers already rely on.

**How are iPaaS and SIs approaching integrations? **

Let’s look at a real example.

As a custom integration partner for private equity, we consistently hear PEs say “Almost all of our companies struggle with Salesforce data quality.” “We don’t know who’s the same customer.” The reason for this is that data from CRMs and ERPs is often fragmented, duplicated, and inconsistent, causing reporting failures, delays, and integration breakdowns.

Fixing this with iPaaS or a traditional SI typically requires:

$100k–300k in upfront spend

$40k–100k in annual licenses and maintenance

3–6 months to achieve a trusted unified customer view

All while poor data quality eats up 15–25% of revenue in hidden friction and bad decisions.

iPaaS can’t handle bespoke, real-world logic

iPaaS tools frequently struggle with real-world, bespoke integration logic, especially in healthcare, ERP-heavy industries, logistics, and finance where schemas are deeply customized, non-standard, or inconsistent across customers.

While iPaaS provide connectors and visual mapping, they cannot resolve domain-specific logic, data-quality issues, or schema drift on their own. And in many cases, iPaaS hides complexity rather than reducing it, making debugging harder because teams must troubleshoot both the business logic and the platform’s abstraction layer.

This is especially true when dealing with legacy systems, CSV/SFTP batch workflows, or heavily customized ERPs.

As a result, engineering teams often spend significant manual effort stitching together workarounds, maintaining brittle mappings, and compensating for the limitations of the iPaaS rather than benefiting from true automation.

Manual unwrap from SIs can’t keep up with PE’s speed

When SIs get involved, that means engineers need to unwrap systems manually.

It is not uncommon for even a single substantial integration to take many months, incur high implementation costs, and require significant engineering effort beyond the out-of-the-box integration scope (custom mappings, data migration, ad-hoc scripts, documentation catch-up, etc.).

Such projects also frequently carry vendor lock-in risk, due to proprietary interfaces and deep dependencies, and may suffer from poor or missing documentation, requiring reverse-engineering and tribal knowledge retention. For PE-owned (or PE-backed) companies where speed-to-value and rapid deployment matter, this can significantly erode the value creation timeline.

**How SDLC-driven AI-coding Becomes the Fast and Reliable iPaaS Alternative **

AI coding has long been viewed as a black box: Prompt in, code out. Without the right framework, it creates operational risks. And every integration faces infosec and legal hurdles, issues generic LLM coding tools like Cursor or Claude cannot address.

The real solution isn’t “the newest model”. It’s AI used inside a disciplined software development lifecycle (SDLC) that harnesses both AI’s execution speed and human oversight and experience. This is what turns integrations from bespoke projects into repeatable delivery.

As a custom integration partner for private equity, Isoform has consistently delivered outcomes projects 70% faster, saving on average $150k per project, producing production-ready code, without operational disruption

This is made possible by our self-built SDLC-driven AI coding tool, Yansu, coupled with expert oversight from our team. The system is built on deep LLM expertise (Isoform’s founder built one of the most active open-source LLM tools) and best-practice from software engineering.

What SDLC-driven AI-Integeration Enables

1. Timelines compress from quarters to weeks

Across recent projects, integration with timelines of quarters consistently dropped to 4–6 weeks, with first working demos delivered in the first 5 days.

Take Rev.io’s case as an example, 70%+ of the integration logic was auto-generated by AI. That leads to their 4-month cloud integration project being completed in just 3 weeks.

2. Code becomes fully transparent and maintainable

Instead of a vendor-owned system or opaque iPaaS logic, SDLC-driven AI-coding is able to get connected to the company’s tribal knowledge, generate code that fits the logic, connect the code and all documentation to the project repo, so nothing is locked behind licenses.

We took it one step further. Through Yansu, our own SDLC-driven AI coding tool, we simulate all scenarios before merging to the main codebase so that risks and edge cases are visible. Before the deployment, we also have a strict human checklist that our experts have to manually go through. This is how we enable production-ready outcomes.

For Operating Partners, this model changes the calculus: First live demo can be done in 5 days, full integration projects become a 30–100 day win.

Most importantly, every successful build makes the next one 40–60% faster. This is how value compounds across 20–40 PortCos. Integrations stop being one-off projects and become a portfolio-wide flywheel.

Closing

AI is already reshaping the integration landscape, not through hype, but through disciplined, auditable engineering practices that finally remove the friction, cost, and bottlenecks PE teams have struggled with for years.

Integrations will always be essential. Now with AI-led integrations, they can also be fast, repeatable, and economically transformative.

If you’d like benchmarks or want to explore a 5-day working demo inside of your initiatives, we’re happy to share more.