Private equity’s tech mandate is clear:

Run faster, build smarter, and choose AI partners carefully

By Phil Westcott, Filament Syfter

For all the commentary about “digital transformation” in private markets, The Drawdown’s recent Future of Technology Adoption survey (with IQ-EQ) lands on a more grounded reality: firms want better outcomes from technology, but execution depends on making disciplined choices about tooling, partners, and what to build internally. 

Three findings in particular stood out to me — and each maps directly to what we see private equity firms wrestling with.

1) The #1 motivation is operational efficiency — and that requires best-in-class tooling, not heroics

When asked about motivations for adopting new technology solutions, respondents rated improving operational efficiency highest at 4.3/5 (1 = not a priority; 5 = top priority). The survey also notes that approximately 90% considered operational efficiency either a top or high priority. 

That’s a meaningful signal. It suggests most firms aren’t buying technology to “innovate” in the abstract — they’re trying to remove drag: fewer manual steps, fewer duplicated workflows, and less time spent wrangling data before anyone can act on it.

This is where best-in-class platforms matter. Efficiency gains don’t come from adding another point solution that creates more swivel-chair work. They come from tightening the operating loop: getting to a single, trusted view of the market, automating repeatable workflows, and enabling teams to move faster with confidence. That is the reason Filament Syfter exists: to help firms operationalise intelligence — and to turn fragmented internal and external information into a live, usable system that makes the investment team faster.

2) Spend is shifting into AI — but firms should be cautious about “AI-first” vendors without domain expertise

Looking ahead, the survey shows that tech spend priorities over the next two years tilt toward AI categories (Generative AI: 3.7/5 and Agentic AI / workflow automation: 3.6/5). These are the two highest-scoring areas in the chart — ahead of data platform (3.2), workflow/process automation (3.3), and other familiar categories. 
The opportunity is obvious: AI can reduce research time, surface relevant signals faster, and automate portions of routine work.

The risk is equally obvious: private markets workflows are not generic. If a provider doesn’t understand how GPs actually source, screen, diligence, and monitor — and how those workflows tie back to data quality, governance, and repeatability — “AI features” can quickly become expensive distractions.

My recommendation is simple: as AI budgets grow, vet partners on domain depth and implementation maturity, not demos. In practice, that means asking:

• What data foundations does the AI rely on, and how is that data connected and governed?
• Can the system be tailored to your firm’s investment criteria and operating model?
• How quickly can it be deployed into real workflows (not pilots)?
• What partner can provide my firm with a path to measurable productivity, not just output generation?

3) Many firms are still building (or keeping) key layers in-house — which raises the bar on integration and “play nicely” tooling

The survey also sheds light on where solutions are delivered — in-house, by fund administrators, or by external tech providers. Even in AI categories — where outsourcing dominates — there is still a non-trivial “internal build” footprint, with Generative AI accounting for 22.6% in-house and agentic AI/workflow automation 23.8% in-house.

While the desire to build proprietarily is a well-founded one, the practical implication is that most firms are operating a hybrid world where internal systems, existing workflows, fund admins, and external platforms all need to coexist. That makes integration the real battleground.

In that environment, the best technology partners don’t require you to rip-and-replace everything. They integrate cleanly into the firm’s existing ecosystem and reduce operational load — which is exactly what respondents say they want most.

Closing thought

The message in the survey report data is consistent: private markets firms want efficiency (4.3/5), they’re prioritising AI spend (3.7 and 3.6), and they’re still keeping meaningful parts of the stack in-house — especially where workflows are embedded and hard to unwind. 

If you’re navigating these trade-offs now, Filament Syfter can help you modernise without disruption: unify your intelligence layer, automate the repeatable work, and deploy AI in a way that’s grounded in private markets reality — not generic tooling.

Find out more about how to build an effective data engine that ensures you never miss a deal: Get your demo today