By John Olita, Managing Director – Technology, Landing Point Consulting
As we wrap up Q1 2026, two themes continue to dominate conversations across financial services: private credit and AI. Both are evolving rapidly and increasingly intersecting.
Private credit is scaling at an unprecedented pace, but its data infrastructure hasn’t kept up. Layer AI on top, and the opportunity is massive, but so are the bottlenecks.
While AI adoption is growing, it’s still early. The bigger challenge isn’t AI itself—it’s the foundation beneath it.
Firms are struggling with fragmented and unstructured data, inconsistent workflows, and complex, disconnected systems. Without clean, centralized data and streamlined processes, even the most advanced AI tools fall short.
Put simply: the real bottleneck in private credit isn’t AI capability; it’s data readiness.
As public and private markets continue to converge, the infrastructure supporting private credit, including data models, governance, and the operational support on the back end is still playing catch-up.
A clear signal of where the market is heading is the ICE–Apollo partnership, aimed at building a scalable data ecosystem for an asset class projected to reach tens of trillions in size.
We’re seeing that both smaller and emerging firms ($1B–$10B AUM) and mid-sized firms ($10B–$50B AUM) are well-positioned, but for different reasons. Smaller firms have strong potential to scale but are often constrained by limited budgets for hiring, while mid-sized firms strike a balance. They are large enough to invest in talent and technology, yet still agile enough to implement modern data and AI strategies effectively.
AI Adoption: It’s Real… But Early
Yes, firms are incorporating AI into daily functions. At present, it is primarily helping lenders source and analyze opportunities, rather than fundamentally transforming operational workflows.
In most cases, it remains concentrated in basic areas such as front office deal sourcing, document processing, reducing manual workflows like emailing, and extracting data from PDFs or flat files stored in SharePoint repositories.
Smaller and mid-sized firms are often willing to test new tools aggressively, while the big players move a bit more cautiously. Right now, AI is augmenting workflows rather than replacing roles.
Meanwhile, there are still gaps in:
- Underwriting workflows
- Administrative agent processes
- Loan monitoring systems
- Portfolio reporting
The point: AI is a helpful tool, but it can’t solve problems if the underlying systems and data aren’t ready. Without that foundation, adoption alone won’t move the needle.
The Real Bottleneck: Data & Infrastructure Maturity
Across the industry, the challenge is consistent: data readiness.
Common challenges I hear most often from clients:
- Unstructured data in PDFs, documents, or SharePoint
- Inconsistent Excel models across teams
- Difficulty building unified data structures across investment strategies
- Limited cross-system reporting
In short, many firms are still solving foundational “data plumbing” problems.
Over the past 12–24 months, most have begun investing in modern data platforms and data warehouses to centralize and automate workflows, but maturity levels vary widely.
- Emerging managers: constrained by lean teams, where talent is the bottleneck
- Mid-sized firms: process standardization becomes the challenge
- Large platforms: architectural complexity is the primary hurdle
What’s consistent across all: the need for a unified data model.
Hybrid Talent: Where Demand is Highest
The professionals most in demand aren’t simply AI engineers, they’re hybrid specialists who can bridge the gap between complex credit operations and modern technology platforms.
Think of the people who are turning day-to-day challenges into scalable solutions:
- Credit operations professionals who are moving into data engineering, turning operational know-how into scalable systems
- Portfolio monitoring specialists stepping into analytics roles, transforming reporting and insights into actionable intelligence
- Loan operations experts building workflow automation tools, making processes faster, smarter, and more reliable
What makes these professionals so valuable is their dual perspective. They understand the problem at hand because they’ve lived it, and they know how to implement the technical solutions to solve it. The combination of operational expertise + technological capability is exactly what firms need to modernize data infrastructure and unlock the full potential of AI.
Roles Driving Modernization
Some of the most in-demand core IT roles I’m seeing:
- Data Platform Engineers to build enterprise data platforms, unified data models, and scalable reporting infrastructure
- Data Operations & Ownership Specialists to create and manage master data systems across clients, securities, references, and legal entities
- Workflow Automation Engineers to focus on automating underwriting, reporting, and document processing
Looking Ahead
The recent launch of ICE Private Credit Intelligence, with Apollo Global Management as an anchor partner, underscores the direction of the private credit industry. This platform provides:
- Secure, permissioned deal-level data sharing
- Standardized reference data sets
- Scalable document ingestion and term extraction
- Performance analytics and pricing insights
This kind of infrastructure shows us where private credit is headed. But to get there, firms need the right talent to modernize their data platforms and workflows.
Data First, AI Follows
The promise of AI in private credit is real, but its success will remain constrained until firms address the underlying data and operational challenges. Clean, centralized data, standardized workflows, and scalable technology platforms aren’t optional; they’re the foundation on which AI can deliver measurable impact.
Firms that prioritize this modernization now by investing in hybrid talent, data infrastructure, and workflow automation won’t just keep pace; they’ll gain a competitive edge. AI will amplify their capabilities, but only if the plumbing is in place. The future of private credit belongs to those who see beyond the AI headlines and focus first on building systems that truly support smarter, faster, and more insightful decision-making.
About John Olita
John Olita is a Managing Director on the Landing Point Consulting team, where he leads technology and contract solutions across the Tri-State area and broader Northeast. With more than a decade of experience supporting financial services and enterprise technology clients, John delivers contract and contract-to-hire solutions for both large-scale transformation projects and one-off needs.
Notable searches include infrastructure, data, and cybersecurity specialists for leading investment banks and enterprise investment managers. His consultative approach balances speed and precision, delivering talent solutions across technology modernization, automation, and cybersecurity.
A graduate of Fordham University, he previously founded a basketball analytics startup used by dozens of colleges and NBA teams. An avid golfer and longtime New York sports fan, he combines a competitive drive with a collaborative approach to business partnerships.