The question for PE firms is no longer whether to adopt generative AI, but how. AI can make portfolio companies more efficient and sharpen their competitive edge on much shorter timelines than ever possible before.
For many portcos, building AI solutions in-house might feel like the logical choice. It promises full control, customization, and can even serve as an exciting initiative to wow investors and prospective customers.
The reality is, taking a do-it-yourself approach to building with AI is a lot harder than it looks. The real challenge is guiding portfolio companies towards building the right AI solutions that address immediate needs within a clearly defined scope, adapt over time, and deliver ROI within the holding period. If your portcos are considering or already investing in in-house AI development, this paper is for you––and for them.
The Risks of In-House AI Solution Development
Consider this scenario: A healthcare-focused PE-funded company attempts to build an in-house GenAI solution using an open-source model. The project is delayed six months due to challenges hiring specialized AI talent. Hardware and software costs quickly spiral past $600,000, far exceeding initial estimates. Despite the effort, the solution fails in beta testing because employees find it adds unnecessary steps and delays to their workflows. After spending nearly $1 million, the company abandons the project entirely.
Developing AI solutions in-house, whether by integrating APIs from providers like OpenAI (GPT), Anthropic (Claude), or Google (Gemini), or customizing open-source models like Llama, demands far more than technical expertise. It requires substantial investments in time, specialized talent, and infrastructure—resources that portfolio companies typically don’t have in surplus.
Private equity timelines are shorter than most, and delays in the software development lifecycle have a ripple effect, leading to missed EBITDA targets and other operational milestones. The ongoing costs of maintaining and updating in-house solutions, paired with the challenges of recruiting and retaining specialized talent, often catch companies off guard, straining both budgets and timelines.
In-house efforts also come with trade-offs. Committing to a specific model locks portcos into that choice, limiting flexibility as new, better options emerge. Plus, APIs bring the risk of downtime. If services like GPT go offline, workflows relying on those APIs can grind to a halt, and there is nothing the portco can do about it. While open-source models avoid this dependency, they come with their own demands, including dedicated infrastructure for development, testing, and deployment, as well as skilled personnel to manage, fine-tune, and roll them out.
Then there’s the integrations. Integrating AI with existing systems is rarely straightforward. Building custom connectors to link AI into workflows can range from simple to highly complex, depending on the systems in place.
It’s easy to underestimate the work and risks involved in building AI solutions from scratch. Technology leaders see a blank canvas and tons of potential, but they need to keep in mind the financial and business goals of the organization when kicking off any new project. This framework is designed to help evaluate the trade-offs between building in-house and partnering with established platforms, comparing cost, scalability, maintenance, and risk to guide informed decision-making.
The Comparison Framework: Two Paths to AI Building
Use this checklist to compare the realities of developing your own GenAI solution versus building it on a proven platform like Krista.
Consideration | From-Scratch Approach | Building AI with Krista |
|---|---|---|
Cost | – High upfront costs for hardware, software, and development. – Hidden costs for maintenance, updates, and retaining scarce AI talent. | – Transparent pricing with predictable total cost of ownership. – No need to hire or train specialized AI talent. |
Speed to Deployment | – 6–12+ months, delays from talent shortages, turnover, and custom integrations. | – Deploy AI-led workflows in weeks with minimal disruption. |
Maintenance | – Requires ongoing reengineering to adopt new AI model capabilities. – Maintenance burden grows as portfolio needs evolve. | – Continuous compatibility updates handled automatically. – Hands-free upkeep ensures new projects don’t add to the maintenance burden. |
Scalability Across Portcos | – Custom solutions and training for each company. – Risk of “innovation lock-in”, where existing solutions can’t adapt to changing needs. | – Flexible enough to support diverse portfolio companies without added technical complexity. – Evolves with business needs without technical overhead. |
Compliance & Privacy | – Maintaining compliance for handling sensitive data across jurisdictions is costly. | – Enterprise-grade privacy and governance tools built in. |
Future-Proofing | – Locked into one model, making adopting newer, more powerful options difficult. | – Model-agnostic, with built-in fallback options for uptime and access to evolving AI models. |
Why This Framework Matters
AI is already changing how many businesses operate, but most are still unsure where to start. Without a clear strategy, companies risk disruptions, employee confusion, and runaway costs. For private equity backed companies, the stakes are too high for a wait-and-see approach. Delays in adopting AI leave companies vulnerable to operational inefficiencies and missed opportunities to secure a competitive edge.
But AI must be adopted in a nondisruptive way. It has to fit seamlessly into existing systems and workflows and be accessible to a range of users, regardless of their technical skillsets. The first step to achieving this type of outcome is to recognize that LLMs are often just the “last mile” in any process. Without the right infrastructure, tools, and strategy, even the best AI solutions can stumble and fall short of the objective.
Krista makes AI integration straightforward. Its platform helps PE firms create nondisruptive AI-powered automations faster—often in weeks instead of the months it takes with a traditional software development process. By using Krista, you not only get faster results but also avoid many of the risks that come with building solutions in-house.
Actionable Takeaways
Assess your AI strategy: Use the framework above to understand whether building or partnering aligns better with your portfolio goals.
Focus on outcomes: AI should drive efficiency and profitability, not drain resources on building the infrastructure to support it.
Consider Krista: A platform that handles complexity for you, so your team can focus on making the most of its opportunities for innovation instead of maintaining systems.
Take the Next Step
Krista team members are available to discuss how a platform-based approach could fit your portfolio’s AI initiatives and answer any questions you have.
You can also try Krista for free on our website! Ask Krista.
