Unlocking real value: How portfolio companies are driving measurable impact with GenAI

Where GenAI is creating measurable impact

The most immediate and measurable gains from GenAI are emerging in middle‑ and back‑office functions where processes are repeatable, data‑rich, and outcomes can be clearly tracked.

Finance teams are using GenAI to accelerate close processes, generate variance commentary, and surface insights from large volumes of financial data, which frees up capacity while improving the quality and timeliness of reporting. Procurement functions are deploying GenAI to review contracts, flag renewal risks, and identify compliance issues, enabling more proactive vendor management. IT organizations are using conversational agents and automated triage to deflect routine service requests and improve response times.

Together, these applications deliver tangible operational improvements such as lower unit costs, faster process cycles, and more productive teams. For investors and operating teams alike, they represent scalable margin‑expansion opportunities and a practical starting point for GenAI adoption, where value can be demonstrated quickly and replicated across the portfolio.

When deployed thoughtfully and supported by appropriate guardrails, GenAI is beginning to reshape how portfolio companies engage customers.

In customer service environments, conversational agents are handling high‑volume, low‑complexity interactions around the clock, improving responsiveness while reducing demand on live agents. For example, Teranet has implemented a customer service chatbot that operates 24/7 and is achieving over 90% containment, significantly reducing call volumes while maintaining service quality. At the same time, GenAI‑enabled tools are supporting agents with real‑time guidance, faster access to knowledge, and targeted training, leading to more consistent resolution quality and improved customer outcomes.

Sales and marketing teams are also applying GenAI to qualify leads, personalize outreach, and coach performance. While revenue impact can be more difficult to isolate than cost savings, organizations that tie these initiatives directly to commercial metrics, such as conversion rates, sales cycle times, and customer retention, are better positioned to translate experimentation into sustainable growth. The strongest results are emerging where revenue‑side GenAI initiatives are measured with the same discipline applied to any other growth investment.

For private capital, value creation and value protection are closely linked. As portfolio companies pursue growth and efficiency through GenAI, effective risk management becomes a critical enabler of sustainable value.

Rather than relying on individual portfolio companies to address cybersecurity in isolation, leading sponsors are implementing standardized, portfolio‑wide cyber programs. For example, TorQuest has established a centralized cybersecurity approach supported by two core partners: a cyber insurance broker and a validated risk‑assessment provider. Together, they enable semi‑annual, validated assessments of portfolio companies against required security controls, helping ensure insurability while creating a consistent baseline for cyber maturity across the portfolio. Importantly, this approach allows cyber risk to be discussed in financial terms. By quantifying exposure and linking security gaps to potential financial impact, boards and management teams can make more informed decisions about where to invest, how to prioritize remediation efforts, and how to protect enterprise value. This shifts cybersecurity from a technical discussion to a value‑focused governance conversation.

In parallel, GenAI is supporting compliance monitoring, document review, and audit preparation in regulated and data-intensive environments – ultimately reducing manual effort while improving consistency and oversight. Combined with clear governance and responsible AI practices, these measures create the confidence required to scale GenAI more decisively across the portfolio.

Beyond the portfolio company level, GenAI is reshaping how investment teams operate – it’s changing the speed and depth at which insight can be generated across the deal lifecycle.

Deal teams are using GenAI to synthesize large volumes of market research, analyze historical performance data, and accelerate industry assessments, compressing timelines from weeks to days. In practice, this shift can be even more pronounced. TorQuest is experimenting with GenAI-assisted industry research. Work that previously required roughly a week of associate effort can, in some cases, be completed in as little as 20 minutes by combining internal investment theses with external data, using a safe, dedicated AI platform and curated prompts. This enables teams to develop an initial industry view much earlier and focus more time on deeper, higher-value analysis.

During post‑merger integration, GenAI can help map processes, identify synergies, and standardize reporting across acquired entities, reducing friction and improving transparency across complex organizations.

For operating partners, this acceleration translates into faster insight, clearer prioritization, and earlier intervention which matters in increasingly competitive deal environments. As these capabilities mature, GenAI is becoming less about efficiency gains in isolation and more about increasing the velocity and quality of decision‑making from diligence through integration and, ultimately, exit.

As GenAI adoption matures, many organizations are shifting from early experimentation toward broader, enterprise‑level deployment. The ability to scale successfully often depends on a small number of foundational disciplines.

Organizations making this transition start with a clear understanding of why a GenAI initiative matters and who is accountable for delivering outcomes. Even concise business cases that define expected benefits, costs, and success measures help create focus and reinforce ownership. Assigning a single accountable leader further supports momentum, adoption, and follow‑through.

How initiatives are funded also matters. Leading organizations allocate capital for innovation deliberately, applying appropriate governance and thresholds for early proofs of concept versus scaled deployments. This approach supports continued experimentation while maintaining financial discipline and aligning investment decisions to risk and return.

GenAI delivers the greatest impact when it is embedded directly into the value creation plan rather than pursued as a standalone initiative.

This starts with clearly linking GenAI initiatives to the core value drivers of the business, whether through margin expansion enabled by automation, revenue growth driven by improved customer engagement, or increased resilience through stronger risk management. Operating partners play a central role in translating these objectives into prioritized use cases and sequencing them alongside broader transformation efforts.

Long‑term impact is reinforced by the capabilities that enable scale. Data quality, integration, and governance shape whether GenAI produces actionable insight or operational noise. While these elements often receive less attention than front‑end use cases, they are essential to scaling GenAI consistently and sustaining value creation throughout the investment lifecycle.

Sustained GenAI impact depends as much on people and processes as on technology. Organizations seeing meaningful results are investing in capability building, clear governance, and deliberate change management.

Targeted training helps employees understand not only how to use GenAI tools, but how to apply them responsibly and effectively. Clear policies around data use, privacy, and intellectual property reduce uncertainty, while human oversight remains essential in higher‑risk or customer‑facing applications.

Together, these measures build trust across the organization. That trust enables teams to adopt GenAI with greater confidence, move more decisively, and scale its use in ways that support long‑term value creation rather than isolated experimentation.