Private equity (PE) firms are increasingly using artificial intelligence (AI) to achieve over 30% ROI, moving from experimental to strategic implementations. Successful AI applications in financial services include enhanced customer service, improved market forecasting, and streamlined underwriting processes. Comprehensive AI technology due diligence is essential for identifying value-creating opportunities, while addressing challenges like data quality and talent acquisition is crucial for scaling AI solutions. Sustainable AI capabilities are necessary for long-term success, ensuring ongoing value creation.
Private equity (PE) firms face constant pressure to outperform benchmarks. Strategic artificial intelligence (AI) implementations are proving to be more than just hype; they are tangible drivers of enhanced performance across portfolio companies. Indeed, PE firms that strategically deploy AI are realising ROIs exceeding 30%, a considerable leap ahead of those still experimenting [1]. This clear advantage is establishing new performance standards in the PE sector, making methodical AI integration no longer optional, but essential.
Moving beyond abstract discussions, this article will explore concrete strategies and real-world examples that illustrate how leading firms are securing these impressive returns through strategic AI deployment. It’s time to focus on the practical applications that are already delivering significant financial outcomes.
Strategic AI: From Experimentation to Core Operations
The private equity landscape is undergoing a profound shift. Firms are evolving from isolated AI experiments to integrated, strategic deployments designed for measurable impact. This evolution is crucial because, despite considerable investment in the technology, many AI projects still fail to deliver the anticipated value [1].
The key differentiator between success and failure lies in aligning AI initiatives with fundamental business objectives and building skilled teams capable of effective execution.
Industry experts recommend prioritising strategic AI implementations that target quantifiable outcomes, such as boosting customer lifetime value and improving operational productivity [1]. This strategic pivot requires identifying high-impact use cases within portfolios that directly enhance financial performance.
It is about concentrating on practical applications that streamline operations, reduce costs, and increase revenue, moving beyond a general enthusiasm for AI towards targeted, impactful deployments. For financial services businesses, this means identifying AI applications that specifically address industry challenges and opportunities, and ensuring AI investments are not only innovative but also deliver robust economic returns.
AI Technology Due Diligence: A Critical Pre-Investment Step
Comprehensive AI Technology Due Diligence is now a prerequisite for PE firms aiming to pinpoint portfolio companies ripe for AI-driven value creation. This process goes beyond basic technical checks, encompassing rigorous assessments of data quality, governance frameworks, and regulatory compliance.
In a sector where regulatory uncertainty is a significant concern – with 77% of financial services leaders expressing apprehension about its impact on AI investment decisions [2] – thorough due diligence is not merely a best practice; it is essential risk management.
Specialist technology advisors like Diligize offer structured frameworks for AI due diligence that address both technical capabilities and regulatory compliance. These frameworks ensure a comprehensive evaluation while streamlining the process, which is particularly valuable for firms without extensive in-house AI expertise.
For smaller financial institutions, or SMEs, this due diligence phase is equally critical. Cost-effective due diligence for SMEs can begin by leveraging internal expertise to assess readily available data sources and using standardised frameworks to evaluate AI opportunities. This pragmatic approach ensures that even resource-limited firms can make informed AI investment decisions, mitigating risks from the outset.
Diligize employs a robust AI technology due diligence framework, encompassing technical assessments of AI systems, evaluations of data quality and governance, model performance validation, operational integration analysis, and reviews of regulatory compliance and ethical considerations. This systematic approach provides PE firms with a clear understanding of a target company’s AI capabilities, risks, and opportunities, facilitating informed investment decisions.
Furthermore, regulatory sandboxes are increasingly important tools used by financial services regulators to foster AI innovation while maintaining compliance. These controlled environments allow companies to test new AI technologies under regulatory supervision.
For example, the SEC has been actively engaged in discussions around AI in finance, as highlighted in recent roundtables focusing on AI’s use in back-office efficiencies and financial operations. These initiatives, alongside frameworks like the EU AI Act, aim to balance innovation with essential consumer protection and regulatory oversight [3].
"AI hype is no longer accepted currency. ROI in dollars only, please." - Andrei Papancea
Real-World ROI: Tangible Case Studies in Financial Services
Examining successful AI deployments reveals actionable insights and repeatable strategies. Consider these examples from the financial services sector:
- Elevated Customer Service: One financial services provider implemented an AI-powered customer service solution, achieving an average response time of just 15 seconds and boosting customer satisfaction by 86% [4]. This demonstrates AI’s transformative capacity to enhance customer interactions and service delivery.
- Optimised Market Forecasting: Global Investment Partners integrated AI predictive models, achieving a 40% improvement in market forecast accuracy [5, 1]. The firm began with structured financial data before incorporating alternative data sources. Overcoming initial data quality challenges, they established a dedicated data governance team and implemented automated validation protocols. This enhanced forecasting led to more informed investment strategies and increased client satisfaction, directly contributing to a 15% revenue growth within the first year of full implementation.
- Streamlined Underwriting: SecureLife Insurers implemented AI in their underwriting process, enhancing risk assessment accuracy by 40% and reducing underwriting time by 50% [5, 2]. The projected annual savings from this efficiency and accuracy is estimated at $2 million, representing a 30% ROI.
These case studies illustrate that targeted AI implementations in areas like customer service, market analysis, and operational efficiency can deliver substantial ROI, frequently exceeding the 30% benchmark when strategically applied and scaled.
AI-driven anomaly detection systems, for instance, are becoming increasingly sophisticated, significantly aiding in fraud prevention and transaction monitoring [6]. In fact, the financial services sector, especially SMEs, are increasingly adopting AI-driven fraud prevention tools to combat deepfake fraud attempts.
Data from Signicat recently indicated a staggering 2,137% surge in AI-driven fraud attempts over the past three years [7].
Navigating Implementation Hurdles to Maximise AI Returns
Despite the clear potential, many PE portfolio companies encounter obstacles when scaling AI solutions beyond initial proof of concept. A notable statistic indicates that only 26% of companies successfully move past the PoC stage to generate tangible value from AI [8]. Common challenges include:
- Data quality issues: Ensuring data is accurate, accessible, and well-governed is fundamental.
- Talent acquisition difficulties: Building internal AI capabilities is crucial for sustained success.
- Legacy system integration complexities: A phased approach to integrating AI with legacy systems can mitigate disruption and ensure smoother workflows.
- Data governance weaknesses: Addressing data loss concerns is paramount, with 84% of BFSI leaders expressing worry about data loss due to AI demands [9].
To overcome these implementation hurdles and unlock AI’s full ROI potential, particularly for SMEs in financial services, several key strategies are essential. SMEs should prioritise data cleansing for high-value datasets and explore cloud-based data management solutions suitable for smaller budgets.
Budget-friendly talent strategies for SMEs include upskilling existing staff and exploring partnerships to access external AI expertise without heavy upfront costs. Furthermore, a phased approach to integrating AI with legacy systems can mitigate disruption and promote seamless operational integration, avoiding fragmented solutions.
To address the critical shortage of AI talent, forward-thinking PE firms are implementing multi-pronged approaches: establishing partnerships with universities, creating internal AI academies, and developing mentorship programmes that pair technical experts with business teams.
These initiatives build sustainable AI capabilities while reducing dependency on the competitive external talent market. Lloyds Bank, for example, is training 300 senior staff in AI and enrolling 3,000 employees in AI courses, demonstrating a commitment to internal capability building [10].
"I believe the consequences of AI are as profound as what occurred in 1880 when Thomas Edison patented the electric light bulb." - Steve Schwarzman, Chairman, CEO, and Co-Founder of Blackstone
Measuring and Validating AI Investment Returns
Establishing robust frameworks to measure and validate AI ROI is paramount for PE firms. This involves defining relevant Key Performance Indicators (KPIs), implementing monitoring systems, and conducting regular performance reviews.
European investors are increasingly expecting demonstrable financial returns from AI investments, emphasising efficiency gains over mere hardware investments [11].
A practical framework for measuring AI ROI in financial services includes:
- Baseline Establishment: Document current performance metrics before AI implementation.
- Cost Calculation: Comprehensively account for implementation costs, including technology, talent, and change management.
- Value Identification: Categorise benefits into direct cost savings, revenue enhancement, and risk reduction.
- Timeframe Definition: Establish short-term (3-6 months), medium-term (6-12 months), and long-term (12+ months) measurement periods.
- Metric Selection: Choose 3-5 primary KPIs aligned with strategic objectives.
- Regular Review Cycles: Implement quarterly assessment meetings with stakeholders.
- Refinement Process: Establish protocols for adjusting AI systems based on performance data.
For different industry verticals, KPIs will vary. In manufacturing, for example, effective KPIs include Overall Equipment Effectiveness (OEE), cycle time reduction, and defect rates.
In healthcare, relevant metrics range from patient throughput and diagnostic accuracy to patient satisfaction scores and cost per patient. Aligning KPIs with industry-specific goals ensures accurate ROI validation.
AI-Driven Value Across Financial Services
The ROI from AI implementations varies across industry verticals within PE portfolios. Understanding industry-specific AI use cases is crucial for PE firms to strategically prioritise implementations that align with each portfolio company’s unique market position and operational context.
It is projected that AI ventures will influence a significant majority of new portfolio deals, underscoring AI’s pervasive impact across the investment landscape [12].
Within financial services, AI applications in risk assessment, fraud detection, and customer service consistently demonstrate high ROI potential. AI is also playing an increasing role in compliance automation, streamlining processes and reducing verification times.
Platforms are automating key compliance checks by as much as 80% and processing millions of documents annually, showcasing AI’s transformative impact on regulatory processes [13]. Recognising these industry-specific nuances enables PE firms to tailor their AI strategies for maximum portfolio-wide returns.
Building Sustainable AI Capabilities for Long-Term Success
Sustaining 30%+ ROI with AI necessitates building enduring AI capabilities within portfolio companies. This involves strategic investments in talent development, robust data governance frameworks, and fostering a culture of AI-driven innovation.
Effective upskilling programmes are crucial for SMEs to develop internal AI expertise. Apprenticeship programmes can also facilitate the transfer of critical skills and knowledge, ensuring expertise is retained within the organisation.
For PE firms, this means encouraging portfolio companies to invest in continuous AI education and create environments that nurture AI innovation. Building internal AI expertise ensures that portfolio companies not only achieve immediate returns but also sustain and enhance these returns over the long term, securing lasting value creation.
AI is not merely a technological advancement; it is a strategic imperative for PE firms aiming to maximise portfolio company performance and deliver exceptional returns. By transitioning to strategic AI implementation, conducting thorough due diligence, addressing implementation challenges, and fostering sustainable AI capabilities, PE firms can confidently unlock AI’s transformative potential and achieve—and surpass—the 30%+ ROI benchmark.
For financial services firms seeking to navigate this evolving landscape and capitalise on AI’s promise, partnering with expert technology advisors like Diligize can provide the necessary guidance and support to ensure strategic AI investments translate into measurable, impactful results.
How might your portfolio companies benefit from strategic AI implementation? Consider conducting an AI readiness assessment with Diligize to identify your highest-value opportunities and develop a roadmap for achieving 30%+ ROI through targeted AI initiatives.
Our Opinion
At Diligize, we see the integration of strategic artificial intelligence within private equity portfolios not just as a trend, but as a fundamental shift in how value is created and maximised. The pursuit of a 30% or greater return on investment through AI is not aspirational; it is an achievable benchmark for firms that adopt a focused and pragmatic approach. Our experience consistently demonstrates that moving beyond isolated AI experiments to embedding AI into core operations is the linchpin for unlocking substantial financial benefits. For private equity today, strategic AI deployment is no longer optional – it is the essential evolution for sustained outperformance and securing a decisive advantage.
To truly capitalise on the transformative power of AI and realise these significant returns, rigorous AI technology due diligence is non-negotiable. A superficial assessment is no longer adequate; a deep dive into a target company’s AI capabilities, data integrity, and governance frameworks is now a critical pre-investment step. Furthermore, navigating the intricacies of AI implementation and ensuring measurable, validated ROI demands a partner with expertise and a results-focused methodology. At Diligize, we are committed to providing precisely this level of guidance and support, empowering PE firms to not only implement AI effectively but to cultivate enduring AI capabilities that drive long-term success and consistently deliver exceptional, quantifiable value.
Author Bio
Steve Denby, based in London, UK, is a Senior Partner and an entrepreneur, technologist, consultant, public speaker, and leader with 28 years of experience in managed IT services. Specialising in private equity-backed businesses and rapid-growth organisations, Steve has deep expertise in mergers and acquisitions (M&A), supported by his studies at Imperial College Business School. He focuses on minimising risk and creating value through technology in privately invested companies growing by acquisition.
References
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