The future of value creation

Following the publication of the book “Transforming Financial Institutions”, we have continued to explore and share our value-creation methodology through our Programme “Innovation and Value Creation – Entrepreneurial Leadership in the Digital Age”.

This Programme serves as the platform for the ongoing dialogue and engagement with partners.

As part of this ongoing exchange, we set out three core theses on the future of value creation in the digital age that are applied universally at the intersection of the financial and enterprise ecosystem:

1. Insights drive decision making and automation

2. Platform integration drives service delivery

3. Full ownership drives the investment lifecycle

These three value-creation hypotheses (VCTs) have been instrumental in shaping our operating track record, serving as the foundation for our strategic pillars and their successful execution to drive entrepreneurship in the digital age.

Here are our core theses:

VCT I: Insights drive decision making and automation

In the age of digital transformation, value creation hinges on turning vast, fragmented data into actionable intelligence. This is not merely a technical challenge but a strategic imperative. At the heart of it lies a simple but powerful insight. Better data leads to better decisions, and better decisions fuel smarter automation.

This value-creation journey begins with the establishment of robust operating systems for complex, distributed data sets. As businesses grow in size and complexity, so does the information they generate. To harness it, organisations are building unified knowledge and intelligence platforms, defined as systems that integrate structured and unstructured data, embed advanced search and discovery capabilities, and power the workflows of knowledge workers across functions and geographies.

Artificial intelligence (AI) is redefining how we engage with data, information and knowledge. It enables organizations to move beyond static dashboards and backward-looking reports, toward real-time decision engines that drive insights and performance. These systems do not just automate processes but amplify human intelligence.

This is critical in high-stakes business environments where accuracy, speed, and context are non-negotiable. Whether it is understanding specific markets or surfacing best practices, AI-powered knowledge systems elevate the decision value chain.

Consider the impact of Retrieval-Augmented Generation (RAG), which combines the natural language power of large language models (LLMs) with access to curated, real-time knowledge. Unlike traditional search, RAG does not just return documents. It reads, interprets, and responds with synthesized insights grounded in verified data.

Beyond delivering insights, automation is undergoing a profound transformation from static, rule-based scripting to intelligent, adaptive orchestration. Modern AI systems can now automatically tag, cluster, and curate content to ensure relevance; extract hidden relationships that would be invisible through manual analysis; and trigger process automations such as approvals, alerts, or reporting based on real-time data inputs. This shift enables a dynamic, feedback-driven architecture that not only enhances operational efficiency but also establishes a foundation for scalable, repeatable value creation for businesses.

Human judgment remains at the core of effective AI deployment. Rather than replacing human intelligence, AI depends on it, thriving in systems designed with a human-in-the-loop approach. In these models, domain experts play a crucial role in verifying, contextualising, and refining machine-generated outputs. This collaboration ensures that insights remain relevant, trustworthy, and aligned with strategic goals, reinforcing the essential pillars of any robust knowledge strategy. Relevance and validation that lead to trust and accountability.

Leading organizations are already executing this strategy at scale. Companies like Palantir and IBM are deploying AI-driven platforms that unify structured and unstructured data to power real-time, high-stakes decision-making across industry verticals like finance, healthcare and government. Bloomberg is advancing financial intelligence with LLMs grounded in proprietary market data, while LexisNexis and Elsevier enable trusted knowledge access in legal and scientific domains. Perplexity, Glean and Sana Labs are building enterprise-grade knowledge assistants with semantic search and Retrieval-Augmented Generation (RAG). Starmind maps human expertise to unlock internal intelligence while it actively partners with enterprise-search platforms to build a unified knowledge platform.

Transforming insights into automation constitutes a competitive advantage. The integration of AI-powered knowledge platforms and intelligent automation is a foundational pillar of today’s decision making, performance improvement and overall value creation.

VCT II: Platform integration drives service delivery

Delivering consistent, scalable, and high-quality services in today’s fast-moving digital landscape is not only about building technology, but also about architecting ecosystems. The platforms that drive meaningful value are not isolated systems. They are integrated, composable, and interoperable that connect people, processes, and players across the enterprise ecosystem. This architecture unlocks network effects, especially in interconnected industries like financial services, healthcare, and logistics, where platforms plug into broader infrastructure development.

Modern service delivery demands open, modular, and intelligent technology infrastructure through three leading design principles. The most resilient digital platforms no longer centralise functions in rigid monoliths. They connect, compose, and collaborate across services and systems. This platform ecosystem mindset supports agility at scale, enabling organisations to evolve faster, adapt to market demands, and unlock innovation across internal and external boundaries.

Integration is the foundation of platforms architecture. By designing for connectivity from day one, companies can eliminate silos and ensure seamless data and process flow across their operations. API-first architecture, allowing secure and consistent connectivity across services, Event-driven models enable real-time responsiveness. Low-/no-code connectors democratise innovation across business units.

Composability enables platforms to be built from interchangeable building blocks, allowing capabilities to be added, removed, or modified without disrupting the overall system. This flexibility is crucial for scaling operations, localising offerings, or expanding into new markets. Microservices architectures support this by allowing individual components to evolve independently, while domain-driven design ensures that technology modules align closely with specific business functions. A marketplace mindset further enhances this approach by promoting the reuse of internal tools and integration of third-party solutions. The result is greater agility, faster time-to-market, reduced technical debt, and the ability to customise service delivery efficiently and sustainably.

In an interconnected ecosystem, no system thrives in isolation. Interoperability, empowered by open standards, shared protocols, and collaborative governance, enables seamless integration with external platforms, vendors, and partners. Standards such as OAuth, gRPC, OpenAPI, GraphQL and FDC3 facilitate secure, meaningful data exchange across systems. Data portability and sovereignty ensure that organizations can operate flexibly across jurisdictions and technology environments. Collaborative architecture designs unlock multiplier effects, drive co-innovation, accelerate time-to-value, and enable the delivery of shared services at scale. Both Microsoft and Google have embraced and prioritize interoperability but across distinct approaches. Microsoft, particularly through Microsoft Graph, focuses on enterprise-grade integration and support for backward compatibility. Google champions open, cloud-native architectures and developer-first flexibility through open-source leadership.

Open architecture is the strategic imperative that brings these ecosystems together. By enabling third-party tools, SaaS products, and proprietary systems to plug into a unified platform, companies gain the agility to evolve without rebuilding. This is especially relevant timelines are short and adaptability is paramount. Whether integrating new bolt-on acquisitions, launching digital services, or scaling operations across borders, open architecture ensures that infrastructure supports the business with its strategic imperatives.

Several leading technology companies exemplify this platform-centric, composable approach to value creation. Stripe has revolutionised financial infrastructure with an API-first model that empowers businesses to embed payments and financial services modularly. Shopify has built a composable commerce platform that enables merchants to customize and scale their operations using a vast app and partner ecosystem. Snowflake and Databricks offer interoperable data platforms that prioritize openness, scalability, and seamless collaboration across ecosystems. MuleSoft, now part of Salesforce, is a pioneer in integration, helping enterprises break down silos and orchestrate connectivity across legacy and modern systems. Digital and challenger banks follow a platform architecture in their service approach while Here (formerly OpenFin) and interop.io have focused on interoperability in capital and private markets.

Platform integration has become a strategic differentiator beyond cost efficiency. It enables scalable service delivery across geographies and customer segments, reduces IT complexity, improves ROI on tech investment and enhances resilience in the face of changing regulations or customer needs. It is also foundational for investor-backed companies looking to create repeatable value across their portfolio companies. Whether enabling synergies in a roll-up strategy or modernising a legacy operation, platform integration serves as the engine of operational leverage and customer experience transformation.

VCT III: Full ownership drives the investment lifecycle

In investor-backed companies, the traditional distinctions between venture capital (VC), growth equity, and private equity (PE) are dissolving. No longer content with being passive backers, many of today’s most sophisticated investors are shifting from funding innovation to owning and operating it. This evolution is reshaping how value is created and captured across the entire investment lifecycle with strategic positioning, operational support, and infrastructure-building as key differentiators.

Strategic moves by firms like Thrive Capital, Lightspeed, a16z, and Sequoia over the last couple of years reflect a broader shift. Growth investors are deploying PE-style strategies, focusing on consolidation, operational rigor, and platform scaling. Ownership is no longer simply about capital. It is about control, accountability, and execution. As the lines between VC and PE blur, success demands the vision of a startup backer combined with the disciplined execution of an operator.

VC has traditionally been about high-risk, high-reward bets on early-stage startups with exponential potential. Maturing markets, intensifying competition, and rising growth costs are forcing investors to rethink how value is built. Many technology startups drive strong innovation but falter in commercialisation. Growth capital becomes the base of a wider journey – not just to fund, but to build, combine, and scale. The investment and value-creation approach in VC and PE are converging.

PE has long focused on buying, consolidating, and optimising businesses to create value over time. A growing number of growth investors are employing similar playbooks. Buy-and-build strategies by acquiring complementary growth and scale-ups to create category leaders. Platform creation by uniting related businesses under shared services, infrastructure, and brand to unlock efficiency and scale. Operational excellence by embedding operators and subject-matter experts to drive growth, product development, and go-to-market strategies. 

The role of M&A is changing. It is no longer just an exit path. It is a strategic growth lever by driving roll-ups and platform consolidation. Investors are helping portfolio companies identify targets, execute deals, and integrate operations to accelerate market capture and realise synergies in talent, technology stack, and customer base.

Based on categories and strategic alignment, platforms are getting scaled, focussing on integration and synergies. They often consist of several acquisitions aligned under a shared mission and thesis, leadership team, and operating model. This buy-to-build strategies are particularly effective in fragmented markets like AI and enterprise technology. Scale and integration help mitigate commercial uncertainty and the increasingly prolonged sales cycle.

This evolving model elevates VC and growth investments beyond capital deployment. The convergence of VC and PE further blurs the lines between traditional funding stages. Growth equity, late-stage VC, and mid-market PE may all compete for similar deals, leading to more hybrid funding and value-realisation models. Specialisation and operational depth are key differentiators for value creation.

The author used ChatGPT for editing and researching this article.

Please see our fifth Blog series “Value creation and leadership for innovation and special situations” on Our Insight page for reference and literature reviews.