Commercial- and operationalising innovation

This article is the second post of the fifth series. It elaborates on the core principles introduced by the book "Transforming Financial Institutions" while applying them more broadly to enterprise ecosystems.

Innovation is a multifaceted concept, but at its core, it can be defined as the practical implementation of ideas that lead to the development and enhancement of products and services. It lies at the heart of entrepreneurship and serves as a fundamental driver of growth and competitiveness in any business environment.

This blog explores key concepts essential for driving innovation from a value-creation perspective. It examines foundational and platform technologies – as part of the broader category of emerging technologies that are reshaping industries through disruptive business novel operating models. Furthermore, it outlines a strategic roadmap for fostering innovation across organisations, ultimately transforming ecosystems and disrupting incumbent players

Foundational and platform technologies

Emerging technologies are both disruptive and transformative, reshaping operating environments and redefining business ecosystems. These technologies can be broadly categorized into foundational and platform technologies. Foundational technologies target specific components, delivering focused services and solutions. In contrast, platform technologies integrate a range of technology solutions and technology-enabled services through a unified access point.

Our focus lies on artificial intelligence (AI), enterprise software – leveraging open-source software (OSS) and software as a service (SaaS) – as well as blockchain with its early-stage use infrastructure cases. These form the core components of foundational technologies. Building on these, we apply the principles of open architecture to develop scalable and integrated platform technologies.

Artificial intelligence (AI)

Intelligence, the ability to reason, rationalise and make fact-based decisions has been a distinctive human quality that gives power and wealth for the one mastering it. To augment human intelligence with the help of artificial tools goes back a long way. Its evolution had an inflection point with Alan Turing’s “Turing Test” that sets out the theory of an universal programmable computer, the intelligent machine. The programmable digital computer as a tangible device became reality in the 1940s. It inspired the possibility of building an electronic brain while the field of AI research was founded in the 1950s, starting with a workshop at Dartmouth College. It faced it’s a series of setbacks, however, until the 2000s when the field of big-data techniques and machine learning (ML) made substantial progress due to advanced in computational power and the availability of immense data sets that emerged with the internet since the 1990s.

In 2010s, Google, DeepMind and OpenAI advanced the field of deep learning that reached another milestone with the debut of the transformer architecture in 2017. This led to the rapid scaling and public releases of large language models (LLMs) like OpenAI’s ChatGPT. OpenAI released ChatGPT in late 2022, running an improved LLM called GPT-3.5, followed by its successor GPT-4 with even more adept human-intelligence (HI) like capabilities. LLMs have made substantial advancements with the process of reinforcement learning from human feedback (RLHF). The concept allows human raters to adjust, reward and penalise output produced by AI through which content gradually becomes better. Intelligence augmentation under the objective to enhance decision making and productivity became for sudden a tangible reality with a visionary path to artificial general intelligence (AGI).

The AI value chain and lifecycle start with data collection, ingestion and integration across different sources. The data sets need to be contextualised before getting processed in more advanced forms of big data techniques and ML. Natural language processing (NLP) is a branch of ML that facilitates the interaction between computers and human language. Retrieval-augmented generation (RAG) is a technique that specifies the information and data source. Sentiment analysis and different modelling techniques drive today different analytics with the objective to discover useful patterns between different items of data.

This process leads to the build up of specific capabilities across numerous use cases. Sentiment analysis describes the process of computationally identifying and categorizing opinions in text and other written content, especially in order to evaluate their attitude towards a particular subject is positive, negative, or neutral. Bayesian inference has an important applications in AI-driven modelling and expert systems. It played a crucial role in computerized pattern recognition techniques and analysing different sets of structured and unstructured data. Complexity science is a multi-disciplinary modelling approach that incorporates different scientific fields to study and predict complex system. It includes the fields of system theory and cybernetics which both have similar objectives but different origins and scopes. Those AI techniques have advanced dramatically over the last couple years due the developments of LLM and Generative Artificial Intelligence (GenAI).

Enterprise software

Enterprise software refers to computer software designed to meet the needs of an entire business organisation. It is typically used by larger organizations – the classification for enterprise – to handle complex operations across departments. It is built to be scale, secure, and customise. We are covering three key concepts of enterprise software as part of this section.

Open-source software (OSS) is software that is distributed with its source code, making it available for use, modification, and distribution with its original rights. Open-source began as a radical idea, part of a fringe political campaign in the 1980s, believing that anyone should be allowed to use and exchange software without copyright restrictions. In the 1990s, the campaign evolved in a ideological technology movement, but remained at the edges of the software industry. With the rise of Linux, the now ubiquitous open-source operating system, in the 2000s, it reached wider acceptance as a technology-development and design movement. Today, most software and devices are run in OSS.   

Integration, composability and interoperability are key features of OSS applications. Integration refers to the incorporation of freely available, community-developed software components into an existing technology stack to enhance functionality, reduce costs, and promote interoperability. Composability is the ability of different components, systems, or modules to be combined and recombined in agile ways to create new functionalities and products. Interoperability refers to the ability of different systems, devices, applications, or organizations to communicate, exchange data, and work together seamlessly. Composability is the key enabler for interoperability at a higher level by ensuring that modular elements can work together seamlessly. 

Software as a Service (SaaS) is a cloud-based software deployment model in which applications are centrally hosted and managed by a cloud service provider. It operates on a subscription-based licensing model, typically allowing users to pay on a pay-as-you-go basis. The entire stack – including infrastructure, middleware, applications, and data – is managed within the provider’s platform and data centre. A SaaS platform delivers an integrated environment equipped with essential tools and functionalities designed to support the full lifecycle of user requirements. This includes capabilities for data sharing, due diligence, and fund discovery, enabling seamless and comprehensive service delivery.

Enterprise Resource Planning (ERP) is a software system that integrates and manages various business processes across different functions. It provides a single, unified view of operations with the objective to streamline processes, improve efficiency, and enable better decision-making. Different ERP software systems can be integrated to an operating system that supports a platform solution for different client segments such as small and mid-size enterprises and/or specific industry verticals. Core systems, for instance in banking, can be the starting point for building and scaling overarching systems for payment, lending and risk management. 

Blockchain

Blockchain is commonly associated with cryptocurrencies like Bitcoin and Ether, as it underpins global transactions without the need for financial intermediaries. However, its applications extend far beyond digital currencies. Blockchain is increasingly being used in areas such as payment and settlement infrastructure, supply chains, and data ownership and management. By storing data on a decentralized network, it offers enhanced transparency, security, and resilience. This decentralization is a foundational principle of the Web3 movement, which envisions the next evolution of the internet – one where users can not only read and write content, but also own and control their digital presence.

At its core, blockchain is a collection of protocols, defined as rules that govern how the network functions. These protocols are written in computer code and compiled into software, which enforces the logic of the system. They define how ownership is recorded, what qualifies as a valid transaction, and how participants interact with the network. This capacity to programmatically enforce trust and ownership makes blockchain a powerful tool for transactional and contractual processes, especially in finance and law.

In his book “Read Write Own: Building the Next Era of the Internet”, Chris Dixon explores the evolution of the internet and makes a compelling case for a decentralised future driven by blockchain technology. He envisions blockchain as a means of creating open, transparent, and community-governed networks. The technology hereby realises the original promise of the internet by giving users true ownership and participatory rights.

Platform technologies

Platform technologies serve as a base and infrastructure which allows the development, deployment and run of other applications, processes, or systems. These technologies integrate different technology-enabled services on a open and unified access layer, and enable the development, integration, and operation of applications and entire ecosystems. They represent building blocks that enable a wide range of functionalities across different industries and sectors. Common framework, tools, and standards are provided that allow different users, developers, or businesses to build and interact with applications efficiently. Integration and interoperability are key features of platform technologies. These features allow the build-up of advanced operating systems and the establishment of disruptive business models.

Transformative operating and disruptive business models

The integration of foundational technologies into commercial and operational platforms drives the evolution of innovative and disruptive business models. This transformation is guided by a structured innovation roadmap, which is closely aligned with the product roadmap. Together, they define the product-market fit and inform the broader commercial strategy. These technologies not only enable product capabilities but also shape how the product is positioned in the market and monetized.

The commercial framework supporting these technologies spans across product development, marketing, sales, and customer success – with a clear focus on accelerating revenue growth. This framework is driven by detailed client segmentation and the development of buyer personas, all anchored around the ideal client profile (ICP). This approach informs lead generation strategies, campaign messaging, and sales approaches. Sales velocity is then optimized through clearly defined processes and prioritization mechanisms such as pipeline scoring, lead nurturing workflows, and conversion rate analysis. This ensures that resources are allocated effectively to high-impact opportunities, increasing both efficiency and growth potential.

The author used ChatGPT for editing this blog, and different research topics. 

Notes and further reading:

• DAVENPORT THOMAS H. AND MITTAL, NITIN: All in on AI – How Smart Companies Win Big with Artificial Intelligence; 2023

• DIXON, CHRIS: Read, Write, Own – Blockchain, Crypto and the Future of the Internet; 2023

• JENSEN, HENDRIK JELDTOFT: Complexity Science – The Study of Emergence; 2022

• KISSINGER HENRY ET AL: The Age of AI; 2021

• MITCHELL, MELANIE: Artificial Intelligence – A Guide to Thinking Humans; 2020

• MOLLICK, ETHAN: CO-INTELLIGENCE – Living and Working with AI; 2024

• OLSON, PARMY: AI, ChatGPT and the race that will change the world; 2024

• SULEYMAN, MUSTAFA: The Coming Wave: AI, Power and Our Future; 2023

• VAN DER KOOIJ ET AL: The SaaS Sales Method; 2018

• WITT, STEPHEN: The Thinking Machine; 2025