Impact of Innovative Technologies on Business Ecosystems
A critical search for clues through networks and business ecosystems with a focus on the financial industry
Technological innovations have always been catalysts for social change – but never before have business ecosystems or networks of different companies and their role in integrating a wide range of services and products been so visibly at the center of attention. Whether we are making contactless payments, consulting chatbots or entering virtual worlds – often, networks and business ecosystems are based on platforms, which are an important component for orchestrating value creation. Platforms are purpose-built organizational and transactional infrastructures (modules and interfaces) of business ecosystems. You can find a blog post on the basics of business ecosystems here: Basics of business ecosystems | cc-bei.news.
A change in key technological foundations often has an impact on how the business models of business ecosystems change via the platform. One example of this is network-sensitive content – AI algorithms dynamically adapt offers to collective behavioral patterns.
The paradox is that while the number of connected devices and services is growing exponentially, the user’s perceived effort is shrinking to just a few taps or a voice command. The Internet of Things, artificial intelligence, distributed ledger technology and the emerging metaverse are acting like turbochargers – they are not only connecting objects, but also markets, communities and entire business models. In the following blog post, I outline some thoughts on the impact of innovative technologies and data on the technological basis (platforms) of business ecosystems
The starting point is data that circulates via platforms using various technologies, generates added value, and creates new dependencies. The four key technologies mentioned above are changing the network of digital interactions and therefore also the underlying business models.
The Logic of Data: From Digitization to the Information Value Chain
It all starts with digitization – the digital representation of physical objects and events. It creates digital data that creates value as raw material along the five stages of the information value chain: Data Collection, Data Storage, Data Processing & Analytics, Presentation & Distribution and Information Use (Davenport, T. H. 1993, Process Innovation: Reengineering Work through Information Technology, Harvard Business School Press).
Each of these stages is currently subject to its own exponential but synchronized cost and performance curve. This multiplies the effect; cheap sensors would be worth little if there weren’t also inexpensive cloud terabytes and AI algorithms that distil meaning from the noise. For companies, this means that value creation is shifting from physical products to information-based services.
Internet of Things (IoT): Connected Things, Connected Business Models
Kevin Ashton coined the term Internet of Things (IoT) in 1998 with the vision of connecting people and objects anytime, anywhere and via any network (Web Semantics: the Internet of Things | WIRED). 25 years later, we are closer than ever to achieving this goal – the volume of IoT is expected to grow by almost 20% between 2024 and 2029 (Internet of Things (IoT) Market Share, Forecast | Growth Analysis and Trends Report [2032])
There are numerous practical examples, e.g. “beacon banking”. Beacon banking is a concept in which banks use beacon technology to send customers personalized offers or information via their mobile device in real time, based on their proximity to a physical location of the bank. For example, Barclays branches recognize customers with special needs before they even enter the door and inform bank staff to assist them, e.g. to access the bank via a ramp (Barclays taps beacons to streamline bank visits for disabled customers | Retail Dive). In addition, we will also hear commands to digital assistants more frequently in the future, such as “Alexa, pay for gas” at petrol stations (Alexa, Pay for Gas | Vimeo). This illustrates how IoT and voice commerce are merging.
However, there are cracks in the glossy picture: a lack of interoperability, data overload and, last but not least, cyber security are major challenges for the providers, but also for customers in the context of security. Passwords, credit card information, location data – the more we network technology, the bigger the attack surface becomes. Regulation and liability issues hang like swords of Damocles over every IoT roll-out, especially in the financial sector. The success of IoT will not be determined by sensor prices alone, but by who can set the relevant standards and establish trust.
Artificial intelligence (AI): When Algorithms Reorganize Value Creation
The definition of AI is constantly changing, while machine learning used to be considered AI, AI experts often refer to AI as “machine learning + x”. This is also described as the AI effect and corrects definitions as new milestones are created. The financial industry has been actively using machine learning since the 1980s, e.g. for fraud detection. The emergence of Gen AI has provided an enormous boost. Since then, enormous efficiency gains have been observed. For example, JP Morgan COIN saves 360,000 hours of legal document searches per year by using large language models (Case Study: JPMorgan Chase’s Contract Intelligence (COiN) platform for Document Analysis | LinkedIn).
Nevertheless, hurdles remain: poor-quality data, a lack of willingness to integrate AI within the company (corporate mindset), legal gray areas and the simple fear of losing control. Non-existent or changing regulation is also a stumbling block. But potential lock-in effects and geopolitical risks also make companies think very carefully about who they choose as a solution provider. AI is therefore not a miracle cure, but requires disciplined data hygiene, clear governance and an awareness of the dynamic framework in which “intelligence” is constantly being redefined.
Distributed Ledger Technology (DLT) as a Tool for Decentralized Trust Generation
Numerous approaches to the use of DLT can be observed in practice. These include the enormous range of tokenized assets. From gold NFTs to whisky tokens – physical assets are given digital twins, tradable in micro-denominations. However, entire processes are also handled on the blockchain. This includes, for example, Allianz’s catastrophe swap – processes that used to take days now run in minutes or seconds (Allianz Risk Transfer and Nephila implement catastrophe swap with blockchain technology | Allianz).
However, cryptocurrencies account for the highest proportion of activity. Numerous banks now hold these assets in custody, even if significantly fewer institutions still offer advice, presumably due to the risks of liability.
Where assets can be traded in seconds, memecoin volatility flourishes: a tweet from the US president can catapult prices to astronomical heights – or plummet to rock bottom. Companies are exposed to reputational risks if partner exchanges collapse or private keys are lost. The interoperability of different networks and effective customer demand remain unresolved issues. Irrespective of this, DLT can be seen as a tool for process optimization via programmable payments. Its benefit to society depends on who sets standards and which use cases create added value directly for end users.
Metaverses: The Hype Currently Seems to have been Replaced by Gen AI
Virtual worlds have been around since Second Life 2003 (What Is Second Life? A Brief History of the Metaverse | MUO). What is new is that VR/AR hardware, 5G networks, blockchain economics and creator tools now promise a common platform. Global shipments of AR/VR headsets are expected to rise to 22.9 million units by 2028, which corresponds to a compound annual growth rate (CAGR) of 38.6% (VR and AR headsets demand set to surge on AI, lower costs, IDC says | Reuters). But devices alone do not make a metaverse. There is currently no network infrastructure that streams immersive content for millions in parallel. However, three scenarios seem to be emerging for the future:
- Winner-takes-it-all: A central universe à la Meta
- Hybrid clusters: Industry-specific sub-metaverses, e.g. for gaming or work
- Web page analog: Anyone can host their own mini-metaverse.
The industry is still struggling with governance, hardware costs, legal issues and content moderation. Whether and how metaverses become the next stage of Internet evolution will be decided by open standards, net neutrality and social acceptance – not by the number of glasses sold.
Patterns of digital transformation
Similar patterns of change are emerging across all technologies:
- Falling marginal costs: Copies of data cost (almost) nothing.
- Ecosystem economy: orchestrators dominate markets, peer-to-peer changes the balance of power.
- Industry convergence: payment services in the metaverse, retail via IoT – boundaries are blurring.
- Personalization: AI refines offers at micro-segment level.
These patterns have a reinforcing effect: the cheaper the transactions, the more data; the more data, the more powerful the AI; data or property rights and contractual relationships can be stored decentrally on a blockchain. All human interaction is increasingly taking place in immersive metaverses using these technologies.
Key Aspects of Business Ecosystems & Networks platforms
Conclusion: Who will win the game of technology?
Innovative technologies are radically changing the interaction within networks & business ecosystems by increasingly turning technological platforms into decisive hubs. They no longer form networks passively, but actively and dynamically:
- Platform dominance: Technologies such as IoT and AI strengthen platform providers by bundling transactions and communication on platforms. Whoever controls platforms determines the rules of networks & business ecosystems.
- Network dynamics: Distributed ledger technologies are changing the trust structure and enabling decentralized interactions – this could call the role of central platforms into question in the long term.
- Immersion and interaction: The metaverse takes interactivity within digital networks to a new level by drawing users into immersive, virtual environments and creating new social and economic dynamics.
To be successful in this new network economy, it is not enough to simply adapt technological trends. Companies must strategically decide which platforms they want to be active on, which standards they want to support and how they want to secure long-term value creation and influence. It will be crucial not to blindly follow technology, but to actively and responsibly shape it.
