B2B Value Capture in the Intangible Economy – What’s It Really Worth to Technology Buyers?

Observations

Since the early 1990s, technological advancements have generally followed a four-stage market cycle: Innovation, Disruption, Standardisation/Commoditisation, and Industrialisation, though with compressed timelines in recent decades². For instance, digital platforms demonstrated almost 2x faster progression through stages compared to physical infrastructure technologies, with examined cases validating these cyclical patterns in value dynamics. Furthermore, regulatory interventions now disrupt traditional value-capture strategies in two-thirds of digital markets.

Furthermore, network effects created disproportionate first-mover advantages, with early innovators securing over half of market value capture across sectors. By way of explanation, network-effect technologies like social media platforms, e-commerce platforms, sharing economy services and instant-messaging services become more valuable to each user as more people use them. This is a self-reinforcing cycle where the value of the product increases with the number of users, creating a competitive advantage for early movers.

Given the four-stage market cycle for technology advancement, SVR’s central hypothesis is that strategic options emerge for market participants in a four-stage value cycle:

1. Innovation: breakthrough technological development.
2. Value Creation: commercialisation generating novel economic value.
3. Competition: market saturation and rivalrous dynamics.
4. Value Capture: institutionalisation of revenue models and regulatory frameworks³.

This cyclical process creates windows of opportunity for businesses to first create and then capture value, with temporal progression influenced by technological characteristics and market conditions.

Value Creation Patterns

Broadly, there is an average 18-month lag between innovation (Stage 1) and measurable value creation (Stage 2), with network effect technologies creating greater value than standalone innovations⁴.

Cycle acceleration speeds up the technological adoption cycles e.g., through digitalisation (AI, Cloud), network effects, and regulatory changes. This has driven the average cycle duration down from ~14 years (1990s innovations) to ~6.7 years (post-2010 innovations). AI and blockchain technologies show potential for sub-5-year full cycles, with recent AI models reaching the market in under two years, down from five years for traditional software.

These conditions point to an emerging phenomenon – SVR hyper-cycles – that has important implications for the way that buyers and suppliers frame next-generation agreements for mutual success.

Value Capture Challenges

Early movers capture ~60 per cent of total addressable market value in digital sectors e.g., Amazon, Facebook, Google, Microsoft, and Apple demonstrated how early adoption and network effects yield disproportionate market share, and their initial leads created ecosystems and loyal user bases, solidifying their pricing power, while hindering competitors and regulators.

Against this, regulatory interventions have undercut value capture potential in data-dependent industries post-2016 e.g., EU General Data Protection Regulation (2018) and California Consumer Privacy Act (2020) imposed data privacy restrictions, reducing data monetisation potential for industries like advertising and Fintech⁵.

The Rise of Intangible Value in Modern Economic Systems

Intangibles are a material driver of modern economic value creation, though traditional institutions, measurement systems, and accounting rules lag in recognition and management.

Intangible value constitutes non-physical assets that generate economic returns through knowledge, relationships, and systemic advantages. Unlike traditional physical assets, intangibles exhibit:

• Non-Rivalrous Consumption: simultaneous use without depletion (e.g., software algorithms).
• Scalability Multipliers: near-zero marginal replication costs (e.g., digital content).
• Network Synergies: value accretion from user interactions (e.g., social networks, AI).

AI’s value grows through data accumulation, feedback loops, and force multiplier impacts from ecosystem effects. Whereas network effects focus on the increased value of a product or service as more people use it, ecosystem effects encompass the broader, interconnected relationships and value creation within a system of interacting parties. User interactions fuel data, refine performance, and expand integrated services, creating a self-reinforcing cycle of improvement, as seen in recommendation systems and, looking forward, the huge potential for societal benefits from platform effects in AI-driven integration e.g.,:

• Medical research tools and genomic databases.
• Transport management and predictive analytics.

Organisations that master intangible flows capture 3–5x greater value from technological adoption cycles compared to physical-asset options⁶. Emerging challenges around equitable distribution and sustainable governance will define next-generation value capture battles.

Our view is that the dominance of intangible value and compressed technology cycles demands a radical rethinking of B2B procurement strategies. Buyers must navigate ecosystems where IP owners and suppliers wield asymmetric power through control of algorithms, data networks, and platform effects.

For SVR to yield maximum benefits, all market participants must commit to collaborate where their interests align best. This focus must build on connections through procurement to stimulate cross-functional collaboration across the C-suite, who must engage so that procurement can do their part to source what is actually needed, in contracts that will perform for the business owners in the C-suite, and with flexibility to easily change supplier mix and contract objectives throughout the emerging SVR hyper-cycles.

Next Steps

Undertake structured analysis of enterprise-wide implications and adopt commercial excellence mechanisms to align suppliers’ commitments and relationship investments with buyer’s strategic objectives, and to secure buyer’s fair shares of value captures.

Immediate Commercial Excellence⁷ (CommX) Options Include:

a) Move from Fixed Requirements to Adaptive Roadmaps

Embed Living Requirements Model⁸ regimes in existing and new contracts that regularly auto-refresh technical specifications using AI analysis of buyer-relevant data sources (market trends, competitor moves, R&D pipelines).

• For example, a major global financial services institution’s blockchain procurement team might use machine learning (ML) to predict needed application programming interface (API) changes 6 months ahead.

b) Establish Ecosystem-Driven Competition

It’s a commercial reality that most B2B technology buyers share critical suppliers with direct competitors. To secure advantages, embed asymmetric dependency balances in contracts e.g.,

• Demand custom algorithm forks e.g., a world champion retailer requires an exclusive version of Google’s AdTech stack.
• Negotiate preferential access to beta features e.g., a creative software solutions provider gives priority clients early AI model access.

c) Prevent Coopetition Value Leakage

Suppliers repurpose buyer data/IP to serve buyer’s competitors. To protect your advantages demand mitigation clauses in your agreements e.g.,:

• Data Sandboxes: an iconic beverage manufacturer negotiates that their Azure contract isolates training data from Microsoft’s general AI models.
• IP Escrow Accounts: a pharmaceutical manufacturer requires codebase deposits with third parties like IronMountain and EscrowTech.

d) Secure Commercial Mechanisms for buyer Value Capture

Adopt Pricing for Value Through Outcome-Linked Models⁹

Implement Algorithmic Escalation Clauses

Use AI-specific dynamic performance ratchet clauses that are tied to performance standards e.g., “If supplier’s AI accuracy drops below 92 per cent vs. competitors, fees reduce by 1.5 per cent/point”.

Secure relevant, contemporary Audit Rights

In AI-driven contracts, black box audit rights should allow a buyer to inspect the algorithms or models used by a supplier to ensure they meet agreed-upon standards or do not contain biases.

Consider Ecosystem Participation Terms

Examples include sharing innovation dividends e.g., through revenue partnerships from solutions built using buyers’ data, and First-Look Rights that grant exclusivity on partner accelerators for a specified period or market.

e) Update Procurement Playbook E.g.

Undertake Supplier Power Mapping and Mitigate Risks Arising

Conduct regular assessments using tools like:

• Dependency matrices to plot a cohort of suppliers on axes of substitutability vs. value contribution.
• War gaming to simulate supplier responses and prevent lock-in against commercial scenarios e.g., M&A, technology pivots, regulation changes.

Establish Coopetition Firewalls

Negotiate robust property rights protections e.g., IP zoning, ethical walls, and secure substantial indemnities.

Looking Forward: CommX Strategies Towards the Horizon

Buyers must adopt militant contractual flexibility to survive in intangible-driven ecosystems. The new procurement rulebook demands:

• Algorithmic Contract Governance: real-time KPI tracking with automated enforcement.
• Coopetition Architecture: ethical walls and value-sharing pools.
• Ecosystem Equity: warrants, royalties, and IP flows that capture upstream innovation value.

To win in the value capture war, implement structural shifts in supplier management:

• From vendor/contract management to value orchestration.
• Real-time contract governance.
• Co-investment platforms.

Embed buyer value capture options in contracts to capture upside e.g., through:

• Equity Warrants: for a percentage of supplier shares.
• Royalty Stakes: for a percentage of supplier revenue from any trade using the buyer’s background or new data.
• Anti-Homogenisation Levers: such as exclusive feature development.
• Ecosystem Arbitrage: such as multi-vendor playoffs through parallel supplier trials, pays only the top performer.
• Patent Cross-Licensing Hubs: that set clear boundaries for IP sharing and market exploitation.

Footnotes

1. © Barta Global Services 2014–2025.
2. Sources: ‘Digital Platforms: A New Era of Growth and Innovation’, McKinsey & Company, 2020; ‘Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages’, Pérez, C., Edward Elgar Publishing, 2002; ‘Digital Markets Act: Ensuring Fair Competition in the Digital Sector’, European Commission, 2020; ‘How Network Effects Can Create Powerful First-Mover Advantages’, Harvard Business Review, 2019. Noting that findings discussed here in general terms are illustrative of broader trends observed in the literature. For precise figures, additional specific studies or data analysis would be required.
3. Exceptions occur in regulated industries (e. g., Healthtech, Fintech) where Stage 4 interventions precede full Competition development.
4. Sources: A University of Cambridge study found that platform businesses with strong network effects have significantly higher valuations compared to traditional pipeline businesses (Moazed, A., & Johnson, N. L. (2016) Modern Monopolies: What It Takes to Dominate the 21st Century Economy. St. Martin’s Press). NFX, a venture capital firm, analysis of over 200 technology companies found that network effects were responsible for 70 % of the value created (https://www.nfx.com/post/network-effects-manual). This evidence supports the idea that network effect technologies can create significantly greater value than standalone innovations, noting that the degree of value creation varies depending on various factors e. g., the specific technology and market context.
5. Sources: iapp.org, Forrester
6. Sources: ‘Intangible Assets Market Report’, PwC, (2023); ‘Intellectual Property Statistics’, OECD, (2024); ‘The Evolution of Artificial Intelligence (AI) Spending by the U. S. Government’, Brookings, 2024; ‘Impact assessment of the Digital Markets Act’, European Comission, 2020.
7. In this context, CommX is about equitable market arrangements that vest commercial benefits in the buyer through commercial innovations.
8. In this context, a Living Requirements Model is a flexible, evolving framework for automatically capturing and managing scope, quality and pricing requirements, designed to be updated and refined throughout the contract’s lifecycle, rather than being a static document.
9. Sources: ‘Outcome-Based Pricing Models; Harvard Business Review (2019) Gain-Sharing Contracts’, McKinsey & Company, 2020; ‘Cloud Computing: CapEx to OpEx; AWS Official Case Studies (Various Dates) Ecosystem Value Creation’, Forrester Research (2020).