Open innovation is an innovation approach in which companies combine internal ideas and technologies with external knowledge, and also create pathways to bring internal assets to market through partners. Instead of relying solely on in-house R&D, the organization orchestrates knowledge flows with startups, universities, suppliers, customers, and even competitors, with clear governance and strategy.
In recent years, open innovation has gained even more relevance because digital transformation and AI have accelerated product cycles and broadened access to talent and technologies. At the same time, the demand for impact has increased: recent reports show that mature programs are migrating from "networking with startups" to tangible value metrics (revenue, margin, cost reduction, and speed), as indicated by market analyses and benchmarks, including studies with more than 1,600 corporations and startups in Europe (Sopra Steria Next, 2025). Open Innovation Report 2025).
Key points
- Open innovation is not about "making everything open": it's a intentional process to manage the input and output of knowledge, aligned with business model.
- The concept was popularized by Henry Chesbrough (2003) and evolved into a more complete definition in Chesbrough & Bogers (2014), incorporating monetary and non-monetary mechanisms.
- In practice, open innovation happens in two stages: inbound (outside-in) to bring technology/ideas and outbound (inside-out) to license, sell or disclose assets.
- The biggest gains tend to appear in time-to-market, access to technologies, R&D cost reduction via sharing and generation of new recipes (e.g., licensing).
- Success depends on governance, IP, absorption capacity and funnel metrics; without them, the program becomes a mere "innovation theater.".
What is Open Innovation?
Definition (classic and updated)
The most well-known way to explain open innovation is: innovating by combining what the company knows and does internally with external contributions, and exploring multiple ways to capture value in the market.
Chesbrough (2003): internal/external ideas + internal/external pathways to the market
In "The Era of Open Innovation," Henry Chesbrough describes the shift from a closed model to a scenario in which Valuable ideas can emerge from within or outside the company., and can reach the market through internal routes (self-launch) or external routes (partnerships, licensing, spin-offs). Reference: MIT Sloan Management Review.
The central point is strategic: it's not enough to invent; it's necessary market, And often, the person who invents isn't the one who best captures value.
Chesbrough & Bogers (2014): distributed process with intentionally managed knowledge flows (pecuniary and non-pecuniary mechanisms)
The definition evolves in Chesbrough & Bogers (2014): open innovation is a distributed process based on knowledge flows intentionally managed between organizations, using mechanisms pecuniary (e.g., purchase and licensing) and non-monetary (e.g., collaboration, sharing, disclosure). This reinforces that open innovation is not about "letting things leak," but to design How knowledge circulates and how value is captured. PDF: Chesbrough & Bogers (2014).
Concept premises
Useful knowledge is widely distributed.
The economic basis of open innovation is simple: talent, applied research, data, and solutions are everywhere, in startups, universities, specialized suppliers, communities, and research centers. With the mobility of professionals and the speed of technological advancement, a company rarely concentrates "the best knowledge" alone.
The need for "architecture"/governance to connect dispersed contributions.
The challenge, then, ceases to be "having ideas" and becomes... connect Dispersed contributions need focus and security. This requires architecture: governance, processes, contracts, prioritization criteria, clear roles, and integration with the innovation funnel and portfolio.
What Open Innovation is not (to avoid confusion)
One of the reasons programs fail is conceptual confusion. Open innovation is not an automatic synonym for other practices.
It is not synonymous with open source.
Open source is a specific licensing and collaboration model (primarily in software), with its own rules. It can be a tool within an open innovation strategy, but open innovation also includes closed and transactional mechanisms, such as licensing, IP acquisition, and joint ventures.
It's not just supply chain management.
Working with suppliers is important, but open innovation goes beyond operational efficiency. It involves... knowledge flows and assets (technology, patents, data, prototypes, methods) and may include partners outside the traditional supply chain.
It's not just user innovation.
User-driven innovation (lead users, communities) is powerful, but it's only part of the repertoire. Open innovation also includes universities, startups, intermediaries, platforms, competitors, and even internal asset monetization routes.
Why innovation moved from "closed" to "open"“
The Closed Innovation model (how it worked)
In the closed innovation model, the company concentrated R&D, development, and commercialization internally. The reasoning was: "if I control the entire process, I protect my competitive advantage and capture all the value.".
This worked well in sectors and times when:
- Knowledge was scarcer and more concentrated;
- Talent mobility was lower;
- The cost of creating and scaling technology was high for new entrants.
Factors of erosion of the closed model
Mobility of talent/knowledge workers
Professionals move between companies, undertake ventures, and collaborate globally. Because of this, critical knowledge "travels," and the advantage lies not only in creating, but also in... orchestrate and perform.
Venture capital and the emergence of startups
The ecosystem of venture capital And startups have created a machine for rapid experimentation. Startups test hypotheses with speed and focus, and large companies have come to see them as a source of technology (inbound) and also as a destination for underutilized assets (outbound).
Spin-offs and licensing as alternative routes
Ideas that don't "fit" into the core business can generate value outside of it. Instead of shelving them, the company can license them, create spin-offs, co-develop them, or sell the technology. This point appears strongly in the literature and in classic cases of "discarded ideas" that thrive in other contexts.
Growing role of external suppliers and partners.
Specialized suppliers (materials, semiconductors, automation, data, cybersecurity) often lead innovation in niche markets. Companies that create mechanisms to co-innovate with these players reduce risk and accelerate adoption.
How it works in practice: knowledge flows and market routes.
The most useful way to understand open innovation is as a system of flows (what comes in and what goes out) and of mechanisms (with or without money involved).
Inbound / Outside-in (bringing knowledge inside)
Inbound open innovation is when a company incorporates external knowledge to solve problems, accelerate development, or open new avenues.
Sourcing (non-monetary inbound)
Here, the company accesses knowledge without necessarily buying it: technical collaboration, joint research, innovation challenges, communities, partnerships with universities, pilot programs with startups.
Common examples:
- Co-creation programs with customers (customer immersion);
- hackathons and challenges for specific problems;
- Projects with universities and institutes (with well-defined publication and IP rules).
Acquiring (inbound monetary: purchase/licensing)
When the urgency is high or the technology is critical, it makes sense to pay: license patents, acquire a startup (M&A), hiring a company to develop, buying datasets, subscribing to AI platforms, among others.
This path usually requires:
- technological due diligence;
- IP risk assessment;
- Integration with product architecture and security.
Outbound / Inside-out (taking knowledge outside)
Outbound open innovation is when a company creates value by allowing internal knowledge to be used externally, with or without monetization.
Revealing (non-pecuniary outbound)
It is the intentional sharing without direct payment, for example:
- publish standards and specifications to encourage adoption;
- Open APIs/SDKs to attract developers;
- Contribute to communities (including open source, when it makes strategic sense).
The return usually comes through indirect effects: ecosystem, market pattern, reduced development costs, and increased complementarities.
Selling (outbound monetary: sale/licensing)
Here, there is direct monetization:
- out-licensing of patents;
- technology sales;
- creation of a spin-off with equity participation;
- Joint ventures to explore adjacent markets.
This is an underestimated point: many companies talk about inbound (bringing in startups), but ignore the value of outbound (transforming underutilized assets into revenue).
The role of the business model as a "filter"“
Open innovation only yields results when it passes through the filter of business model (how the company creates, delivers, and captures value).
What goes in (fit with the business model)
Not all foreign technology should be included. Typical criteria:
- Adherence to the priority problem (customer pain point or efficiency);
- compatibility with architecture and compliance;
- internal capacity to absorb (time, data, processes);
- economic viability (TCO, ROI, time-to-value).
What goes out (doesn't fit / is underutilized internally)
Similarly, internal assets can exit when:
- There is no strategic priority;
- The target market is not served by the company;
- The organization lacks the capacity to scale.;
- Another player has a better sales route.
Open Innovation Models and Mechanisms (with examples)
Ideation & crowdsourcing
Idea competitions, hackathons, challenges
Innovation crowdsourcing mechanisms work well when the problem is well-defined and measurable. Platforms such as Kaggle, HeroX e OpenIDEO They exemplify the challenges and communities model.
Best practices to avoid turning it into an "event":
- Define a problem with success criteria;
- Provide access to test data/environment whenever possible;
- Have a clear path: idea → evaluation → pilot → scale.
Co-development and collaborative design
Customer immersion
Customer immersion involves placing teams in close proximity to the customer's real-world context to reduce guesswork. In open innovation, this connects to pilot projects with customers, prototype testing, co-creation, and early feedback, decreasing rework and increasing buy-in.
Collaborative product design & development
Co-development with partners (suppliers, startups, universities) is useful when there is complementarity: one actor dominates technology, another dominates channel, data, brand, or scalability.
Here, contracts and governance are crucial:
- Definition of IP background (what each part already brings);
- foreground IP (which will be created along with it);
- rules of commercial exploitation and territoriality.
Platforming (APIs/SDKs) and innovation in ecosystems
Network effects and governance/QA
When a company becomes a platform (APIs, SDKs, marketplace), it creates an environment for third parties to innovate "on top" of its product. This can greatly accelerate innovation, but it requires strong governance.
- security and authentication;
- Review and QA of integrations;
- Monetization and revenue sharing rules;
- Compliance (especially in regulated sectors).
Innovation networks and intermediaries
Innovation intermediaries (role, when to use)
Intermediaries help reduce search costs and accelerate the match between problem and solution: hubs, specialized consultancies, scouting platforms, accelerators, and industry networks.
They make sense when:
- The market is large and fragmented;
- The company has low scouting maturity.;
- It is necessary to increase the diversity of sources (geographies, sectors, deep tech).
Programs driven by government and academia.
Knowledge Transfer Partnerships (KTP)
Models such as KTP (structured knowledge transfer partnerships) show how academia and industry can co-produce innovation with clear objectives, deadlines, and governance.
Open innovation in science (e.g., crowdsourcing of research questions)
In science, open innovation appears as crowdsourcing of questions, open data, and distributed collaboration. The logic is similar: to expand the pool of knowledge and accelerate discoveries, with rules of authorship, data, and ethics.
Expected benefits (and where they appear)
Acceleration and reduction of time-to-market
By repurposing existing technology (inbound) and testing with partners, the company reduces discovery and validation cycles. Modern programs tend to operate with short pilots and objective decision criteria, for example, rapid validation initiatives in dozens of days in corporate market programs (as described in open innovation benchmarks in 2025).
Cost-sharing in R&D
Co-development and partnerships allow for the sharing of costs and risks. This is especially relevant in:
- deep tech;
- hardware and advanced manufacturing;
- health and biotechnology;
- Applied AI (data, infrastructure, MLOps).
Access to a broader pool of ideas/technologies
The company is broadening its scope: trends, patents, papers, startups, and ready-made solutions. This is even more critical in the age of AI, where new tools and models are constantly emerging.
Competitive advantage, differentiation and new revenue streams
Open innovation isn't just about "bringing something new"; it's about capturing value. This includes:
- new products and features;
- entry into adjacent markets;
- Licensing revenue (outbound);
- creation of ecosystems and patterns.
Better market fit (early customer engagement)
By involving customers and partners early on, the company avoids building "in the dark." The result is usually better product-market fit and a higher adoption rate.
Risks, disadvantages, and trade-offs
IP: Leaks, Loss of Competitive Advantage, and How to Mitigate Them
Open innovation increases exposure. Without controls, there is a risk of know-how leakage, authorship disputes, and loss of competitive advantage.
NDAs, governance, legal frameworks
Practical mitigation measures:
- NDAs and confidentiality agreements proportionate to the risk;
- Clear definition of background vs. foreground IP;
- exclusivity clauses (when necessary) and usage limits;
- Governance committee to approve partnerships and publications;
- Documentation trail (who contributed, when, and in what way).
Complexity of coordinating and controlling contributions.
More actors = more dependencies. Without an operational "backbone," the program becomes a collection of disconnected initiatives.
Warning signs:
- many non-stop pilots;
- absence of product/area owner;
- Lack of decision criteria and deadlines.
Absorptive capacity: difficulty in identifying/incorporating external innovation.
Absorptive capacity is the ability to recognize external value, internalize it, and apply it. Without it, a company may find good solutions, but it cannot integrate them.
How to strengthen:
- Hybrid teams (business + technology + legal + purchasing);
- architecture and integration patterns;
- due diligence and experimentation routines;
- “Internal "owners" for each pilot (accountability).
Cultural tension and strategic alignment beyond the firm.
Open innovation requires collaboration, and this clashes with highly hierarchical or risk-averse cultures. It also requires alignment with strategy: if the core team doesn't buy into the idea, it dies.
How to implement an Open Innovation program (step by step)
Preparation
Objectives, strategic thesis, and focus areas.
Start with a simple thesis: "What is the purpose" of open innovation in the company? Examples:
- Reduce time-to-market in X area;
- Solve operational bottlenecks with AI;
- Access deep tech for new products;
- Monetize idle patents (outbound).
Define 3 to 5 focus areas to avoid dispersion.
Stakeholders and governance design
Map it out and get involved from the start:
- business (who has a P&L and a real problem);
- technology/engineering;
- Legal and compliance;
- purchases (if there is a contract);
- information security;
- finance (funding model).
Minimum governance includes: selection criteria, approval model, contract templates, and committee meeting schedule.
Operation (program backbone)
A mature operation typically relies on three self-reinforcing routines.
Trend management
Trend management is about maintaining a live radar for trends, with prioritization. Instead of "pretty reports," focus on decisions:
- Which trends became theses?
- Which theses became pilot projects?
- Which pilots saw a layover?
Technology scouting
Technology scouting is the active search for solutions (startups, papers, patents, suppliers). Here, it's worthwhile to combine human curation with digital tools and public databases.
Crowdsourcing/idea management
Idea management organizes the intake of internal and external ideas, with screening and feedback. Without this, the company generates frustration: people contribute and never receive feedback.
Tools and software (when it makes sense)
Tools are helpful when volume increases. It makes sense to consider platforms for:
- Challenge and submission management;
- CRM for startups and partners;
- pipeline of drivers;
- knowledge base and reuse (documentation, lessons learned);
- collaboration (e.g.: Miro, Notion, Slack, GitHub).
The criterion is not "having the tool," but rather reducing friction and increasing funnel traceability.
Support
Communication, transparency, incentives
To support open innovation:
- Communicate what is open for discussion (topics, problems, criteria);
- Publish results (including lessons learned from pilots who failed);
- Create incentives for departments to adopt solutions (time, budget, recognition).
Evaluation routine, pilots and scale
Define clear gates:
- discovery (fit and feasibility),
- pilot (success metric and timeline),
- Scale (owner, budget, integration and rollout).
Without stage 3, the program becomes a "POC factory".
Metrics: How to measure success and impact
Funnel metrics (ideas → evaluation → pilots → implementation)
- Number of opportunities identified (startups/solutions/theses);
- Conversion rate: opportunity → pilot;
- conversion rate: pilot → scale;
- reuse rate (solutions reapplied in other areas).
Speed metrics (time-to-market, time-to-pilot)
- Average time for screening;
- Time to sign pilot contract/agreement;
- time-to-pilot;
- time-to-value (time until it generates measurable benefit).
Financial metrics (economy, revenue, ROI)
- cost reduction (OPEX/CAPEX) attributed;
- Incremental revenue (new products, upsell, channels);
- Licensing revenue (outbound);
- ROI per initiative and per portfolio.
Engagement and satisfaction metrics
- NPS/satisfaction of internal areas with the program;
- Satisfaction of startups/partners (clarity, speed, fairness);
- Recurring partnerships (will they collaborate again?).
(Advanced option) Ecosystem health metrics (diversity, reuse, spillovers)
For platform/ecosystem-based programs:
- Diversity of partners (sectors, geography, profile);
- Quality and security of integrations (incidents, SLA);
- growth of add-ons (apps, integrations, modules);
- Spillovers: knowledge that is generated and reused.
Open innovation vs. related concepts
Open innovation vs closed innovation
- Closed innovationFocus on internal R&D and total control; tends to be slower and more expensive when the environment is dynamic.
- Open innovationIt combines internal and external factors; it accepts multiple market pathways; and it requires governance and integration with the business model.
In practice, it's not "either/or": leading companies maintain strong internal R&D and use open innovation to expand their reach and speed.
Open innovation vs. open source (where they conflict, where they combine)
Open source can accelerate development and create standards, but it comes with trade-offs:
- licensing compliance requirements;
- Code exposure and security;
- Contribution governance.
It combines well with open innovation when there is a clear thesis (e.g., attracting ecosystem, reducing development costs, accelerating adoption). For strategy, it is worth reading West & Gallagher (2006): paper.
Open innovation and startups (inbound/outbound in the startup-corporate relationship)
Startups can be:
- Source of innovation (inbound): pilots, licensing, acquisition;
- Destination of assets (outbound): spin-offs, licensing, co-development.
The critical point is aligning expectations: deadlines, compliance, data access, internal sponsor, and scalability criteria. Research on startups and open innovation helps structure this relationship (e.g., Usman & Vanhaverbeke, 2017).
Trends: From "firm-centric" open innovation to ecosystems
Open innovation ecosystem and digital platforms
The trend is moving away from the "company at the center calling startups" model towards ecosystems with multiple actors, where platforms, data, and integrations define speed and scale. In AI, this is even stronger: the advantage lies in data, distribution, and orchestration capabilities.
Multi-stakeholder orchestration and shared asset governance
With more actors, the importance of:
- Rules for accessing APIs and data;
- Security and privacy policies;
- interoperability standards;
- monetization models and value sharing.
“"Value constellation" and data-driven ecosystems
In data-driven ecosystems, value is co-created: one partner generates data, another creates the model, another distributes it, and another integrates it into the client's workflow. Open innovation, here, becomes a competence in designing incentives and governance.
FAQ (Frequently Asked Questions)
What is open innovation and why do companies adopt open innovation?
Open innovation is innovation based on combining internal and external knowledge and exploring multiple paths to market. Companies adopt open innovation to accelerate time-to-market, access new technologies (such as AI), and reduce R&D costs and risks.
What is inbound and outbound open innovation?
Inbound open innovation (outside-in) is bringing external knowledge in, through partnerships, scouting, licensing, or acquisition. Outbound open innovation (inside-out) is taking internal assets out, through disclosure, sale, licensing, or spin-offs.
Does open innovation replace internal R&D?
No. Open innovation complements internal R&D: the internal team defines strategy, integrates technology, and ensures execution. Without strong R&D and engineering, the company cannot absorb and scale external innovation.
Is open innovation the same thing as open source?
No. Open source is a licensing and collaboration model with specific rules, widely used in software. Open innovation is broader and also includes paid mechanisms (licensing, acquisition) and partnerships with defined governance and IP.
How does the business model define what goes in and what comes out in open innovation?
The business model acts as a filter: what increases the capacity to create/deliver value with technical and economic viability is included. What is underutilized internally but can generate value through licensing, sale, joint venture, or spin-off is excluded.
What are the main IP risks in open innovation and how can they be mitigated?
The main risks are knowledge leakage, authorship disputes, and loss of competitive advantage. To mitigate these risks, use NDAs, define IP background/foreground, establish approval governance, and maintain documentation and decision trails.
How to start a small open innovation program and scale it up?
Start with 1 to 2 focus areas and a simple funnel: problem → scouting → pilot with metrics → scaling decision. Scale when there is governance, business sponsor, legal templates, and conversion and financial impact metrics.
What metrics show whether open innovation is truly working?
In addition to the volume of ideas, track pilot rate → scale, time-to-pilot, cost savings generated, incremental revenue, and satisfaction of internal teams. Mature programs also measure ROI by portfolio and reuse of solutions.
When is it worthwhile to license technology (acquiring) instead of developing it internally?
Licensing is worthwhile when time is critical, the technology is already mature, and the total cost (including integration) is lower than developing from scratch. It's also a good option when the company lacks internal capabilities or when the technical risk is high.
How to choose between partnering with a startup, forming a joint venture, or making an acquisition in open innovation?
Partnerships and pilot programs are effective for quick validation with low commitment. Joint ventures make sense when there are strong complementarities and a need for co-investment. Acquisitions are usually indicated when the technology is strategic, requires control, and the cultural fit and integration are feasible.