Open Innovation and AI: How Artificial Intelligence Is Changing Open Innovation
Open innovation and AI They're redrawing the boundaries between human creativity, data, and algorithms. Open innovation and AI are no longer simply improving existing processes; they're changing the rules of the game for businesses, research teams, and digital businesses.
Researcher Linus Dahlander, a professor at the European School of Management and Technology in Berlin, analyzes this transformation starting from the open innovation model introduced over twenty years ago by Henry Chesbrough. Today, the integration of’artificial intelligence It introduces new collaborative dynamics, new forms of knowledge exchange, and new challenges of governance, transparency, and trust into innovation processes.
From distributed deep learning to global collaborative platforms, the combination of open innovation and AI It creates hybrid ecosystems in which algorithms actively participate in the generation, selection, and implementation of ideas. This revolution also directly impacts the way companies design digital products, customer experiences, and marketing strategies. marketing automation.
The Evolution of Open Innovation in the AI Era
In Henry Chesbrough's original definition, open innovation consisted of opening corporate boundaries to foster a two-way exchange of ideas between internal and external stakeholders. Organizations needed to learn to bring external ideas in, but also to spread internal ideas to partners, startups, universities, and markets.
According to Dahlander, this open innovation model was based on two fundamental movements: the import of knowledge from outside and the export of knowledge to the ecosystem. With the arrival of open innovation systems, artificial intelligence, Fueled by ever-larger datasets and scalable cloud infrastructures, these boundaries are becoming more blurred and dynamic.
AI-based collaborative platforms enable businesses to work seamlessly with distributed communities, developers, creatives, and customers. In this scenario, the combination of open innovation and AI It transforms innovation into a much more distributed process, where human creativity intertwines with the computational capacity to analyze patterns, generate solutions, and simulate complex business scenarios.
Open Innovation and AI: Enhancement, Enabling, and Replacing
Dahlander proposes three lenses to understand how open innovation and AI are changing the nature of innovation: enhancement, enablement and substitution. From the perspective of the improvement, artificial intelligence works as an amplifier of human capabilities, automating repetitive and time-consuming tasks.
Analyzing customer feedback, monitoring social media conversations, exploring patent or scientific databases: all these tasks can be entrusted to AI models that operate on large volumes of data. Dahlander cites the example of LexisNexis, which uses AI algorithms to scan millions of documents in minutes, identifying patterns, trends, and opportunities that would be difficult for a single analyst to detect.
In this context, automation does not replace creativity, but frees up time for activities with greater strategic value: defining new product lines, experimenting with business models, designing more effective marketing funnels. The second lens, that of’enabling, shows how AI opens up new forms of collaboration between organizations that, until a few years ago, would have hardly been able to share data or insights.
An example is the Federated Learning, which allows different companies to train shared models without directly transferring sensitive data, maintaining local privacy. This way, different parties contribute to a common algorithmic resource without giving up control of their own databases.
In the music industry, IK Multimedia's TONEX platform uses AI to model rare amplifiers and complex sound chains, turning highly specialized technical skills into replicable digital assets. Here, open innovation and AI enable you to codify and distribute expert know-how through software products accessible to a global audience.
The third lens, that of the replacement, introduces the strongest discontinuity. Generative AI systems no longer simply support ideation, but directly produce ideas, concepts, names, texts, images, and prototypes. Dahlander observes that AI-generated ideas are often rated as more creative than those of human professionals, at least on some metrics of originality and combination of inputs.
In this scenario, the role of people is gradually shifting from pure creation to the selection, orchestration, and implementation of algorithmically generated proposals. Open innovation thus becomes a curation process, in which human teams and AI systems work together to filter, validate, and transform the best ideas into concrete projects.
New knowledge networks: hybrid ecosystems between open innovation and AI
The intertwining between open innovation and AI It also profoundly changes the networks through which knowledge circulates. Collaboration is no longer limited to select partners, but extends to global platforms, online communities, and marketplaces for AI models and datasets.
In this context, a real "hybrid world" is emerging, in which AI contributes to democratize innovation. According to Dahlander, innovation becomes accessible to anyone with an idea, regardless of technical background, because intelligent tools reduce barriers to entry and provide support during design, development, and testing.
AI-based platforms thus foster a new balance between individual and collective intelligence. Experiments become continuous, rapid prototyping is within the reach of even small businesses, startups, and creators, and knowledge flows more quickly through shared repositories, open APIs, and libraries of pre-trained models.
Dahlander points out, however, that as the power of security systems grows, artificial intelligence, attention to the human side of innovation must also increase. Without transparency on the use of data, the logic of algorithms, and the criteria for selecting ideas, open innovation models risk losing credibility with users, partners, and institutions.
Intellectual property management, particularly AI-generated content, becomes a central issue, as does the protection of sensitive datasets and the definition of shared responsibilities. From this perspective, open innovation and AI They require new governance arrangements between companies, end users, and regulators to balance the speed of innovation, the protection of rights, and ethical sustainability.
Towards a new culture of open innovation with AI
Dahlander's contribution opens a broader reflection on the relationship between humans and intelligent systems. AI is no longer just a support technology, but a true cognitive actor that participates in shaping creative work and strategic decisions.
In this context, the opposition between human creativity and algorithmic generativity is reductive. The challenge, rather, is to integrate the two dimensions in a complementary way, building processes in which data, intuition, and experimentation coexist. This applies both to research and to digital marketing, in service design as well as in operations.
Dahlander draws attention to three pillars that are becoming increasingly crucial in new ecosystems of open innovation and AI: trust, transparency, and collaboration. Without these cultural elements, technology risks generating mistrust and resistance to change, rather than enabling broader participation in innovation.
In short, the evolution of open innovation in the AI era is redefining not only the tools and processes of innovation, but also the very meaning of innovation. From the exclusive focus of internal labs, we're moving towards a distributed model, where networks of partners, communities, customers, and algorithms jointly build new products, services, and business models.
Open Innovation and AI: Impact on Marketing and Business
The impact of open innovation and AI Its impact on marketing and business is direct and profound. The same logic that's transforming research and development is changing the way companies design conversion funnels, automated campaigns, and omnichannel customer journeys.
Generative AI platforms enable marketing teams to quickly test variations of messages, creatives, offers, and segmentations, reducing experimentation time and costs. In a context of data-driven marketing, open innovation enables you to integrate insights from customers, partners, and external communities directly into your automated workflows.
The customer experience is also being redesigned: combining open innovation and AI, businesses can create virtual assistants on channels like WhatsApp, advanced chatbots, and recommendation systems that improve over time thanks to real user feedback. Every conversation becomes a collection of useful data to optimize products, services, and communications.
For growth-oriented companies, this scenario opens up three major opportunities. First, accelerate the innovation cycle, moving from ideas to market testing in much shorter timeframes. Second, build more personalized customer relationships, thanks to AI-powered conversational journeys on messaging channels. Third, scale initiatives. marketing automation without multiplying operating costs.
In this context, conversational platforms and messaging APIs become fundamental components to translate the principles of open innovation and AI into concrete business results, especially in the retail, e-commerce, financial services and professional services sectors.
How SendApp Can Help with Open Innovation and AI
To apply the principles of open innovation and AI For conversational marketing, companies need tools that combine automation, integrations, and advanced conversation management. SendApp was created precisely for this purpose: to bring the power of WhatsApp Business and AI into innovation and customer relationship processes.
With SendApp Official, businesses can use WhatsApp's official APIs to integrate intelligent chatbots, autoresponder workflows, and CRM systems. This allows them to transform every chat into a laboratory for continuous experimentation, in line with the logic of open innovation and AI: new scripts, new conversation paths, new offers can be quickly tested and optimized.
SendApp Agent It also allows for the management of teams and agents that work in synergy with AI. Human operators can focus on the highest-value cases, while bots handle repetitive microtasks, initial handover, and structured collection of key information. This approach is perfectly aligned with Dahlander's vision, in which humans move from pure execution to the selection and orchestration of solutions generated by intelligent systems.
For companies that want to go further, SendApp Cloud It offers a scalable infrastructure for advanced automation workflows, integrations with external systems, and centralized management of conversational data. This way, data collected via WhatsApp can fuel AI models, dynamic segmentation, and customer engagement strategies. marketing automation multichannel.
Integrate open innovation and AI In your communications strategy, it's no longer a theoretical exercise, but a concrete lever for increasing leads, sales, and customer satisfaction. If you want to understand how to apply these principles to your business, you can request a personalized consultation or start a trial of SendApp solutions for WhatsApp Business directly from the official website. sendapp.live.
