Opening an innovation process also creates additional management work. Every external technology, startup or partnership opportunity has to be assessed. Someone must determine whether it supports the organisation’s strategy, which business problem it addresses, what evidence is required and who will own the next step.
As external knowledge becomes easier to access, the ability to select and mobilise it becomes more valuable. Competitive advantage increasingly depends on how quickly an organisation can recognise a relevant opportunity, connect it with internal priorities and translate it into action.
Innovation Strategy Has to Survive Contact with the Operating Model
The relationship between strategy and execution emerged as another central concern. Many organisations already include innovation in their strategic plans. The difficulty appears when broad ambitions have to be translated into measurable objectives, budget allocation, management responsibilities and day-to-day decisions.
Innovation asks managers to explore opportunities whose outcomes remain uncertain. Existing performance systems often reward efficiency, predictability and delivery within established business models. When operational targets are precise,and innovation objectives remain broad, resources naturally move towards the activities with clearer accountability.
This creates an important test for any innovation strategy. Can managers explain which types of innovation the organisation is seeking, what level of uncertainty it is prepared to accept and which evidence is required before additional resources are committed?
One question raised during the session exposed the ambiguity that often surrounds innovation: would a product becoming 10 per cent cheaper qualify as innovation?
The answer depends on the strategic context. A substantial cost reduction may strengthen an existing business model, create access to a new customer segment or enable a different value proposition. In another context, the same improvement may represent routine optimisation. The management challenge begins when teams use the same term while applying different assumptions about novelty, value and risk.
Organisations therefore benefit from a clearer portfolio logic. Improvements to the core business, adjacent growth opportunities and more transformative initiatives serve different purposes. They also require different time horizons, funding mechanisms and evaluation criteria. Treating every initiative in the same way either exposes the organisation to unnecessary risk or removes the space required for meaningful experimentation.
AI Is Becoming a Test of Organisational Readiness
Artificial intelligence appeared throughout the working discussions, although the most consequential questions concerned organisational capability rather than individual tools.
Participants discussed the availability and quality of data, access rights, analytical capabilities, return-on-investment calculations and the role of external partners. Healthcare organisations highlighted persistent challenges around data sharing and public-private collaboration. Several groups identified shortages in analytics and big-data competencies.
These issues illustrate a wider transition in AI adoption. Awareness is already widespread. The next stage will depend on whether organisations can connect technological possibilities with business priorities, governance structures, relevant data and measurable value.
AI also exposes weaknesses that may remain less visible in conventional innovation projects. An AI initiative can involve business units, data owners, technology teams, cybersecurity specialists, legal experts and external providers at the same time. Fragmented ownership, unclear decision rights and weak coordination quickly become implementation barriers.
The strongest initiatives begin with a business decision, operational process or customer problem. The organisation can then work backwards to identify the required data, technology, skills and governance conditions. This sequence creates a clearer basis for evaluating potential partners and selecting appropriate tools.
Growth Provides Direction, while Evidence Builds Credibility
Prof. Henry Chesbrough brought the discussion back to the strategic purpose of open innovation: growth. External knowledge and collaboration should strengthen an organisation’s capacity to create value, develop new opportunities and adapt to changing market conditions.
He also emphasised that performance indicators need to support organisational change. Long-term growth objectives often require evidence that can be demonstrated much earlier. Well-selected short-term indicators build credibility, support internal commitment and create the confidence required for larger strategic investments.
This has implications for how innovation ecosystems evaluate their own contribution. Participation levels, community size, events, partnerships and project numbers provide useful signals of activity and reach. They reveal little about whether organisations have become more capable of making and implementing innovation decisions.
Without these elements, innovation becomes vulnerable to changing priorities, unclear responsibility and organisational fatigue.
Stronger measures examine movement through the innovation process. How quickly does a business challenge reach relevant expertise? How much time passes between identifying an opportunity and beginning an experiment? How many externally sourced ideas progress into pilots? Which uncertainties were reduced? Did the collaboration influence an investment decision, create a new capability or open a credible route towards growth?
These measures are more demanding because they focus on consequences. They also provide a more useful basis for improving the ecosystem itself.
