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Four management capabilities for successfully operationalizing open innovation are: strategy and culture, ecosystem capability, internal capability and technology enablement. Surveying more than 1,000 executives on current open innovation practices and capabilities, IBM IBV was able to identify how the different operating model capabilities interact and complement each other to drive better innovation and business performance.

To help organizations build and improve their open innovation capabilities, the IBM Institute for Business Value (IBV) partnered with APQC to develop the Ecosystem-Enabled Innovation Maturity Model (EEIMM) - an open standards model encompassing four domain competencies required for successful open innovation. To assess the maturity and benchmark the performance of organizations’ open innovation capabilities, the IBV, in collaboration with Oxford Economics, used the EEIMM to survey over 1000 leaders responsible for open innovation at their organizations.

Four management capabilities for successfully operationalizing open innovation are: strategy and culture, ecosystem capability, internal capability and technology enablement. IBV analysis found that organizations that are more advanced in developing the four building blocks see significantly better performance across key financial and innovation metrics.

For every dollar of investment, the proportion of direct revenue attributed to open innovation is four times higher than for traditional innovation.

Leading organizations are embracing open innovation as a critical component of innovation strategy and investment. They recognize that adopting open innovation yields far greater returns than traditional innovation can. Recent research by the IBM Institute for Business Value (IBM IBV) reveals, for example, that as many as 84 percent of executives now view open innovation as important for their organization’s future growth.

Most recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation.

Findings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000.

1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovation

We are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure.

Business application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments.

To alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial.

This research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain.

To better understand the role of creativity and its impact on business, IBM Institute for Business Value (IBM IBV) surveyed 400 United States-based CEOs across 11 industries in 2022.

Average organization size of these businesses was a little over $26 billion in annual revenue. The survey covered a range of questions on the role and impact of creativity in their organization. Then survey data was analyzed to reveal the key drivers and success factors of creativity.

When the CEOs were asked to describe an organizational structure most conducive to perpetuating creativity, as many as one quarter of them identified a hierarchical structure – a structure typically considered antithetical to free-flowing creativity, and one highly susceptible to micro-management and bureaucracy.

CEOs identify fear of failure as the most significant barrier to creativity.

Tapping into ingenuity and creativity has been critical to navigating recent market disruption – and going forward, effective creativity is becoming even more central to business success. Study identifies best practices of leading organizations that believe that. creativity is not a skill solely for a few selected innovators and makers, it’s a quality required across the workforce.

As businesses in a wide range of industries increasingly adopt digital ecosystems, the benefits include opening up new and untapped customer segments and channels, enabling access to new and untapped talent pools and expanding previously unexplored modes of innovation. Ecosystems – defined as digitally enabled networks that enhance value propositions by linking business functions, suppliers, distributors, partners, customers and other stakeholders – are now an engine that drives performance. Multiple studies confirm that value creation and competitive advantage are increasingly tied to organizations’ ability to engage partners and stakeholders. IBM Institute of Business Value’s most recent research has found that revenue growth of ecosystem leaders outpaces others by a five-to-one ratio.

Yet despite these notable successes there is also a dark side to digital ecosystem engagement. Large ecosystems have become a central to business strategy while little attention has been paid to the potential for cyber threats. Increased openness of business operations can result in greater risk.

Security must graduate into a central enabler of business transformation. If businesses do not fundamentally rethink their security equation, ecosystems and the trust on which they are built, which often take years and billions of investment dollars to build, can be squandered in minutes by ever-growing security threats.

Large ecosystems have become a central to business strategy while little attention has been paid to the potential for cyber threats.

Mature, risk-mitigated ecosystems are starting to resemble a mature supply chain, where some risks are precluded by design, some partners are favored based on established trust criteria and remaining risks are explicitly managed transactionally as part of the partner relationship.

Since the release of ChatGPT by OpenAI in November 2022 – with its ability to create compelling, relevant content, new large language model (LLM) technology – business leaders, especially CEOs, are being pressured to accelerate new generative AI investments. IBM IBV surveyed executives to assess their progress and concerns and their adoption strategies.

Adoption of generative AI is still in its very early stages. Most organizations are only beginning to figure out how and where to make use of it. In fact, as few as 6 percent of executives in new surveying conducted by the IBM Institute for Business Value say they are operating generative AI in their enterprise today.

In contrast to many peoples’ expectations about AI, automating tasks is not the top priority for executives looking to tap generative AI to grow business value. Looking at benefits by function, research and innovation is the primary area where organizations see opportunities for generative AI.

IBM IBV's recent survey of executives found that the key barriers to the effective deployment and use of generative AI are linked to security, privacy, ethics, regulations and economics – not access to the underlying technology itself.

Organizations will have to evaluate where in their enterprise the potential gains and cost efficiencies outweigh the risks of possible errors or unintended consequences from the use of generative AI along with broader ethical considerations. Ecosystems expand generative AI opportunities to harness data, insights and technology capabilities from across partners and stakeholders while enabling control over the capabilities that are most central to an organization’s value proposition.