AI has the potential to significantly change the outcomes for businesses, governments, and society. But why are organizations right now adopting AI? What are the areas of focus now? We asked this exact question in a detailed survey conducted in November 2023. To many, the question may seem odd. After all, many technology companies are releasing AI products or products with enhanced AI extensions and are telling us that AI is booking. So, should everyone not adopt AI? Why ask this question at all? The reason is organizations adopt technology based on the level of maturity around that technology. AI is no different. So, understanding the reasons for AI adoption right now will indicate how to improve the organization’s AI Maturity. And technology providers have a vested interest in boosting the hype of AI. Organizations must think strategically about this and not react to hype.
The results may not be surprising, as they show that today, companies generally look at use cases for AI that focus on business optimization, improving and optimizing current business models while keeping their own business model the same. It is likely too early for many executives to formulate radical innovation and transformation approaches. However, CEOs and Boards must start to focus on the transformation skills of their leadership teams, both individual leaders and the composition of the leadership team.
Source: Reply. N=265. See Survey Methodology details later.
Each of these six areas shows that organizations are primarily internally focused and that the activities are about productivity and operational efficiency. This is common for significant technology shifts as organizations are still determining the second and third-order impacts of adoption and need more experience to understand what the transformative impact can be on their business models.
The survey demonstrates a very high level of involvement when looking into how senior executives are involved in adopting AI. This positive shows that organizations will soon get to scaling AI more broadly. A requirement for this to happen will be that the existing POCs run in many organizations prove their business cases. While many organizations have had AI-focused projects for years, 2023 saw an explosion in interest, leading to an increase in POCs in 2024. Proving the business cases in these POCs remains to be determined for overall AI adoption in 2024.
Many organizations seek guidance regarding applicable use cases for AI adoption in their organization and industry. Not all POCs will work out, but that should not determine the overall outcome of AI adoption in an organization. Therefore, organizations must adopt a more programmatic or portfolio management approach to managing potential POCs for AI adoption.
Source: Reply. N=265. See Survey Methodology details later.
The survey equally showed an interesting difference between the involvement of senior management and that of business unit management or mid-level managers regarding AI in the organization. Senior managers are significantly more involved than middle managers. While this is not unusual in the initial phases of a significant technology shift, organizations will not be able to scale the usage of AI if this persists. CEOs and Boards must sponsor and commence learning & development programs for managers around AI and ensure that annual objectives align with the strategic ambition of increasing the usage of AI. If not, the efforts to scale the usage of AI throughout the organization will stall.
Source: Reply. N=265. See Survey Methodology details later.
Doing so will help address the difference between executive management and business unit management when it comes to involvement in AI initiatives. Only when the two align will it be possible to scale the adoption of AI.
As with any digital transformation activity, the more involved the whole organization is with AI, the more managers are encouraged to develop their skills in AI, and the more a common language around AI and the business becomes pervasive, the more likely the organization is in rapidly improving the AI Maturity.
Our Recommendations:
Alignment around AI: As with all technology transitions, ensure that the objectives of managers within the organization, especially middle management, align with the strategic objectives regarding adopting artificial intelligence. Metrics and specific annual objectives can often encourage managers to implement any change. Objectives and metrics aligned with the strategic elements around AI will help support the scaling throughout the organization.
Continuous Leadership Improvement around AI: Even with a high level of maturity or knowledge of AI by senior management, leadership needs to improve continuously regarding AI's impact on business optimization and transformation. The maturity level is always expressed based on what the leadership knows now rather than understanding the endpoint for AI adoption.
AI Use Case Portfolio: Most organizations will adopt use cases from other organizations, use input from technology providers, or episodically survey managers about possible AI use cases. At best, the IT organization will manage possible use cases. While this is a way to start with AI, involving all managers in the identification and portfolio management of AI use cases is essential going forward. Centrally coordinating these use case opportunities and managing the outcome of AI better than we have done with previous technology transitions will be valuable for organizations.
Optimization versus Transformation using AI: Initially, organizations will focus on optimizing their existing business processes and business models by augmenting them with AI solutions. While optimization is critical, as outlined above in the need to demonstrate continuous improvement, executing projects that can lead to the transformation of the business model will become a requirement for all organizations. The understanding and leadership capabilities required to optimize versus transform are different. CEOs should ensure that their leadership team demonstrates both skills by developing all leaders or mixing different leadership capabilities within a management team.
The Survey Methodology: The survey comprised five open-ended questions regarding AI. The responses, all long freeform verbal answers, were collected through conversations between clients in Europe and the United States and senior partners of Reply. The responses were entered into a spreadsheet file. Using OpenAI’s GPT 4, specific prompts were written for all the analyses made on the answers. Human experts checked insights, ranking of the answers, and summarized results.
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