The Developing level represents a transitional stage in the AI maturity framework, where organizations have begun to recognize the potential of AI and are taking initial steps to integrate it into their operations. While progress is being made, efforts are still somewhat fragmented and lack the coherence and structure seen in more mature organizations.
Key Characteristics
■ There is an increased understanding of AI’s potential among leadership and key stakeholders.
■ AI is beginning to be viewed as a strategic priority, but understanding of specific applications may still be limited.
■ Organizations may start developing a formal AI strategy, but it is often not yet fully realized or widely communicated.
■ Some departments may have individual plans for AI projects, leading to inconsistencies in implementation.
◉ Emerging Internal Expertise
■ Teams may start building internal capabilities through training, hiring data scientists, or partnering with external experts.
■ Cross-functional teams may be formed to work on specific AI initiatives, but collaboration is not yet fully optimized.
◉ Data Management Improvements
■ Initial efforts to collect and manage data more systematically are underway.
■ Organizations are beginning to understand the importance of data quality and integration, although challenges remain.
◉ Pilot Projects and Experimentation
■ A number of pilot projects are initiated across various departments, allowing teams to experiment with different AI technologies.
■ Some projects may show promise and positive results, but there is still a lack of widespread adoption.
Common Challenges
■ Organizations may face budget constraints that limit the scale and number of AI initiatives.
■ Competing priorities can divert attention and resources away from AI projects.
■ While some governance structures may exist, they are often informal or inconsistently applied.
■ There may be no established metrics to evaluate the success or impact of AI initiatives.
◉ Data Silos
■ Despite improvements, data is often still trapped in silos, making it difficult to access and integrate across departments.
■ Organizations may struggle to develop a unified data strategy.
◉ Cultural Resistance
■ Employees may still be hesitant to embrace AI due to fears of job displacement or a lack of understanding of its benefits.
■ Change management efforts may be insufficient to foster a culture that supports AI adoption.
Activities Typically Found at the Developing Level
■ Organizations begin to run more structured pilot projects, often with defined goals and metrics for success.
■ These projects may involve using AI for specific tasks, such as predictive analytics or automation.
■ Training initiatives aimed at building AI skills within the organization start to take shape, targeting both technical and non-technical staff.
■ Workshops, seminars, and online courses may be offered to improve AI literacy.
◉ Data Strategy Development
■ Efforts to create a comprehensive data strategy that focuses on data quality, governance, and integration begin to emerge.
■ Organizations may start implementing data management tools and platforms.
◉ Interdepartmental Collaboration
■ Cross-functional teams or working groups are formed to share knowledge and best practices related to AI.
■ Collaboration between IT, data science, and business units begins to increase.
◉ Engagement with External Partners
■ Organizations may engage with external consultants or technology vendors to gain insights and support for AI initiatives.
■ Collaborations with academic institutions or industry groups may also begin to take shape.
Recommendations for Progression
To advance from the Developing level, organizations should consider the following strategies:
■ Finalize the AI strategy and ensure it is communicated clearly across the organization.
■ Align AI initiatives with broader business goals and objectives.
■ Implement formal data governance frameworks that define roles, responsibilities, and standards for data management.
■ Invest in data integration technologies to break down silos and improve accessibility.
◉ Expand Training and Development
■ Scale training programs to reach more employees, including leadership and operational teams.
■ Establish mentorship programs to foster knowledge transfer and skill development.
◉ Implement Metrics and Evaluation
■ Develop key performance indicators (KPIs) to assess the impact of AI initiatives and track progress over time.
■ Regularly review and adjust strategies based on outcomes and lessons learned from pilot projects.
◉ Cultivate a Supportive Culture
■ Foster an organizational culture that encourages experimentation and innovation.
■ Communicate success stories related to AI initiatives to build enthusiasm and support among employees.
By focusing on these areas, organizations at the Developing level can create a more structured approach to AI adoption, positioning themselves for greater maturity and effectiveness in leveraging AI technologies in the future.