AI Maturity: An Organizational Guide to AI-Powered Transformation

As the transformative power of generative AI reaches new heights, the technology is transforming the way almost every organization around the world works, collaborates and innovates.

However, adoption and progress have been uneven, and two cohorts are emerging: those organizations that have stalled in adopting AI tools, versus those that are making progress in realizing the opportunities. According to a recent BCG survey, only 10% of organizations are scaling AI to one or more business functions, while 40% of organizations have taken no action. The BCG research shows that the benefits are clear, as those organizations that are further along in AI adoption see 2.6x higher revenue growth and 38% in EBIT growth over three years, as well as substantial increases in market share and customer satisfaction. satisfaction.

There are several ways in which AI can be applied to simplify and streamline parts of the daily work process, and to support creative content creation and complex problem solving. However, to truly leverage these new capabilities, organizations will need to rethink traditional ways of working and adapt to new paradigms to achieve the business impact mentioned above.

Through our conversations with leading enterprise clients, countless analyst briefings, and our own independent research, we’ve found that organizations are exhibiting a common set of characteristics as they mature their understanding and adoption of AI. By understanding the indicators, incubators, and inhibitors, organizations can accelerate their path to realizing the benefits of AI. Let’s walk through these phases to boost the way organizations deploy AI.

Phase 1: Exploration

The exploration phase, known as the nascent phase where organizations begin to understand what AI is and how it can be applied within their context, is entrepreneurial and opportunistic.

For any organization, building a strong foundation with AI emphasizes educating the team on the basics of AI and machine learning, and strategically addressing the existing
IT infrastructure and data, responsibly focusing on forward momentum and evaluating existing policies. This approach ensures that AI integration aligns with your established data security, privacy and ethical protocols, minimizing risk and maintaining regulatory compliance.

By basing AI initiatives on trusted governance practices, organizations will have the opportunity to create a solid foundation for responsible AI deployment, while facilitating smoother transitions and promoting trust among stakeholders. It means you can also identify gaps or necessary adjustments in policies to better accommodate AI technologies as they evolve.

Phase 2: Experimenting

At this stage, organizations can start experimenting with AI technologies to upskill their teams and put knowledge into practice. It is a good idea to conduct pilot projects and proof-of-concept initiatives, encouraging targeted use of AI to address specific opportunities. These user tests will also help teams gain first-hand experience working with AI, aligned with their company’s policies and governance in mind.

Some processes you might consider trying include:

1. Enhance AI ‘champions’ with critical knowledge – these are the key employees who will form the core AI team, provide support to all departments and ensure company-wide alignment

2. Fund targeted AI applications to address specific challenges or opportunities. By being selective and prioritizing key projects, you can scale the AI ​​implementation and ensure it is fit for purpose.

3. Establish or brief the governance team to identify risks, ensure data integrity, and promote accountability on AI projects.

Communication is critical at this stage, so ensure teams and pilot project groups are aligned and led with a central vision or set of objectives. This could include streamlining customer communications across the organization so that there is full transparency into evolving changes and the integration of AI into the business, while highlighting how this will benefit the business and the services provided.

Phase 3: Innovation

The innovation phase is perhaps the most exciting. This is when the basics and testing are put to good use. Here, organizations can consider establishing new AI roles and reskilling team members, upgrading existing infrastructure to support long-term AI adoption, redesigning the work process, and continuously monitoring and updating policies work as soon as new processes are officially brought online.

This phase will also demonstrate the tremendous benefits and learning development opportunities that come with AI adoption. A comprehensive reskilling program is essential to equip existing employees with the knowledge and skills needed to work effectively with AI technologies.

It is also worth investing in some high-performance computing resources, cloud computing platforms, and advanced data storage solutions that can handle the increased processing demands of AI workloads.

Phase 4: Realization

This next phase is about fully integrating AI into decision-making and operational processes to unlock new opportunities, drive innovation and maintain a strong competitive position in the market. Here everything learned in the previous phases is formally embedded in the daily way of working.

As in the innovation phase of employee reskilling, organizations here must ensure that their employees are fully equipped with the technology and leadership skills needed to leverage AI technologies. This means ensuring that all employees have the skills to adapt to the new ways of working, by benchmarking skills, identifying skills gaps and implementing training programs to address them accordingly.

Organizations should also evaluate the consolidation or retirement of legacy infrastructure and tools, scale work processes across all business functions and units, and enable the governance team to actively monitor progress and update policies.

Overall, as your organization begins to adopt AI enterprise-wide, it’s important to remember that it’s not just about using AI to make things faster and more efficient, but about leading with empathy and understanding. When we as leaders focus on emotional intelligence, we create work environments where both people and technology can thrive.

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