As the transformative power of generative AI reaches new heights, the technology is transforming the way nearly every organization in the world works, collaborates, and innovates.
However, adoption and progress have been uneven, with two cohorts emerging: those that are stuck in adopting AI tools versus those that are making progress in realizing the opportunity. According to a recent BCG study, only 10% of organizations are scaling AI into one or more business functions, while 40% of organizations have not taken action. The BCG study shows that the benefits are clear, as organizations that are further along in AI adoption see 2.6x revenue growth and 38% EBIT growth over three years, as well as significant increases in market share and customer satisfaction.
There are several ways AI can be applied to simplify and streamline parts of the daily workflow, and to support creative content creation and complex problem solving. However, to truly capitalize on these new opportunities, organizations must rethink traditional ways of working and adapt to new paradigms to achieve the business impacts noted above.
Through our conversations with leading enterprise customers, numerous analyst briefings, and our own independent research, we’ve discovered that organizations share a common set of characteristics as their understanding and adoption of AI grows. By understanding the indicators, incubators, and inhibitors, organizations can accelerate their path to realizing the benefits of AI. Let’s walk through these stages to improve how organizations implement 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 begin experimenting with AI technologies to improve their teams and put knowledge into practice. It’s a good idea to run pilot projects and proof-of-concept initiatives to encourage targeted use of AI to address specific opportunities. These user tests also help teams gain first-hand experience with AI, aligned with their company’s policies and governance.
Some processes you might consider include:
1. Empower AI ‘champions’ with critical knowledge – these are the key employees who form the core AI team, provide support across departments, and ensure alignment across the organization
2. Fund targeted AI applications to address specific challenges or opportunities. Being selective and prioritizing key projects will help you scale your AI implementation and ensure it’s fit for purpose.
3. Assemble or brief your governance team to identify risks, ensure data integrity, and drive accountability for AI projects.
Communication is key at this stage, so ensure teams and pilot project groups are aligned and guided by a central vision or set of objectives. This may include streamlining customer communications across the organization, ensuring there is full transparency about the evolving changes and integration of AI into the business, while highlighting how it will benefit the business and the services provided.
Phase 3: Innovation
The innovation phase is perhaps the most exciting. This is when the foundation and testing are put to good use. Here, organizations can consider establishing new AI roles and retraining team members, upgrading existing infrastructure to support AI adoption in the long term, redesigning the workflow, and continuously monitoring and updating policy work as new processes are officially brought online.
This phase will also demonstrate the enormous 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 cof 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 into the daily way of working.
Similar to the innovation phase of reskilling employees, organizations here need to 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 empower the governance team to actively monitor progress and update policies.
Overall, it’s important to remember that as your organization begins to adopt AI across the enterprise, it’s not just about using AI to make things faster and more efficient, it’s also 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.