Key considerations for successful AI adoption to drive business outcomes are as follows:
Identification, adoption, and prioritization of AI use cases
In the last few years, more and more organizations are committing to AI programs to drive business value. That being said, it is essential to align with business goals and identify AI use cases. It becomes crucial to carefully evaluate where AI can really bring about additional value for the business and prioritize the use cases accordingly.
Standard frameworks should be leveraged to carefully evaluate the state of data, fitment of business goals, technology readiness of the organization and assess RoI before prioritizing the use cases.
Stakeholder involvement
Working with stakeholders to understand their business goals to align the overall roadmap for AI for their respective business span. Agreeing with stakeholders on KPIs to evaluate the success helps setup right expectations. Â Businesses want to keep ahead of the competition and use cases can be a solid tool for showcasing the benefits of AI and translating AI- and data-led decisions into digestible information.
It can be easy to get swept up in the hype, but data leaders need to demonstrate that AI is not a magic cure for all business ailments and that its success can take time to prove. Data leaders need to be prepared for the board and leadership to be a combination of nervous, excited, and inquisitive. One data leader explained how their data team started with a simple proof of concept project to win over the interest and enthusiasm of stakeholders.
Enabling stakeholders to manage expectations of board members and leadership team is important to secure investment for the long term AI program. Identify and quantify the tangible benefits of the AI investment, such as cost savings, increased revenue, time saved, or improved quality. This may involve calculating potential revenue increases or operational efficiencies. Additionally, consider intangible benefits such as improved customer satisfaction, enhanced decision-making capabilities, and increased employee morale. Compare results against industry benchmarks or similar organizations to assess performance and validate the effectiveness of AI investments.
There will likely be questions examining things such as what the future organisational structure would look like, and how rapid the return on investment will be for different departments. These questions can be difficult to answer, and data leaders need to effectively communicate with business decision makers to keep expectations in check.
AI governance and regulation
Organizations should create a council to govern the use case identification and prioritization, development and deployment of AI use cases in the business workflows and measure them regularly. The council should have a complete view of the progress of the AI program and it should be empowered to iterate the plan to align with the business objectives.
As new government policies get enacted on the use of AI, a prompt response in terms of preparedness and implementation is important. AI regulation has been an ongoing topic and further changes are expected across different business regions as governments slowly start to catch up with designing these regulations. For example, in the US it has been mandated that all governmental agencies now require a CDO. The changes can have repercussions on the overall data governance which provides management and oversight of data within an organisation.
Training to utilise new AI
As new AI developments become a day-to-day tool for even non-data professionals, the data culture and literacy of the organisation will become stronger, which means new hires need to be trained rapidly to match the new baseline levels of literacy. The training will continuously need to evolve and be updated to ensure each department is extracting the most from the investment.
To achieve this, data leaders need to ensure strong communication with other department heads, stakeholders, and teams to understand their needs and where they require support. One data leader explained how they had seen more than 300 executives in their organisation be trained on AI – including the board – and they all now understand the business terms and glossary of AI.
Training should be meticulously personalized based on the specific business span’s requirements. For example, the operations department will have drastically different AI need to the sales department and this needs to be highlighted. Training also needs to include sections on importance of AI and its relevance for their specific business use cases. This enables swift acceptance and lower resistance for AI adoption.
A crucial step toward fostering human and AI culture within an organization is to keep the AI solutions human-centric. This enables the engagement of the users to adopt AI solutions faster and become evangelists besides further contributing to new and relevant business use cases. Â
Ultimately, there is a lot of groundwork that needs to be laid by data leaders and their teams before a successful AI adoption and change can take place. The keys to success here is communication, clarity, and consistency, which are all achievable for any business at any stage of its data evolution. By taking methodical and steady steps, data leaders can ensure that their organisations will benefit as much as possible from new AI tools while simultaneously improving their wider data literacy and culture.
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