Here are five key strategies* for leveraging generative AI, offering guidance to leaders on fostering a culture where data and AI can thrive.
- Democratise data
One of the key benefits associated with generative AI is its capacity to make data accessible across the organisation. For instance, data architectures such as data intelligence platforms, which enable generative AI use cases to be more easily brought into production, allow users to query data with natural language. As the models behind these platforms are trained on organisations’ internal data, employees can easily access highly relevant results.
“Explaining the tangible benefits that generative AI can offer to employees across departments is the first step in achieving workforce buy-in,” comments Dael Williamson, EMEA CTO, Databricks. “By responding to user needs for greater data access, organisations can empower their workforce to leverage generative AI effectively across various functions. This is the rise of generative business intelligence.”
- Implement strategically
Democratising access to generative AI does not always mean encouraging every employee to use the technology from day one. Organisations should still consider their level of data and AI maturity, making strategic choices about which teams have the right skills for generative AI and where there is the greatest need.
“For some organisations, it is most logical for a core data science team that is aligned with the different business units to handle initial generative AI rollout. However, for others, a decentralised approach, where different business units are empowered to pursue data and AI initiatives independently, may be better. Every organisation must ensure that their generative AI deployment strategy is aligned with their broader business objectives and priorities,” comments Robin Sutara, Field CTO, Databricks.
- Communicate effectively
Effective communication is at the heart of driving a successful data and AI strategy. Any change in how people work requires that employees understand why the change is happening and its impact. To fulfil this need, details of the organisation’s generative AI roadmap should be shared on multiple channels, frequently, and over a long period of time.
“Leaders must articulate the strategic importance of AI, share best practices, and provide avenues for feedback to ensure widespread understanding and support. Communications should also be tailored around how different employees will get value from data and AI – sales leaders may want to focus on lead generation, whereas recruiting teams will focus on candidate acquisition,” continues Sutara.
- Anticipate resistance
Resistance to change is common, especially when introducing new technologies. Leaders can anticipate and address any scepticism by allowing employees to gradually acclimate to new processes. In doing so, people can discover the value of generative AI for themselves.
“Demanding every employee immediately adopt a new application or process is a quick way to fail,” continues Williamson. “Leaders should regularly evangelise the advantages of a new technology, but also give time for employees to access specific value from its use. In doing so, adoption, experimentation, and improved business results will come.”
- Encourage upskilling
Employees are never done learning, especially as the pace of technological change continues to accelerate. Establishing a culture of continuous data and AI upskilling yields immediate benefits for the individual, as well as laying the groundwork for sustained adoption in the long term.
“Offering employees the time to learn about generative AI is crucial. Organisations could even use an AI model to turn one learning concept into many different outputs, such as a video, blog, or infographic, that resonate with different audiences. These materials should be shared with employees in every department, at every level, to democratise data skills across the organisation,” concludes Williamson.
Expanding data and AI initiatives, like generative AI projects, throughout an organisation can pose significant challenges for leadership and IT teams. However, with careful planning that considers both the AI infrastructure and organisational requirements from the outset, businesses can integrate data and AI more smoothly across all facets of their operations, ultimately achieving implementation success.
*Guide from Databricks
