Business objectives
Examine the objectives the wider organisation has, as well as the department-specific objectives, and assess what AI can do to help achieve these goals. One of the key purposes of AI is to improve the efficiency of operations which directly converts into savings with time, staff and time to market. A CDO needs to examine how these individual aspects can not only be improved by AI tools, but how they directly link to business and team objectives. This is also a good exercise to check that business and team objectives are easily understandable, widely understood and even feasible.
For example, if a business objective is to reduce overheads, it can be quite straightforward to see how AI tools will be able to contribute to making this a success – AI can find efficiencies in operations, supply chains, vampire technologies and more. When the business objective is more nuanced or addressing something that is intangible, the ways in which AI can be beneficial become harder to spot. When it comes to staff wellbeing businesses may aim to meet the needs of the employees and stakeholders, through job satisfaction and personal growth – but how can personal growth be measured? What can be done to improve job satisfaction across all teams? AI can be used to examine and address inequalities in the workplace, discrepancies in daily operations and identify where specific training may be needed. In coalition, these AI uses can help improve intangible ideas such as individual satisfaction and job prospects.
Existing team skills
A major hurdle to vault is whether the skills to accurately and efficiently utilise AI tools currently exists in the business. It is well known that there is a shortage of data professionals for the amount of data roles that sit empty, but this is likely to get worse as businesses seek AI-capable employees. Therefore, CDOs need to examine who in the team is currently able to handle the implementation of AI tools and who would need additional training. Training existing staff is usually cheaper and faster than hiring new talent and much swifter to see results on.
Furthermore, investment in skills, training and highlighting employee value is beneficial to talent retention – which is another ongoing problem for the data sector. By pinpointing where investment in talent and training is required, the capability for a business to rapidly incorporate AI tools and efficiently implement them can be quicker than the hiring process. Staff are less likely to leave for similar positions at rival businesses if they receive attention, investment and training from a business as they feel more valued, and the longevity of their role is increased.
It can be appealing to business leaders to grow a team and have a larger number of staff, but the reality is that this is not necessarily the best move for organisations that are new to AI. With this in mind, CDOs need to support and promote their existing teams before fighting for funding to expand their team in order to satiate the desire for AI capabilities.
Trust in the data team
For a business to become data-driven, it first needs to be data-led, and this requires investment and trust in the data team. All too often business leaders can find themselves looking at something new and shiny and imagining it works seamlessly immediately, but this simply cannot happen with AI. The data teams need to be given time to examine, evaluate and plan for the introduction of AI tools, and this must be spearheaded by the CDO in collaboration with other department heads, such as IT.
CDOs need to explain the real-world processes of utilising AI tools with business executives with jargon-free storytelling and examples of how it will work and timelines. This can be achieved through improved data literacy levels and a strong data culture that permeates all areas of the business. The moment a data team has the backing of decision makers, the ways in which data can provide results can be felt almost immediately which will, in turn, further improve the standing of the data team in the organisation.
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