While most of the discussion around AI in the workplace focuses on the potential for automating certain jobs, there are even more cases where it will require a rethinking of roles. This transformation must begin even before AI tools are introduced into an organization, as the demands of effectively implementing these solutions will require new approaches. Organizations must rethink their talent management strategy to account for the new skills needed to understand AI use cases, properly deploy AI tools, and drive systemic change across the enterprise.
What AI Talents Will You Need?
Keith Collins, CIO of technology company SAS, said Four main types of AI talent are needed to build a high-performance team to oversee AI projects. This involves:
- Someone who understands the business processes that will be involved in achieving the desired concrete results.
- Someone who understands machine learning, statistics and analytics and can ensure the right techniques are considered.
- Someone who understands how business data is produced and why it is used, as well as the quantity and quality of data available within your organization and from external sources.
- Someone who understands the AI and machine learning architecture needed to create the desired business outcome.
In some organizations, a single person may hold multiple roles, and in others, a role may be spread across an entire team. The important thing is that the company can trust their expertise in each of these areas.
Conducting an AI-centric talent audit
Once you’ve identified the role of AI in your business and defined your desired outcomes, you need to align your talent management strategy with the desired transformation. The first step is to examine your organization to identify employees with the skills needed to manage the integration, launch, evaluation, adjustment, and ongoing monitoring and maintenance of the system.
Ram Narasimhan, global head of AI and cognitive services at Atlanta-based consultancy Xebia, identified a set of Key roles required for an effective AI teamNot all organizations necessarily need all of these functions, but it’s worth considering each role in the context of an organization’s needs.
- AI or Technology Lead: This is a senior technical manager or other senior executive who oversees the project and is responsible for ensuring that the AI solution remains aligned with the company’s strategic mission. The person in this role must understand the rapidly evolving field of AI well enough to guide other executives on the opportunities offered by current technology. They must also be able to present effective business cases for investments and serve as a bridge between technical staff and non-technical executives.
- Business Analyst(s): People in this role translate the business needs, processes, and limitations to be incorporated into the AI solution, first reviewing the project guidelines to understand how they will impact different areas of the business. People may be added to play this role at different phases of an AI project, with the primary goal being to help translate their business challenges into tasks for the AI team. For example, even if a call center manager doesn’t know the data attributes of the systems they use, they can explain the problems they face every day.
- Data Engineers and Scientists: The success of AI applications depends on the quality of the data and the algorithms it contains. People in these roles will define how data fits into the overall AI process, and will focus on extracting information from multiple sources and developing it into algorithms that can help make business decisions. Much of their time may also be spent cleaning the data, such as detecting and removing duplicates, blanks, and anomalies. These roles will typically benefit from programming skills and a background in data science.
- IT/Systems Architects: This role reinforces the work of engineers and data scientists to integrate AI projects into the organization’s existing technology. Typically, this will be someone with an IT background focused on systems integration and understanding existing technology, with a working understanding of AI models and the corresponding IT requirements.
- Systems Developers/DevOps: They work with data engineers, data scientists, and IT architects to ensure their solutions can be transformed into viable technology. They have also likely worked with traditional programming languages such as Java and C++.
- User Experience/Marketing Experts: A person with marketing or digital experience who understands the demands of consumers and other end users of the organization. Marketing experts are usually invaluable in ensuring that the project delivers the right products or services, whether to internal or external customers. While the technical team can create a brilliant innovation, it must also work for the customers or employees who are supposed to use it.
- Experts in the field/subject: These team members bring their understanding of each phase of the business domains in which the AI project will be deployed. For example, if an algorithm is to be used to make a decision about a product, it must be validated by a product manager to ensure it makes sense in practical terms.
- Risk and Compliance Experts: These team members analyze AI projects to ensure they do not violate any industry or government guidelines.
By identifying gaps in your organization, you can begin to see where investments will be needed. But increasing your headcount isn’t your only option.
Finding AI expertise: train, hire or outsource
Once you’ve assessed the potential costs and the type of budget your organization can dedicate to launching an AI initiative, you can determine which roles can be filled by existing employees, whether to retrain existing employees or hire new people with the right technical skills, or whether these functions should be handled by consultants or vendors. For small or non-technical organizations, the best approach might be to purchase a ready-made solution or to engage a software as a service (SaaS) provider to manage the project.
Whether your organization is large and tech-savvy enough to implement your own AI program or you outsource the project entirely, you’ll also face the prospect of training your current workers and building employee buy-in.
The first step is to educate people in the organization about what AI is and isn’t, what it can and can’t do, and how it’s already being used. Share examples of how other organizations’ use of AI has improved productivity, reduced tedious tasks, improved internal functions (like answering HR questions or generating training recommendations), and otherwise contributed to the workplace.
But, like any new technology or company-wide change, implementing AI will require acquiring new skills, adapting existing procedures, and simply training (if necessary) on how to work with AI. It is worth emphasizing to employees that training and acquiring knowledge about AI and aspects of machine learning can enrich their skills, make them more valuable and competitive in the market, and open up new opportunities for career development.
Tips for training your team on AI tools
Stephen Chen, CTO of NuCompass Mobility Services, a global move management company, recommends three steps businesses can take to train current employees in AI:
- Train your employees on the technological changes that AI will bring and on creating a positive culture around technology.
- Prepare your employees for AI reskilling and upskilling by encouraging a “growth mindset” in which they learn to meet challenges.
- Identify AI use cases for automations that are worthwhile, and eliminate those that are not.
In a survey According to a study conducted for employee training software company TalentLMS, 49% of U.S. employees say they would need training to effectively use the widely used ChatGPT program and similar tools. Of the remaining respondents, 23% said they did not need training because the AI tools were easy to use, and 14% said they did not intend to use the AI tools at all.
Of course, with any change comes some fear, which should be addressed during training. Emphasize the ethical and other safeguards your AI deployment team has in place, the work of your AI ethics committee (if your organization creates one), and the relevant AI ethics training the organization provides.
The specifics of on-the-job training will depend on whether the organization develops its own complete AI solutions, outsources part of the project, or manages the entire process with an off-the-shelf AI solution. Sometimes, vendors can provide training on the specifics, external training programs can be added, or the organization can create its own internal training program. AI considerations and specifics will require HR leaders to be the primary drivers of the discussion in consultation with other leaders.
Remember, the process of re-evaluating your talent management strategy is not a simple setup. This is a rapidly evolving field, and you’ll need to continually update your plan as new use cases emerge and new skills become essential.
