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Byline by Chris Stephenson, alliant Managing Director of Intelligent Automation & AI
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As businesses worldwide race to integrate AI into their operations the biggest roadblock may not be a tech issue but a hiring one.
The majority of employees lack confidence in how to use AI, and only a third of managers feel equipped to support AI integration, according to a study from EverythingDisc. Consequently, there’s been a surge of AI-skilled hiring across industries, with over 66 percent of business leaders asserting they would not hire someone lacking AI skills, according to a report from Microsoft.
What Is an AI Explorer Team?
An AI explorer team is a unit made up of individuals from different teams who are open to, and familiar with AI and automation. Their role is to investigate areas in which AI can drive effective operational changes and then partner with technical experts to design a solution. The team’s multidisciplinary background allows it to take a more holistic approach to AI transformation.
However, a successful AI-led transformation hinges on more than just rapid hiring. Businesses that are only focused on quick deployment to outpace competitors are likely already feeling AI fatigue as pilots crash and clear business use cases fail to materialize.
Companies willing to invest in personnel that can fully explore the scope of AI and the potential for their business are much more likely to find lasting value.
While your CTO and CIO are crucial players, the data you need to make the highest impact often sits with another executive: your Chief Human Resources Officer (CHRO).
Businesses need to strike a balance between achieving immediate objectives and fostering an environment of exploration and innovation. This is where hiring and building the right AI teams becomes crucial. So, how can companies assemble the right teams to drive successful AI transformations?
Why AI Tiger Teams Aren’t Enough
A business needs two distinct units for successful AI implementation. The problem is that many only build one unit without the other.
Usually, the only unit deployed consists of a small, elite group of internal experts renowned for their deep technical knowledge and swift problem-solving skills, often called a tiger team. These units are called to tackle a specific situation, and given near carte blanche to deliver immediate, high-impact results.
Most businesses build tiger teams to launch AI pilots as fast as possible, so they won’t fall behind competitors. Tigers are easier to find; just look for technical experts with a strong determination to get things done. What businesses are discovering is that many of their pilots haven’t panned out and it’s souring them on AI technology as a whole. They’re finding that a tiger team’s rapid approach may be good for quick results, but it’s inadequate to find AI solutions aligned with, and scalable to the business’ wider goals.
The truth is it’s not necessarily the tiger team’s fault, they were just following orders to stand up a solution quickly. Businesses need tiger and explorer teams to work in conjunction, but explorers can be harder to find internally.
The Case for AI Explorer Teams
Unlike the tiger team, the explorer team is multi-disciplinary, collaborative and agile. They’re able to experiment with diverse ideas, explore novel use cases, and pivot strategies whenever required. Their role in AI transformation is to take the time to thoroughly understand the business landscape and work across different departments to identify areas where AI can drive effective operational changes.
Ideally, the explorer team will be made up of members from each department to take a birds-eye view of the business and make strategic decisions on deploying the technology. While they can be used to tackle a single project to start, the explorer team should be formed with the intention that they will be continuously working helping the organization grow with AI.
Day-to-day, members of the explorer team will liaise with different areas of the organization to probe how AI could best enhance their work, plan new AI projects and collaborate with the tiger team to implement and scale them. Both teams should fit into the overarching AI steering committee responsible for deciding how AI gets implemented across the organization.
By fully understanding the capabilities of the technology and engaging with various departments, explorer teams will uncover opportunities for AI to enhance efficiency and innovation and find unique solutions that few had previously considered.
For example, we ran workshops with several accounting firms that were deploying AI solutions to solve specific tasks, such as automating collecting client documents. What we found during the discovery process with our explorer team was that many of these firms were using AI to bridge the gap between disparate systems.
While our tiger team solved for the gaps, our explorer team spent time thinking through the issues with the overall ecosystem. What they came up with was a platform that connected all of the bridges through a generative AI chat interface. This allowed the accounting firms to call all of their systems through a single chat. It substantially reduced end user frustration while increasing productivity. With tiger teams alone, this solution likely would have never been conceived.
How to Hire AI Explorers
Because explorer teams often must find new ways of looking at a company’s priorities and how they intersect with potential AI solutions, it’s most effective to recruit AI explorers from both inside and outside of the organization.
The explorer team should be a melting pot of interdisciplinary professionals with both longstanding internal experience and outside AI expertise to recognize prime opportunities for implementation. They don’t need to be software engineers or coders initially, but instead must be intellectually curious and open to AI as a whole.
To build out the explorer team, start with those who are educated on the industry. Hire people who are capable of doing the work even without AI. Besides having core competency, explorer team hires should have experience using AI and a base understanding of automations and large language models.
These hires should also be lateral thinkers. A good interview question might be, “Describe a unique way you have used AI in your job or during your studies.”
Software engineers can undoubtedly make great AI explorers, but they need to be consistently solution oriented. Look for people who have deployed AI solutions in a business setting, and hire those who consider every pathway before deeming something impossible.
A good interview question might be, “I need to automate [insert business specific process here], how fast could you automate it and how would you do it?” Then follow up with, “What if I told you I need it completed in half the time, how would you do it?”
Start With AI Discovery, Then Embed Explorers
As technology evolves, some businesses may want to dedicate a department just to AI development. Not every business may have that luxury. In lieu of a full AI department, businesses should form their first explorer team to conduct an AI discovery initiative.
A good AI discovery process should collect all of the AI solutions and ideas that key people at the firm have thought up. It should then score and prioritize those ideas based on the value to the business. AI discovery should also evaluate a business’ systems to determine how ready the firm is to adopt AI. Finally, the process should produce a roadmap that lays out initial AI pilots, KPIs and a phased approach to developing the highest scored AI solutions.
AI discovery often helps identify ideal explorers and highlights skills gaps that may best be filled by outside talent. Then, to bolster the continual evolution of a company’s use of AI, organizations should look to build explorer teams composed of employees that are more likely to become a permanent part of the company.
The best AI explorer team should be made of employees that can continue to function in the organization when their initial implementation project is complete. These trailblazers will continue to bring their unique perspective to their teams and answer the call to integrate new AI solutions with even more knowledge of the organization’s inner workings. Adding AI explorers can inspire teams of legacy employees stuck in the inertia of AI inexperience, establishing a dialogue to understand their specific needs and guiding them in adopting new tools.
Amidst a growing sea of failed AI pilots, AI explorers can help steer the ship, discovering uncharted possibilities for effective AI adoption.
Featured Leadership
Dhaval Jadav is Chief Executive Officer of alliantgroup, America’s leading consulting and management engineering firm, which helps American businesses overcome the challenges of today to prepare them for the world of the 22nd Century and beyond. Jadav co-founded the firm in 2002 to be unlike any other consultancy, with an emphasis on partnerships with clients to not only identify but also implement quantifiable solutions to their most critical concerns.
Chris Stephenson is Managing Director of Intelligent Automation & AI at alliant and was previously a Managing Principal at Grant Thornton.
