As Founder and CEO of Sapia.ai, Barb Hyman helps organisations including BT, Costa Coffee and Qantas redesign hiring to be fairer, faster and more human.
Drawing on insights from more than ten million candidate interactions, she brings a unique perspective on what today’s hiring processes get wrong and how responsible AI can improve candidate experience, reduce attrition and uncover hidden talent.
Barb is a leading voice on ethical AI, the future of work and workforce transformation. Her talks focus on real life applications of AI and how organisations can embrace AI without losing the human judgement, trust and empathy that define great businesses. Audiences leave with practical strategies for implementing AI responsibly, challenging bias and building organisations where both people and technology thrive.
AI has already made a significant impact on the tech industry, and its influence is not slowing.
This trend has caused roles and workflows to change drastically, leaving companies in desperate need of people who can effectively work with AI tools. However, the tech industry is still relying on traditional indicators when scouting out new talent. When hiring managers continue to look for applicants who have proven abilities with using specific tools, certifications or previous AI experience, they can find themselves neglecting a large talent pool capable of driving sustained AI innovation.
The reality of many candidates is that they are being overlooked because they aren’t able to show that they have direct experience with AI; despite having many transferable skills that will help them to thrive in AI-enabled environments. AI tools are becoming what the computer was twenty or so years ago; skills were highly sought after and rare but have since become an unspoken expectation in tech roles. For now, AI does help to demonstrate ability, but the decisive factor is the capability to develop, learn and work alongside AI as it evolves.
Defining AI-ready thinking
When we talk about AI-ready thinking, we’re moving away from the use of specific skills such as using ChatGPT, for example, to instead show how a combination of skills, judgement and behaviours can determine whether an individual has what it takes to generate substantial value while working with AI, regardless of the specific application in order to solve problems.
Built around five main competencies, including critical evaluation, learning agility, calibrated judgement, workflow imagination and ethical accountability. These AI-ready skills are invaluable for promoting inclusive hiring practices in tech roles because someone’s skillset often shines a light on their ability to work with AI more than it does their technical ability or familiarity with related tools.
Critical evaluation enables people to assess AI outputs, recognise limitations and apply their expertise where necessary. Adaptivity is a key characteristic when working with AI, and learning agility reflects this by demonstrating the ability to grow alongside it. Organisations are constantly looking for ways to innovate and remain competitive, and AI is a common way of doing so. With that in mind, candidates should be able to showcase their workflow imagination and ability to find new and interesting ways to create value through AI integration. Lastly, calibrated judgement and accountability are non-negotiable traits for candidates wanting to work in tech roles. An individual who understands when AI should be trusted and how to ensure it is employed responsibly will stand out in a competitive job market.
Experience is no longer enough
When hiring for AI roles, businesses continue to rely on outdated indicators including CV keywords, certifications and self-proclaimed AI experience, which show a candidate’s exposure to AI, not their capability. A candidate may use AI tools throughout their workday, and others may build entire AI systems or have experience conducting AI research, but that does not necessarily mean they know when to trust AI, when to challenge inaccurate results and how to manage prominent risks. This adds to the problem, as businesses continue to neglect individuals without AI skills that can be showcased on a piece of paper, even though they are likely already confidently demonstrating AI-ready behaviours.
It can be extremely confusing for candidates applying for AI jobs in the current climate; however, there are many strategies which can help them navigate interviews and show their full potential. That starts with demonstrating how they think, not just which AI tools they have used. When a candidate is able to explain a time when they adapted to a new technology or where they have experienced limitations with AI, they can show hiring managers they are prepared to work alongside AI.
Realistically, employers want people who can identify where AI creates value and solves real problems. For example, a successful candidate could talk about how they designed a process which combines automation with human decision-making, showcasing their ability to improve and add genuine value to daily operations. To demonstrate AI-readiness, candidates need to be ready to learn, showing they can use and evaluate AI responsibly.
Hiring for AI-ready talent
To be able to identify AI-ready thinkers, businesses need to redesign the way they conduct hiring assessments. Traditional and unstructured interviews often favour candidates merely based upon their previous experience. However, every business wants to hire someone for their future potential. By defining transferable skills and employing structured interviews, which ask questions about realistic scenarios, businesses can evaluate a candidate’s ability to judge and employ AI, rather than their technical knowledge alone.
There is a framework for AI skills hiring which supports businesses throughout this process, helping them understand and employ AI-ready professionals. The framework consists of five layers, with the most visible being fundamentals and core skills. Most tech companies already test for this when hiring new talent, but these competencies are often the easiest to train and the fastest to lose.
Judgment and Dispositions are layers three and four, which are valuable for assessing how well a candidate can reason and adapt to new tools, not specifically AI, showcasing their transferable skills. Layer five is Domain x AI and is where the most value is revealed. A candidate that can pair domain experience with judgement and disposition to use AI effectively will prove to be the most productive, yet it tends to be the most neglected layer in AI hiring.
How hiring practices adapt to changes in the industry will determine the roles that exist and the future career opportunities that are available in technology. Careers that require AI skills are new and extremely rewarding, yet they are largely inaccessible for people wanting to enter the technology sector from alternate backgrounds. Because of this, businesses need to adapt their hiring practices to reflect a person’s potential, not just their specific, tangible experience with AI. For candidates, this shift presents an exciting opportunity to demonstrate how they can adapt, evaluate and create value through AI. If you’re looking for new roles in tech, be ethical, flexible and ready to adapt to new tools, but most importantly, be human.
