The KEY things you need to know to make this essential leadership hire.
It’s clear that Artificial intelligence (AI) is on the rise with 22% of Fortune 500 CEOs investing or planning to invest, according to the PWC 22nd Global CEO survey. With these investments, companies are expecting their Data Scientist teams and AI endeavors to drive business results. Many companies are unaware that hiring a VP of AI and ML product is the key to unlocking this success, and here’s why… An AI product leader steers the data organization to think beyond models and instead focus their attention on driving the delivery of exceptional, high-quality AI products that lead to financial impact and delighted customers.
So, what does a successful AI product leader look like today and how do you hire the right one for your organization? Here are the key areas to get smart on:
What an AI product leader does and how the role differs from a traditional software product leader:
- The role of an AI product leader is to drive the productization of AI models – setting the vision, continuously improving it, and then delivering high-quality AI products for the business that drive financial impact and outstanding customer experience.
- Like a software product management leader, a VP of AI Product needs to inspire confidence and align all stakeholders around roadmaps, plans, and programs — and systematically execute against them. They must also understand the needs of a business user and tech partners and serve as their champion, while hiring, motivating, and managing a high performing team. Additionally, having user experience (UX) design skills is just as important when dealing with AI products. How do you create meaningful experiences for the end users?
- Unlike a traditional VP of Product’s role, which follows a rules-based design process, this leader needs to harness design thinking within an AI-based setting, which has a continuous and dynamic learning cycle and demands ongoing data adjustments. They need to be adaptive and focused on monitoring, irritation, and continuous improvement. Because of this complexity, having robust technical knowledge is an even more important skill set to have.
What a strong VP of AI Product looks like from a profile and background perspective:
- Cross-functional team leadership
- An AI product leader must instill and improve best practices, be acutely customer-focused, have lean/agile product development processes through a diverse group of product, design, data science, and engineering groups.
- Business-focused and big-picture mindset
- This executive must have the ability to change the Data Science team’s culture from model-first to impact-first. Thinking beyond data analysis, this leader will align the team goals around use cases, business value, and overall outcomes.
- They will also need to be able to communicate with business stakeholders and internal and/or external customers. Continually getting user feedback and showcasing AI’s business value are critical since these investments are typically expensive and lengthy.
- A candidate can gain these skills by working at a consulting firm like McKinsey, Bain, Boston Consulting Group, etc., getting a master’s in business, and/or spending many years in an operational role with a P&L.
- Highly technical with hands-on experience developing AI/ML products:
- Ideally, you’ll target candidates with experience from innovative technology environments that embrace not only data science, ML and AI, but also public cloud (AWS), open source technologies, and agile environments.
- A traditional product management leader doesn’t necessarily require a technical background or degree. The same is true for an AI product executive role, but it’s extremely advantageous to have one due to the complex nature of these products and the technical employees that this leader will be influencing.
- Crisp and clear communication:
- Like most executive roles, being clear and concise increases a leader’s ability to influence and make overall business changes. It’s especially important in this role in which they will need to translate technical AI jargon to convey the benefits and outcomes for the business to various stakeholders
The challenges of hiring this type of leader:
- Machine learning, and more generally AI, within the product discipline is still nascent. Since the ML product function is new, the talent pool within this area is small, so if candidates have relevant experience, they are often not as tenured and mature as we would like them to be — and they are also very well compensated.
- These leaders require a unique and rare set of experiences, which includes enough technical acumen to be dangerous, in-depth knowledge of AI based applications/solutions and their user experience, and excellent communication and leadership skills.
Things you can do in the search process to make a successful hire:
- Target candidates with the right educational credentials. You’ll likely want to include an ML case study during your interview process, and it will be challenging for candidates to pass unless they have advanced degrees in quantitative fields such as Mathematics, Statistics, Physics, Engineering, Computer or Data Science. There are always exceptions to this, but it’s a good starting point for your target candidate profile.
- Be mindful of the scale of your business and the amount and types of data that they will be dealing with. You should assess the differences in models at the candidates’ various companies. Additionally, from a cultural perspective, candidates with pure startup experience may struggle in a more bureaucratic, matrixed environment found at a Fortune 500 company.
- Because this is a small candidate pool, it’s important to have a tight and candidate-driven interview process. The process takes an extra level of candidate care because there are only so many leaders in the market that have the exact qualifications for success. We suggest engaging a headhunter or retained executive search firm with these candidate relationships and having the hiring manager take the time to engage with top candidates. Examples of this type of close engagement include, building a texting relationship with the candidate during the interview process, offering to share insights on other interviewers, and/or meeting with the candidate in a social, in-person setting.
I led my first Head of Machine Learning Product searches at the beginning of 2020 and since then, companies have become increasingly aware that integrating AI is key for long-term success and staying ahead of the game. By leveraging these key insights outlined above, your business will be far better positioned to identify and hire this integral member of your leadership team.