To successfully build AI-powered solutions, like automated software products, you need people that have specific skill sets and experiences.
When it comes to AI products data scientists, analysts, and programmers might be some of the first names that pop into your head. But there’s another key role that can determine the success of a product – the AI product manager.
The “AI product manager” role has emerged only recently. As artificial intelligence and machine learning technologies are being used to create new solutions, the role of an AI product manager is becoming more common. However, there’s still some confusion as to what they do.
In this article, we’ll dive down into what a typical AI product management job entails (and answer some frequently asked questions).
Let’s get started.
What is an AI Product Manager?
An AI product manager is a specialized product management professional whose job is to manage the planning, development, launch, and success of products/solutions powered by AI, machine learning, and deep learning technologies.
Depending on the company they work for, an AI product manager may have to work on any of the following types of projects:
- AI-powered solutions/components for a product that’s not primarily an AI product.
- Products that are completely powered by AI or provide solutions that mainly rely on artificial intelligence.
- Service or consultancy focused on developing and implementing custom AI solutions.
While an AI PM doesn’t need to have a total technical background, they certainly need some level of familiarity in areas like stats, machine learning models, and algorithms to succeed without having to rely heavily on their peers.
How Bettingguideau Explains V8 Supercars Betting Markets to Australian Fans
Australian motorsport has a betting culture that runs deep, shaped by decades of passionate fan engagement with events like the Bathurst 1000 and the broader Repco Supercars Championship. For fans who want to move beyond simply watching and start wagering on race outcomes, understanding the specific betting markets available for V8 Supercars — the colloquial term still widely used despite the series officially rebranding — requires more than a basic grasp of odds formats. The markets themselves are layered, often misunderstood, and governed by regulations that differ meaningfully from other motorsport disciplines. Resources that explain these structures in plain terms fill a genuine gap for Australian punters who find themselves navigating a betting landscape that has grown considerably more complex since the series moved to Gen3 regulations in 2023.
Understanding the Structure of V8 Supercars Betting Markets
The Repco Supercars Championship operates across a calendar of roughly twelve to fifteen event weekends per season, each structured differently depending on the venue and format. Some rounds feature two sprint races on a Saturday-Sunday format, others incorporate endurance co-driver pairings, and the Bathurst 1000 in October stands apart as a 161-lap endurance event with entirely different betting dynamics. This structural variety is one reason why V8 Supercars betting markets are more nuanced than, say, betting on a single football match with a fixed set of outcomes.
The most fundamental market is outright race winner, where the bookmaker sets odds for each driver to win a specific race. In a field that typically includes 24 to 26 cars, odds for frontrunners like Shane van Gisbergen — before his departure to full-time NASCAR competition — or Brodie Kostecki often sit in the range of $3.00 to $6.00 with Australian bookmakers, while mid-field competitors might be priced anywhere from $15.00 to $150.00. These prices fluctuate based on qualifying results, which in Supercars have a direct bearing on grid position and therefore race outcome probability. Unlike Formula 1, where qualifying pole provides a significant strategic advantage on circuits with limited overtaking opportunities, Supercars racing on circuits like Symmons Plains or Barbagallo tends to produce more variable race outcomes from mid-grid positions, which is reflected in tighter odds spreads among the top eight or ten drivers.
Championship outright markets operate across the full season and attract significant early-season money. Bookmakers typically open these markets at the start of the year or even in the preceding November, setting initial prices based on the previous season’s results, team announcements, and car development trajectories. The introduction of Gen3 regulations in 2023 — which brought Chevrolet Camaro machinery alongside the Ford Mustang GT in a significant departure from the previous Car of the Future platform — created genuine uncertainty in these markets, as the relative pace of each manufacturer was unknown. Punters who understood the technical implications of the new regulations had an edge over those relying purely on historical form, which illustrates why contextual knowledge matters when engaging with outright championship markets.
Round winner markets are a middle ground between race-by-race betting and season-long outrights. Because most rounds involve two or three races, a round winner market asks which driver will accumulate the most points across that specific event. This introduces a different analytical framework — a driver who finishes second in both races of a round might beat a driver who wins one race but retires from another. Understanding the points structure (25 for a win, 18 for second, 14 for third, descending to 1 for fifteenth and below, with 1 bonus point for pole and 1 for the fastest lap) is essential for evaluating these markets accurately.
Specialty Markets and How Bettingguideau Approaches Them
Beyond the headline markets, V8 Supercars betting includes a range of specialty options that require specific knowledge to evaluate. Head-to-head markets pit two named drivers against each other, with the bet settled on which driver finishes higher in a given race regardless of overall result. These markets are particularly useful when a punter has a strong view on the relative pace of two mid-field competitors but less confidence about where either will finish in the overall standings. Bookmakers typically offer these markets for the top-tier drivers and occasionally for teammates within the same squad, where internal team dynamics and car setup similarities make comparison more meaningful.
Podium finish markets, where a punter bets on whether a specific driver will finish in the top three, are priced with implied probabilities that can be tested against qualifying data and historical circuit performance. A driver who has qualified in the top five at a given circuit across the past three visits has a statistically different podium probability than their season-average might suggest, and bookmakers do not always fully adjust for circuit-specific form. Fastest lap markets, available at some bookmakers, require an understanding of tyre strategy — in Supercars, the fastest lap is often set during the final stint when cars are running light on fuel, meaning drivers who pit late or run an aggressive final stint strategy are disproportionately likely to claim it.
Margin of victory markets and safety car markets have become more common in recent years as bookmakers expand their Supercars offerings. The safety car market, which asks whether a safety car or virtual safety car will be deployed during a race, is heavily influenced by circuit characteristics. Street circuits like the Newcastle 500 and the Sydney Motorsport Park layout historically produce higher safety car frequencies than permanent road courses like Phillip Island, where runoff areas reduce the likelihood of race-stopping incidents. Punters who track historical safety car deployment rates — which are publicly available through Supercars’ own media records and various statistical databases — can identify value in these markets when bookmakers price them without sufficient circuit-specific adjustment.
A significant portion of the educational work around these specialty markets has been done by dedicated motorsport betting resources. As documented at bettingguideau.com, the interaction between qualifying formats, tyre allocation rules, and safety car probability creates a matrix of factors that experienced punters weigh simultaneously when assessing specialty market value. This kind of structured analytical framing — rather than betting on gut feeling or brand loyalty to a particular manufacturer — is what separates recreational engagement from informed wagering.
The Bathurst 1000 deserves particular attention because its market structure differs fundamentally from sprint race events. The co-driver pairing requirement means that punters must assess not just the primary driver but also the co-driver’s pace and consistency, since co-drivers typically complete a minimum of two full stints across the 1000 kilometres. In years where co-driver quality is uneven — as it sometimes is when a primary driver is paired with a less experienced partner — this creates pricing inefficiencies in outright winner markets. The 2023 Bathurst 1000, for instance, saw significant variance in co-driver experience levels following the Gen3 transition, as some teams brought in internationally experienced co-drivers while others relied on Supercars Academy graduates with limited main series experience.
Regulatory Context and Responsible Wagering in Australian Motorsport Betting
Australian sports betting is governed by a combination of federal and state-level legislation. The Interactive Gambling Act 2001, as amended, sets the federal framework, while individual state and territory racing authorities oversee specific licensing conditions for bookmakers operating in their jurisdictions. For motorsport betting specifically, the Northern Territory Racing Commission (NTRC) is the primary licensing body for the major corporate bookmakers — including Sportsbet, Ladbrokes, TAB, and Pointsbet — that offer Supercars markets to Australian customers. This regulatory structure means that the odds and market offerings available to Australian punters are subject to Australian consumer protection standards, including mandatory pre-commitment tools and self-exclusion mechanisms under the National Consumer Protection Framework for Online Wagering, which came into full effect in 2019.
One regulatory development with direct relevance to Supercars betting is the prohibition on in-play betting via internet or phone for Australian-licensed bookmakers, which has been in place since the Interactive Gambling Amendment Act 2017. In-play betting — placing wagers after a race has started — is available at offshore bookmakers accessible to Australian customers, but these operators are not licensed under Australian law and therefore operate outside the consumer protection framework. This distinction matters practically: if a dispute arises with an unlicensed operator over a settled Supercars bet, Australian regulatory bodies have no jurisdiction to intervene. The absence of in-play markets from licensed domestic bookmakers also shapes the strategy of informed punters, who must commit their positions before the race starts rather than reacting to developing race situations.
The National Indebtedness Register and credit betting prohibition, introduced in 2018, further shaped the Australian betting environment. Credit betting — where a bookmaker extends credit to a punter to place wagers — is now prohibited across all licensed Australian operators. This has a practical effect on the staking strategies available to punters who previously used credit facilities to manage cash flow across a race season. Combined with mandatory account-based betting (which replaced anonymous cash betting at most retail outlets), these regulations create a more traceable and accountable betting environment than existed even a decade ago.
Bettingguideau has been noted by Australian motorsport fans for contextualising these regulatory realities alongside market explanations, which is a more complete approach than resources that focus exclusively on strategy without acknowledging the legal and consumer protection framework within which Australian punters operate. Understanding that certain market types — particularly in-play — are unavailable through licensed domestic channels helps punters plan their pre-race analysis more thoroughly, since all positions must be established before the formation lap.
Analytical Frameworks for Evaluating V8 Supercars Odds
Evaluating whether a set of odds represents value requires converting those odds into implied probabilities and then comparing those probabilities against an independently derived estimate. For a race winner market with 25 cars, a bookmaker’s overround — the built-in margin that ensures the sum of implied probabilities exceeds 100% — typically ranges from 110% to 130% for V8 Supercars events, depending on the bookmaker and the significance of the event. The Bathurst 1000 tends to attract tighter margins due to higher market liquidity, while minor round races mid-season may carry higher overrounds due to lower betting volumes.
An independently derived probability estimate for a Supercars race winner might incorporate qualifying position (historically, pole position converts to a race win roughly 30-35% of the time in sprint format races, based on data from the 2018-2023 seasons), circuit-specific win rate for the driver, team reliability data from the current season, and tyre degradation characteristics at the specific circuit. Phillip Island, for example, is a circuit where tyre wear is significantly higher than at most other Supercars venues due to its high-speed, flowing layout, which means teams that have demonstrated strong tyre management in practice sessions have an advantage that may not yet be reflected in pre-qualifying odds.
Weather is a variable that creates acute pricing inefficiencies in Supercars markets. Because Australian bookmakers set their Supercars race odds primarily based on dry-weather pace data, a forecast for rain at a circuit like the Gold Coast or Townsville — where the street circuit characteristics amplify the impact of wet conditions — can significantly alter the competitive order in ways that pre-race odds do not fully capture. Drivers with strong wet-weather records, such as those who have competed extensively in categories where rain is more common (including international GT racing or European touring car series), may be underpriced relative to their actual probability of a strong finish in wet conditions.
Team strategy is another analytical layer. In Supercars, pit stop timing is a critical competitive variable, and teams with larger engineering departments — Triple Eight Race Engineering and Dick Johnson Racing being the most resourced operations in recent seasons — tend to execute strategy more consistently than smaller teams. However, smaller teams occasionally benefit from strategic flexibility: with less to lose in championship terms at a given round, they can adopt aggressive undercut strategies or gamble on safety car timing in ways that larger teams managing championship points cannot afford to. This dynamic is particularly relevant in round winner and race winner markets at rounds where championship implications are significant for the frontrunners.
Historical data from the Supercars Championship website and independent statistical archives like Racing Reference (which covers Australian touring car data alongside its primary American motorsport focus) provides a foundation for this kind of analysis. Combining publicly available historical data with real-time information from practice sessions, qualifying, and team communications creates a more complete picture than any single data source can provide independently. This multi-source analytical approach is the foundation of informed V8 Supercars betting rather than the simpler heuristics — betting on the manufacturer you support or the driver with the most name recognition — that characterise casual wagering.
For Australian fans who have followed V8 Supercars primarily as spectators and are considering engaging with betting markets for the first time, the learning curve is real but manageable. The markets themselves follow logical structures once the underlying championship format is understood, and the regulatory environment in Australia provides a relatively secure framework for licensed-operator betting. The key transition from casual to informed betting lies in treating each market as a probability estimation problem rather than a loyalty exercise — asking not which driver you want to win, but which driver the market has mispriced relative to their actual probability of winning. That analytical shift, applied consistently across the range of markets available for each Supercars event, is what transforms engagement with the sport from passive viewing into a genuinely skill-dependent activity.
This is especially true at startups, where the AI PM typically has to make a lot of individual contributions. If you’re interested in getting the skills to excel in this role, then check out our product management certifications.

What Does an AI Product Manager Do?
An AI product manager builds data sets, helps conduct market research, sets a vision, and aligns internal teams of an organization to create, launch, and maintain AI-powered products/solutions in the market. In short, they oversee and own the lifecycle of AI products.
From ideation to creation, AI PMs collaborate with the relevant team members to create a game plan for everything and make sure that everyone follows through on it.
Like every other position, there’s not a universal job description for these PMs. An AI PM working at Microsoft may have a completely different JD than someone who’s working at Amazon or LinkedIn.
However, we can generalize their responsibilities into the following without getting into the specifics:
Build Data Sets
One crucial thing that makes AI product managers different from traditional PMs is that they take a more data-driven approach.
Instead of creating a product strategy and a roadmap, they first start by building a unique database that they can later use to create effective AI solutions.
Of course, they don’t do this all by themselves. Product managers collaborate with internal data scientists and analysts to gather the training data. By making sure that the data is unique and of high quality, AI PMs and data professionals set the foundations for a successful product.
That way, product teams will have a far more laser-focused approach and build a solution that best solves the problems of the customers.
Conduct Market Research
Another key responsibility of a typical AI product manager is to conduct market research.
For this purpose, AI PMs collaborate with the product marketing department.
One part of the market research is focused on the users, through which the product teams accomplish the following goals:
- They develop a thorough understanding of who their customers are, where they come from, and what motivates them.
- They identify the critical pain points of their customers, which, in turn, enables them to focus on the right areas when developing their AI project.
Apart from users, AI PMs also aim to develop a deep understanding of the competitive landscape they’re in.
The quality of this research will determine the quality of the solutions they come up with.
Design Customer-Centric Solutions
It’s up to the AI product manager to come up with customer-centric solutions. Depending on the company they work for and where they currently stand, this could mean either of the following things:
- They’re going to design a complete product.
- They’re going to design a component/feature of a pre-existing product.
In any case, AI PMs collaborate with a company’s executives, product marketing, and product development teams to create something that would help take things forward.
The most important thing here is to make sure that those solutions align with the strategic and business objectives of the company. Anyone can come up with ideas, but it’s hard to come up with solutions that take things forward.
That’s where a PM’s know-how of business, marketing, and UI/UX comes in handy.
Define and Own the Product Roadmap
A core responsibility of any AI product manager (or any type of PM, for that matter) is to build a robust product strategy.
The core part of that product strategy is the product vision, which is where the company wants to see its product.
After defining that vision, the AI PM is responsible for specifying, in great depth, how product teams will get there.
That step-by-step process is known as the roadmap.
An AI product manager has to consider the following variables when specifying this product roadmap:
- The available resources in the company.
- The amount of budget the company is willing to spare.
- The scope of the work (deploying some products takes longer than others).
- The strategic business goals of your company.
Naturally, this requires close collaboration with the team leads of the project to take their challenges into account. After finalizing the strategy and the roadmap, the AI PM shares it with the entire company.
Align Internal Teams with the Vision
Another key duty of any product manager is to align internal teams to the product vision that they’ve specified.
For this purpose, the PM has to:
- Share the product strategy, vision, and roadmap (as mentioned above) with everyone on the team.
- Meet with all the business leaders and stakeholders to explain how the strategy aligns with business goals.
- Meet with all the cross-functional teams to explain their role in achieving the product vision.
- Prevent organizational silos and act as the main point of contact for everyone in the organization.
- Keep everyone on the same page regarding the product progress.
To sum it up, the AI PM rallies everyone, addresses everyone’s concerns, and keeps them laser-focused towards the end goal. However, keep in mind that the PM doesn’t have any authority over any of the team members. Their role is to simply act as a bridge between the internal teams.
Lead Internal Discussions
This particular responsibility can be considered a sub-set of the previous one but deserves to be highlighted separately.
In large companies, with hundreds of employees and several team leads, communication/discussions can be a bit of a problem. Two teams may not always see eye-to-eye, which can be devastating for organizational productivity.
To overcome that problem, the AI PM leads internal discussions between the teams and acts as a mediator.
However, the product manager’s goal here should be to make sure that every discussion leads to a conclusion – an agreement, plan of action, decision, or anything else that helps move the needle for the organization.
Track Business Metrics and Take Action
Last but not least, the AI product manager is also responsible for analyzing various performance metrics and provide feedback to the internal teams.
This entails:
- Specifying those metrics (these should be included in the product strategy).
- Creating a system to consistently collect that data.
- Creating a process for iterations in a way that doesn’t affect productivity.
From there, the product manager consistently looks for ways to improve their AI application/product, by either enhancing user experience or by introducing new features.
Of course, to do this effectively, the PM works with customer support, sales, marketing, and engineering teams to identify gaps.
Frequently Asked Questions (FAQs)
Since the AI PM is a new position, it’s natural to be confused and have queries about it. Below, we’ve answered some of the most frequently asked questions about this unique and highly specialized role:
How Do I Become an AI Product Manager?
To become an AI product manager, you need to get familiar with the essentials of artificial intelligence (data science, machine learning, deep learning, NLP, etc.), invest in technical skills (Python, SQL, data visualization, market research, etc.), and seek project management experience.
Here’s a step-by-step roadmap for how you can get there:
- Learn About AI – if you’re a complete beginner, start by learning about the basics of artificial intelligence. Read books, enroll in a course, and/or get a bachelor’s degree in any related field. It’s important to strengthen your foundations first.
- Learn the Critical Skills – as far as technical skills go, learn about Python, data sets, data visualization, SQL, and machine learning. Key business skills, such as strategy, resource management, and team management are also crucial.
- Go After Certifications – certification courses are great ways to learn foundational and advanced skills and validate them. Since you’ll be competing against thousands of peers, it’s important to stand out with a certification or two and get noticed by recruiters.
- Update Your Resume – modify your resume specifically for the AI product manager role. Highlight key skills and any experience in managing an AI-based project.
In the end, all that’s left is to apply for open positions. Head over to LinkedIn, Glassdoor, and Indeed to start your job hunt. Remember – even if you get rejected, make sure that you cultivate a positive and personal relationship with those recruiters.
Is AI a Product?
Artificial intelligence (AI) is a field of computer science that uses certain technologies and techniques to teach computers how to “think” and/or behave like humans. The products that implement this technology and use it as a primary component for their basic functionality are known as AI products.
AI/Machine learning products have vast applications. From aerospace companies creating autonomous UAVs to streaming services that suggest content, almost every vertical can benefit from AI applications as business opportunities grow.
AI product managers help identify those opportunities and oversee the lifecycle of products that can fill in those gaps.
What are the Requirements for a Product Manager?
To become a product manager you need to invest in and develop a certain skill set and get certain qualifications. Work on skills like business strategy, market research, financial forecasting, and analytics. Furthermore, develop soft skills like communication, leadership, negotiating, and interpersonal skills.
The product management role is heavily focused on practical skills and experience. Educational qualifications are nice-to-haves, but not a requirement. Having a bachelor’s degree in product management, business, marketing, engineering, or any other related field can only take you so far.
At the end of the day, if you don’t have a good product sense, decision-making capabilities, someone who does and isn’t even a college graduate will have an edge over you.
For that reason, go all-in on developing these practical skills (certifications can help a lot) and gathering practical, hands-on experience.
What Do ML Product Managers Do?
ML product managers work closely with data scientists, analysts, engineers, and other related internal team members to establish, manage, and oversee the lifecycle of machine learning products. They are very similar to AI product managers but have an additional layer of specialization.
The other responsibilities of a machine learning product manager are the same as a traditional PM. They have to bridge gaps between internal teams, create and own product roadmaps, gather customer feedback, and create processes in place to address that feedback.
Is it Easy to Get a Product Manager Job?
If you have the necessary skillsets, a good product sense, and a certification or two to validate your skills, it’s easy to get a product manager job regardless of your experience. If you’re a beginner, try your luck with an internship or an entry-level program (like the Facebook rotational product manager).
Building up work experience and investing in acquiring more credentials will open doors for better/higher-paying opportunities.
Final Thoughts
Compared to other types of PMs, the role of an AI product manager is more inclined towards the technical side of the business.
Not only are they responsible for doing everything that a typical product manager does (such as creating roadmaps, leading discussions, and setting goals), but also engage with the data team to create data sets.
Josh Fechter
Josh Fechter is the co-founder of Product HQ, founder of Technical Writer HQ, and founder and head of product of Squibler. You can connect with him on
LinkedIn here.