At the end of 2024, over 90% of organizations will face a global skills shortage, costing an estimated $6.5 trillion. Primary causes of this deficit will be delayed product launches, reduced customer satisfaction, and missed revenue targets.
Product designers must now learn AI algorithms to craft intelligent interfaces, while QA testers must refine their ability to test AI-driven features. These new skills are the key to remain competitive.
With AI deeply embedded into everyday applications, all team members need to adapt and integrate AI into their workflow to deliver innovative products on time and with high quality.
The most important AI tools and tech you need to master now
In order to thrive in AI-driven product development, technical proficiency has become non-negotiable. It will include a deep understanding of AI tools, platforms, and relevant programming languages.
AI has infiltrated the entire software development lifecycle (SDLC), and teams need to move beyond just knowing the basics of popular tools like TensorFlow and PyTorch. AI is now shaping every aspect of development, from design to deployment.
It’s not just the engineers who need to embrace AI. Teams in marketing and product design should grasp the fundamentals of AI tools like data cleaning to align their strategies with cleaner, more effective data models.
BairesDev’s research highlights the growing demand for machine learning, data literacy, and data analysis skills in 2024, making it clear that AI is fast becoming a key component across all disciplines in product development.
Why your team must learn AI tools beyond the basics
AI tools now extend their influence across the entire product development process. TensorFlow and PyTorch are well-established platforms, but their impact isn’t limited to data scientists.
Non-engineers, such as designers and marketers, benefit from understanding how these tools operate. For instance, basic knowledge of data cleaning helps these teams to refine their approaches, making sure they generate cleaner data for training AI models.
A foundational understanding broadens the perspective of team members, helping them to contribute more meaningfully to AI initiatives. BairesDev’s internal data suggests that machine learning and related skills are rapidly becoming more and more important, further emphasizing that every department should be familiar with AI-driven strategies.
Data literacy is the superpower you didn’t know you needed
Data literacy has become indispensable in today’s AI-driven world. It involves the ability to manage, interpret, and analyze data to extract meaningful insights. In practice, it translates into making informed, data-driven decisions and converting AI-generated results into actionable business strategies.
Without a firm grasp of data literacy, teams risk misinterpreting AI outputs and making decisions based on incomplete or misunderstood data.
Data literacy is important to make sure that decisions are accurate, timely, and aligned with strategic goals. Data literacy requires interpreting what numbers indicate for future actions and trends.
Teams need this skill to ask the right questions, challenge assumptions, and use data to drive their decision-making processes effectively.
Real-world wins from teams that mastered data literacy
Data literacy helps every role in product development to make smarter, more data-driven decisions:
- Product developers: Use insights from data to fine-tune product features that better align with user needs.
- QA teams: Spot patterns in product defects, allowing for more efficient and targeted testing strategies.
- UX designers: Analyze behavioral data to improve user experiences based on real-world interactions.
- Product marketers: Measure campaign success and tweak strategies for better return on investment (ROI).
- Project managers: Use data to make informed resource allocation decisions, improving project outcomes.
Each of these applications showcases how a strong foundation in data literacy can lead to more refined products, better customer experiences, and improved efficiency across teams.
Using data for smarter decisions
While data literacy helps teams understand data, data analysis goes a step further by transforming that data into actionable insights. Data analysis involves a series of activities such as inspecting, cleaning, and modeling data, all aimed at driving smarter decision-making in product development.
Teams that excel in data analysis are positioned to innovate quickly, respond to market trends, and stay ahead of competitors. With AI offering unprecedented levels of data processing, understanding how to analyze and interpret that data is becoming a key differentiator for successful teams.
Some practical ways data analysis is changing product development include:
- Spotting market trends: Product managers use analytics to monitor social media and online searches, helping them align product lines with emerging trends, such as eco-friendly products.
- Improving user experience: Product designers use customer feedback and behavior patterns to improve popular features while rethinking underperforming ones.
- Simplifying development: Developers rely on usage statistics to determine where to focus their attention, addressing the most important areas first.
- Anticipating demand: Predictive analytics empower product managers to forecast future needs, such as seasonal trends, giving optimal inventory management and minimizing supply chain inefficiencies.
Data analysis allows product teams to make more informed, agile decisions, staying ahead of customer needs and competitive pressures.
Deploy, integrate, innovate, the must-have skills for product managers
Product managers need more than traditional project management skills. They must master deployment and integration skills, connecting AI models with existing systems to improve workflows and drive innovation.
Integrating a machine learning algorithm into a mobile app requires product managers to coordinate API connections, data preparation, and guarantee data security. Mastering these processes helps them innovate faster and deliver smarter, data-driven solutions in real time.
