Imagine telling every manager in your company that before adding a new employee, they must prove an AI can’t do the job. This bold directive is exactly what Shopify’s CEO implemented in 2025, making AI the default consideration for any new task. Such an “AI-first” mindset is rapidly gaining traction across industries as organizations realize they must leverage artificial intelligence at their core to stay competitive. In fact, 92% of companies plan to increase AI investments in the next three years, yet only a mere 1% currently consider themselves fully AI-mature (with AI deeply integrated into daily workflows). The gap between ambition and execution is striking, and it underscores why building effective AI-first teams is now a top priority.
Leading enterprises like Unilever and Shopify have already begun restructuring around AI-first teams that blend human expertise with AI agents. A recent Harvard Business Review feature noted how these companies formed small, agile units composed of a few strategists supported by dozens of on-demand AI tools, delivering output and efficiency human-only teams can’t easily match. The message from such pioneers is clear: integrating AI is not a side project but a fundamental operating principle. Business leaders and HR professionals across all sectors are thus faced with a pivotal challenge, how to hire the right talent and design team structures that put AI at the forefront of innovation. This article offers a comprehensive guide to building AI-first teams, covering key roles to recruit, organizational models to consider, cultural shifts needed, and strategies to future-proof your workforce for an AI-driven world.
Building an AI-first team starts with hiring the right mix of expertise. It’s no longer sufficient to hire a handful of data scientists and call it a day. Successful AI-driven organizations assemble diverse technical specialists who can collaborate to turn cutting-edge algorithms into real business solutions. Key roles often include:
Crucially, technical prowess alone is not enough. Leading companies now seek AI talent with strong business acumen and soft skills alongside technical ability. The best AI team members can connect their work to real business value, for example, framing the right problems to solve and measuring impact on metrics like efficiency or revenue. This means an effective AI specialist should understand the industry context and speak the language of non-technical stakeholders. Collaboration and communication skills are highly prized: AI initiatives touch multiple departments, so team members must be adept at translating complex AI concepts into plain language that marketing, finance, or operations teams can grasp.
Finally, given the breakneck pace of AI innovation, top AI-first teams prioritize continuous learning. New models, tools, and techniques emerge constantly, so ideal hires exhibit curiosity and adaptability. Organizations like Google and Amazon have long fostered a culture of lifelong learning in their AI staff, encouraging attendance at conferences, ongoing training, and experimentation. In practice, this means looking for candidates who proactively update their skillsets. Companies are actively seeking people who demonstrate a growth mindset and comfort with change, knowing that what’s cutting-edge today may be outdated next year. By hiring for a blend of deep technical expertise, business-savvy thinking, teamwork, and adaptability, you create a foundation of talent capable of driving an AI-first strategy forward.
How you organize your AI team within the broader company can make or break the effectiveness of your AI initiatives. There is no one-size-fits-all structure, the optimal approach depends on your organization’s size, AI maturity, and strategic goals. However, several common models have emerged for structuring AI-first teams:
Figure: An example of a flat AI team structure in a startup, where a single product manager oversees a cross-functional group of AI specialists (reporting lines indicated by arrows).
Regardless of structure, an important consideration is whether to build internally or partner externally. Some firms choose to keep their AI team entirely in-house, which provides maximum control and cultivates institutional knowledge, but hiring and retaining talent is costly. Others leverage external resources, such as consulting firms or outsourcing certain AI projects, to fill gaps. For instance, outsourcing development to specialized AI vendors can save up to 60% in costs and accelerate project timelines in some cases. A hybrid approach is common too: maintain a core internal AI team for strategic work, while contracting out niche tasks or using offshore teams for support, thus blending cost efficiency with in-house oversight. There are trade-offs with each choice. In-house teams ensure AI solutions are tailor-made and that expertise stays within the company, but require significant investment. Third-party or offshore teams offer flexibility and quick scaling, but demand diligent management to align with your business context and quality standards.
In practice, many companies evolve their team structure as they grow. A tech startup might begin with a flat structure (one product manager guiding a small group of ML engineers, data scientists, etc., as shown above). As the AI initiative proves value and workload increases, they may appoint an AI team lead or department head, shifting to a functional hierarchy. Later, as AI projects proliferate across the enterprise, a matrix or embedded model could emerge to meet specialized needs. The key is to be intentional and flexible: design your AI team structure based on current needs, but revisit it regularly. Scalability, cross-functional alignment, and clarity of ownership are the guiding principles. By structuring teams thoughtfully, businesses ensure that their AI efforts are not isolated experiments but part of an integrated strategy delivering impact at scale.
Hiring the right people and structuring the org chart are necessary steps, but the true engine of an AI-first team’s success is its culture. An AI-first culture is one that encourages collaboration across disciplines, continuous learning, and an ethical, innovative mindset. As AI becomes woven into every facet of work, leaders must cultivate an environment where humans and AI systems can effectively “team up” to achieve outcomes.
Cross-functional collaboration is essential. AI projects rarely succeed in isolation, a model developed in a vacuum can fail if it doesn’t solve a real business problem or isn’t adopted by end-users. Thus, top-performing AI teams work hand-in-hand with domain experts and other departments. For example, an AI solution for supply chain optimization might involve AI engineers, supply chain managers, and IT all working together. Companies building AI-first teams explicitly value bridge builders, team members who not only excel at AI tasks but can partner with colleagues in marketing, operations, finance, and beyond. These individuals act as translators between technical and non-technical groups. By embedding AI team members into cross-departmental squads or holding regular knowledge-sharing sessions, organizations ensure AI solutions are practical and widely embraced. A collaborative culture also means celebrating team wins (AI developers and business users together) rather than siloed accomplishments.
Another cultural pillar is continuous learning and experimentation. Given how quickly AI technology evolves, teams must constantly update their knowledge. Leading organizations encourage their AI talent to spend a portion of their time on research, attending industry conferences, taking online courses, or tinkering with new tools. This could be formalized through initiatives like “learning Fridays” or budgets for certifications and workshops. Management should send a clear signal that stagnation is not an option, learning is part of the job. Importantly, psychological safety plays a role here: team members need to feel they can experiment, and even fail, without fear of blame. Many companies, such as Amazon with its famous “Day 1” mentality, promote the idea that failing fast and learning fast is positive. A culture of innovation encourages trying creative approaches and proactively seeking improvements. As one HR tech report noted, top firms foster an environment where experimentation is encouraged and failures are treated as learning opportunities, this keeps the AI team nimble and ahead of the curve.
Equally important is an ethical and responsible AI mindset. When AI is driving core business decisions, the stakes are high, issues of bias, fairness, transparency, and social impact cannot be ignored. AI-first teams should internalize the principle that just because something can be done with AI, doesn’t always mean it should be done. Many leading organizations now include ethicists on AI teams or have established AI ethics committees to review projects. Every AI practitioner should be trained to consider the ethical implications of their work: Are the training data representative and fair? Could the algorithm unintentionally discriminate or harm certain groups? Is the model’s output explainable to stakeholders? By incorporating ethical checkpoints in the development process (for instance, running bias audits on models, or consulting a diverse group of reviewers), AI teams build solutions that are not only innovative but also trustworthy and compliant with regulations. This ethical diligence must be baked into the culture from day one. Companies like Google, Microsoft, and Salesforce have published AI ethics principles that their teams follow, signaling a commitment from leadership that responsible AI is non-negotiable.
Finally, effective leadership and vision bind the cultural elements together. AI-first teams benefit greatly from leaders who articulate a clear vision for how AI will advance the organization’s mission, and who empower their teams to realize that vision. This means executives and project leaders who advocate for AI initiatives, allocate sufficient resources, and set ambitious but achievable milestones. A good AI team leader also acts as a shield and a bridge, removing roadblocks and securing cross-functional buy-in so that the team can do its best work. They should champion the successes of the AI team across the company to build momentum and understanding. Notably, recent research highlights that while many employees are ready and eager to work with AI, a lack of leadership alignment is often the biggest barrier to scaling AI in the workplace. Leaders must therefore not only sponsor AI projects but also drive the organizational change management needed, addressing employee concerns, updating processes, and redefining roles as AI is introduced. In short, nurturing an AI-first culture involves a top-down commitment to collaboration, learning, ethics, and supportive leadership, together these create a fertile ground in which advanced AI solutions can truly flourish.
For HR professionals and business leaders, one of the most pressing challenges in building AI-first teams is the global AI talent shortage. Demand for AI skills has exploded in recent years, far outpacing supply. A 2025 Bain & Company report found that AI-related job postings have been increasing by 21% annually since 2019, while compensation for AI roles has risen 11% each year, yet the talent pool isn’t growing fast enough to meet this surging demand. Nearly half of executives (44%) cite the lack of in-house AI expertise as a major barrier to adopting generative AI and other advanced tools. If this gap persists, companies risk seeing critical AI initiatives delayed or unfulfilled, with Bain projecting that in the U.S. up to one in two AI jobs could go unfilled by 2027. In short, there is intense competition for experienced data scientists, machine learning engineers, and other AI specialists, and hiring alone is not a sufficient strategy to build the team you need.
To overcome these talent challenges, organizations should adopt a multi-pronged approach: upskilling, broadening recruitment, and smart workforce planning. Upskilling (and reskilling) your existing employees is often the fastest way to inject AI skills into your teams. Many companies are now investing in internal training programs, online courses, and certification bootcamps to turn software engineers into ML engineers, or business analysts into data analysts familiar with AI tools. This has a dual benefit, it fills roles and increases employee retention by demonstrating a commitment to their growth. As Bain’s AI practice leader noted, companies need to “move beyond traditional hiring, prioritize continuous upskilling, and foster an innovation-driven ecosystem” to address the talent shortage. Some forward-thinking firms have even implemented AI mentorship programs, pairing less experienced staff with AI experts, or established “AI academies” internally to cultivate talent from within.
At the same time, recruitment strategies should broaden to seek talent in unconventional places. Rather than competing head-on for a small pool of PhD-level AI researchers, consider candidates with adjacent backgrounds who can learn on the job. For example, individuals with strong software engineering fundamentals, mathematics/statistics graduates, or people from scientific fields (physics, computational biology, etc.) can often pick up AI skills quickly with the right mentorship. Also, tapping global talent markets can help, remote work and collaboration tools enable hiring from regions with rich pools of STEM graduates. Another tactic is partnering with universities or sponsoring AI research labs to gain early access to emerging talent. Flexible hiring models are becoming common: using contractors or gig platforms for short-term AI projects, or engaging consulting firms to kick-start an AI effort while your internal team ramps up. Shopify’s approach mentioned earlier, making sure “AI is the first resort”, actually extends to hiring practices as well, encouraging teams to consider automation or AI solutions before adding headcount. This doesn’t mean humans are being replaced wholesale, but rather that new roles are carefully considered and often focus on what humans do best (creative direction, complex strategy) while leveraging AI for routine execution.
In implementing AI-first teams, other challenges will arise beyond just finding talent. Change management is critical: introducing AI can unsettle employees who fear job displacement or are unsure how to adapt. Clear communication about how AI will augment rather than replace human work is vital. Many companies emphasize that they are using AI to remove drudgery and free up people for higher-value tasks, and they provide training so staff can confidently use AI tools in their day-to-day jobs. Moreover, establishing success metrics and quick wins helps build momentum. Early AI projects should be chosen not only for impact but for visibility, delivering a tangible result (say, a 10% improvement in customer service response time thanks to an AI assistive tool) can turn skeptics into advocates. It’s also wise to implement governance from the start: frameworks for data security, model validation, and ethical review prevent setbacks that could derail the program. In summary, meeting the talent and change challenges of AI transformation requires creativity and commitment. The companies that succeed will be those that invest in their people, whether through development or strategic hires, and create an environment where humans eagerly collaborate with AI. By doing so, you’ll mitigate the talent gap and build a resilient team that can navigate the fast-changing landscape of AI technology.
Becoming an “AI-first” organization is no longer a futuristic slogan, it is a present-day strategic imperative. Hiring brilliant AI experts and structuring them into a team is only the beginning. To truly reap the benefits of artificial intelligence, companies must infuse AI into the fabric of how teams operate, make decisions, and innovate. The journey will challenge traditional ways of working, but it also promises unprecedented opportunities. As we’ve discussed, success requires a holistic approach: attracting the right mix of talent, organizing that talent in an effective team structure, and nurturing a culture that amplifies their impact. When these elements come together, AI-first teams can achieve remarkable outcomes, from new product innovations to streamlined operations, that set companies apart in their markets.
Perhaps most importantly, leadership and vision will determine how quickly an organization can transform. Bold leaders are already “steering their organizations closer to AI maturity” by aligning teams and rewiring company processes for the AI age. They understand that the future of work isn’t human vs. AI, it’s human plus AI working in tandem. In this future, employees at all levels will have AI co-pilots, and teams will be flatter, faster, and focused on creativity and strategy while machines handle repetitive tasks. The companies who start building that future today, by establishing AI-first teams and continually evolving them, will be the ones leading tomorrow. In a famous analysis of technological revolutions, McKinsey researchers warned that the real risk for business leaders is not that they think too big with AI, but that they fail to think big enough. The rise of AI is poised to redefine industries on a scale comparable to the internet revolution, and it will determine the rise and fall of enterprises in the coming decade.
The takeaway is clear: now is the time to invest thoughtfully in your AI teams, the people who will bridge human ingenuity with machine intelligence. By hiring exceptional talent, structuring them for agility, fostering collaboration and ethical innovation, and continuously growing their skills, you create a powerhouse that can unlock AI’s full potential. For HR professionals and business leaders, this is a unique chance to shape the future workforce. An AI-first team is more than a technical unit; it’s a strategic asset that can drive competitive advantage and adaptation in a rapidly changing world. With the right vision and execution, your AI-first teams will not only deliver business value but also help ensure your organization thrives in the new era of AI-driven everything.
An AI-first team integrates artificial intelligence into core workflows, combining human expertise with AI tools to enhance productivity, innovation, and decision-making across the organization.
Key roles include machine learning engineers, data scientists, AI researchers, data engineers, software developers, AI product managers, and AI ethicists, along with domain-specific and user experience specialists as needed.
Common structures include centralized AI Centers of Excellence, hybrid matrix models, and fully decentralized embedded teams, with the choice depending on the company’s AI maturity and goals.
A strong AI-first culture emphasizes cross-functional collaboration, continuous learning, experimentation, ethical AI practices, and supportive leadership that aligns AI projects with business strategy.
Strategies include upskilling current staff, recruiting from adjacent fields, tapping global talent, partnering with universities, using flexible hiring models, and implementing change management to ease adoption.