7 Important Things to Consider When Building or Scaling AI Teams

AI is powering modern businesses, and investing in an AI team is the best way businesses can harness the technology’s potential. PwC’s 2022 AI business survey shows that over 72% of business leaders and executives believe AI initiatives will provide tangible business advantages. Understanding the importance and possibilities of AI, many companies want to build their own in-house AI teams. 

Building a team from scratch is challenging for organizations with less experience. At the same time, scaling existing AI and data teams is its own challenge. Several technical and non-technical business factors come into play while building and scaling. These hurdles are easier to overcome with assistance from AI partners. This blog will help you learn about scaling and building AI teams.

Benefits of building an AI team 

While some completely outsource AI and tech operations, others build their own teams, and partnering with companies who have AI and data talent to build engineering teams is an increasingly popular strategy. 

Some benefits of having dedicated AI teams:

  • Increased productivity and improved decision-making
  • Ability to develop solutions tailored to your enterprise 
  • Potential to innovate existing products and services
  • Improve employee experiences and skills acquisition
  • Enhanced stakeholder trust and valuation
  • Transparency and control

Challenges of scaling AI teams

Here are some common challenges of scaling AI teams:

  • Finding talent to add to your AI team is difficult due to the AI talent shortage 
  • Planning business strategies, setting goals, and making OKRs for a newer and bigger team
  • Managing resources, maintaining accountability and assigning roles
  • Hiring non-AI professionals such as domain experts, marketers, and risk and compliance consultants 
  • Onboarding and acquainting new engineers to company culture and operations

Factors to consider while scaling or building AI teams

1. Technology and tools 

Your team should be able to work with the latest tools, languages, frameworks, and libraries. The success or failure of AI/data investments relies on these technologies. Understanding the fundamentals can contribute to better decisions when planning, creating, and managing development teams. Harnessing AI while implementing simple and cost-effective systems helps you get the results you’re after.

2. Time and resources 

Focus on the time and resources required to maintain industry standards and remain relevant and accurate. Make sure your end goals are deriving intelligent insights faster and making critical decisions from data. Again, simpler technology helps save valuable resources.

3. Diversifying the hiring pool

Hiring AI talent from a diverse pool increases the range of skills, experience and expertise in your workforce. Indeed listed both AI and data engineers among the most promising jobs. By making your AI team diverse now, you will benefit from professionals flexible to work on varied projects and adapt to different requirements. 

4. Transparency 

Transparent AI is the only way to achieve explainability. While developing AI models or solutions, your team must be able to explain the mechanisms and eliminate bias. Transparency helps make data-backed (and potentially sensitive) decisions with mitigated risk. When AI systems are explainable, teams can be straightforward with regulators and stakeholders. 

5. Collaboration and communication 

Collaboration and communication are key to building strong team cultures, especially on distributed teams. To do this, teams should leverage tools to communicate and keep track of projects and progress. When creating in-house development teams, collaboration ensures resources are supplied and communication issues are limited. Establish a supportive team culture in hybrid work environments by encouraging recognition of good work.

6. Training and upskilling requirements 

It is not practical to expect new hires to perform at similar levels to existing team members immediately. Onboarding and training take time. While building a new team, select candidates who are ready to learn, train and upskill (if required) to fulfill their roles. Make sure new team members are equipped to engage with existing AI infrastructures. 

7. Embrace automation when possible

At the very least, automation takes on repetitive tasks and saves resources. On top of this, it can also provide project insights and reduce human error. If you aim for efficiency and productivity, use automation while building or adding to your AI development teams. 

Partner with Fusemachines to build and scale AI and data teams

A reliable partner guiding and managing your AI teams has several benefits while reducing the risk of investment failure. Here are some reasons to partner with Fusemachines:

  • A trusted name in the AI industry with experience providing services for over a decade
  • Large pool of AI and data talent with a dedicated management network (Squadery
  • Active and responsible customer care 
  • Provides services across all major industries
  • Many long-term partners
  • Provides a wide range of AI services, including training, education, and bespoke enterprise AI solutions

Bottom line

There is no hard and fast rule book for building or scaling AI teams. Every organization or project has unique properties. Fusemachines has provided companies of different sizes and business models with competent AI and data teams to help them reach their goals and lead in their industries. Contact us today and let’s get started.