The limited availability of AI talent may explain why the adoption of AI in production has remained at 50% to 60% in the past two years. One of the main reasons for this is that the pool of AI talent is relatively small and highly specialized. According to a McKinsey report, only 10% of the world’s data scientists have the skills required for AI-related work. The report also shows machine learning engineers are very difficult to hire, while AI data scientist remain particularly scarce out of all AI-related roles.

This means that the competition for top AI talent is fierce, and companies are struggling to find the right people to fill their open positions.

In the face of this grim horizon, many companies tried to go out on a limb and look into how they can reskill or upskill their existing workforce. The most common strategies employed by companies are experiential learning, self-paced online courses, and certification programs. Investing in employee education and in-house training of technical roles has freed up resources, although not all companies can afford this approach due to budget constraints. If this strategy also breaks even, from a financial standpoint, remains unclear.

The most effective way to address the talent shortage is to partner with AI consultants. These experts bring a wealth of knowledge and expertise to the table, with a deep understanding of the latest technology and trends. Additionally, companies can not only build their AI teams, with the transfer of knowledge that some consultancy firms offer, but also create and execute a comprehensive AI strategy.

Furthermore, consultants offer ongoing support and direction to ensure continued success. But let’s explore the four benefits one by one:

1. Bridging the skills gap

The first obvious benefit of working with AI consultants is that they can help companies fill the skills gap. Many companies struggle to find candidates with the right mix of technical skills and business acumen. AI consultants, on the other hand, bring a broad range of experience and expertise across different industries and requirements.

As a result, AI consultants keep the true business value of a project in mind, helping companies to create and deploy AI solutions faster and more efficiently. It enables in-house teams to concentrate their efforts in areas of their expertise, freeing up valuable resources for other projects.

2. Instant access to data and tooling

Creating machine learning and deep learning models is contingent upon access to high-quality data. Even if an organization has access to clean, large-scale data relevant to the model, the process of working with Big Data is both time-consuming and requires expertise. For a successful AI project, proper data preparation is crucial. Without a strong foundation of input data, it may take longer to see results from your AI development efforts.

To make the most of machine learning, organizations must invest time in collecting and working with large datasets. This requires expertise and experience, as well as a commitment to feeding the platform where your machine learning models are hosted with high-quality data. Ensuring that predictions are accurate and relevant is a complex and ongoing process. The importance of specialization cannot be overstated, particularly in industries such as logistics, medicine, and finance.

To our partners, for e.g., we provide not only specialized AI knowledge in their respective industry but also a state-of-the-art management platform, navio, for machine learning models. navio allows our clients to independently monitor and assess the performance of their AI solutions, empowering them to make data-driven decisions with confidence.

3. Shorter time-to-market and its cost implications

In recent years, the availability of high-quality data sets has made outsourcing a popular option for speeding up the development and deployment process of AI and machine learning projects. By partnering with a company that specializes in specific areas or applications and has relevant experience, organizations can save valuable time and resources that would otherwise be spent on training and building in-house teams.

Lower costs than In-house development

Contrary to popular belief, outsourcing can actually be more cost-effective than building an in-house IT team. While the initial expenses for salaries, hardware, software licenses, and cloud service subscriptions may seem lower for an in-house team, there are several invisible costs associated with maintaining and updating the team. These include employee training and workshops, recruitment, employee benefits, and the time and effort spent on the hiring process.

With outsourcing, the service provider is absorbing these costs, resulting in more predictable prices and a clearer ROI. Outsourcing also eliminates the need to constantly update and maintain an in-house team, freeing up funds to be directed towards other areas of the business that require investment.

To summarize, outsourcing alleviates the scarcity of AI expertise in the industry while also being beneficial for saving time and resources, increasing revenue, and promoting specialization within a company.

4. Scalability and flexibility

As businesses strive to stay ahead in the market, they must be able to adapt to changing demands and grow accordingly. As we already know, by now, the traditional approach of building an in-house team to handle these projects can prove to be both impractical and expensive. On the other hand, outsourcing AI talent offers a solution to this problem.

By leveraging the expertise from outsourced AI professionals, small, and growing companies can level the playing field with larger competitors, without sacrificing the quality of the final product. An in-house team is usually limited in size, but outsourcing allows for scalability, so that you can expand and contract your development team as needed, without incurring additional costs.

Additionally, choosing a provider with different areas of expertise can help meet the diverse needs of your ML project. This approach allows for greater flexibility to scale up or down as required.

The AI industry is growing rapidly, but the limited availability of AI talent is hindering the adoption of AI in production. To address the talent shortage, companies partner with AI consultants who are bridging the skills gap, and providing instant access to data and tooling. Outsourcing AI projects to specialists can also result in shorter time-to-market and cost savings compared to in-house development. We have seen this results in a multitude of industries, by working closely with our partners to tackle different use cases for artificial intelligence.

But before suggesting that you contact us, we would like to provide some advice on what to consider in order to select the ideal AI consultant team.

1. Investigate the team: Make sure to research the team's previous projects, outcomes, and achievements. Look for a portfolio, research their content and team on LinkedIn or Google.

2. Schedule a consultation call: Set up a call with a team representative to discuss your AI project proposal and evaluate their communication skills. Ask for reference from your particular industry and ensure that both sides understand each other's expectations.

3. Technological expertise: Check if the team has the necessary skills and expertise in machine learning, project management, and data science. Additionally, ask the team about their project management process. This can give you insight into their level of organization and planning, as well as the steps they take to ensure the success of a project.

Ready to find the best AI consulting team for your company? Get in touch and ask us the tough questions!