Introduction

According to a survey by Omdia, 42% of companies allocated $1 million or more to AI projects in 2023. However, due to the increasing demand for good AI Engineers, even a million dollars aren’t proving to be enough to productionize quality AI models that actually give a return on investment. To resolve the predicament, companies in US and EU are increasingly looking towards countries like India to supply quality AI talent. However, if companies decide to build an in-house team by sourcing talent from the third world countries, they are not able to leverage the price arbitrage effectively. The only other option is to outsource the projects to these countries. But how good is this approach? Can companies realistically get results by outsourcing such critical projects? We will explore this through this blog.

Some Benefits of Outsourcing

As discussed in the introduction, outsourcing is not just an option, but a necessity to a lot of companies today. Here are some of the biggest benefits of outsourcing

[1] Access to Expertise: Companies doing AI projects day in and day out have built considerable expertise not only in the technology, but the industry as well. They know the exact use cases that can be addressed using the technology.

[2] Cost Savings: Outsourcing to a country with lower income like India or Ukraine can deliver up to 75% cost savings on an AI project due to the difference in salaries of an Engineer at a similar level.

[3] Flexible bandwidth: Outsourcing companies have access to 15-20% bench strength, that can help you increase your budget allocation on a critical project immediately.

[4] Focus on Core Competency: If your business is not a Tech company, then building a full fledged Tech team for a few AI projects does not make sense. Outsourcing allows you to complete smaller duration projects without building an entire team.

Potential Challenges of Outsourcing and Tackling them

Even with the strong list of benefits, outsourcing is not as widespread as it is supposed to be, thanks to the challenges. Let’s explore the major ones

[1] Data Privacy: Most of the AI projects deal with huge amounts of data. Training an AI model to an accuracy above 90% is no easy task. This requires GBs to TBs worth of high quality data. However, for outsourcing, this data needs to be shared with the AI partner so they may be able to build a model on top. However, companies always have an apprehension towards sharing such quantities of confidential data. Even a single source of leakage can cause a PR disaster for the company which the company may never recover from. Moreover, some countries have restrictions on sharing of data outside the country of origin. This makes it difficult to outsource the work to an agency based in India or Ukraine. Hence, ensuring security and privacy of the shared data remains one of the top challenges to outsourcing. To mitigate the risks, enterprises can first look at providing synthetic data instead of actual data. However, synthetic data must be modeled as close to the actual data as possible. Secondly, try to ensure strong data governance policies and cement them into legal contracts before sharing of any data. Thirdly, share as little data as possible, and leave the rest to data augmentation. Lastly, enterprises should ensure that all data is anonymized and encrypted, so that it can only be used for model training and nothing else.

[2] IP Concerns: Legally, most AI partners should transfer the entire model to the customer, and not use the trade secrets learnt in one project to make another one for a different customer succeed. However, that is one of the ways for AI consultancies to scale, and it is tough to put tight restrictions on usage of knowledge. Hence, outsourcing of core products that shouldn’t fall in the hands of their competitors can only be outsourced to reputed agencies with airtight legal contracts in place to prevent any leakage. Having a personal relationship with the founders of the agency is one way to provide yourself with the confidence required to outsource critical tasks.

[3] Quality Control: One way to check for quality is to monitor output metrics like increased customer satisfaction, reduction in labor requirements, increased revenue, etc. However, it isn’t always possible to determine success of these metrics in the long term. So, unless you have a team that can monitor the quality of AI model against a diverse data set, or monitor the quality of the code, it is difficult to rely on outsourced work in the long term. In such cases, prototypes and MVPs can be built using outsourcing, as these aren’t productionized, but are used for pilots. Alternatively, you can go for agencies with an incredible reputation for high quality of code and delivering high model quality.

[4] Communication Challenges: The challenge of working with a team that you don’t know personally, and sits in a different location than yours, can’t be understated. Effective collaboration can potentially boost the productivity of teams by up to 1000%, which can make or break a project depending on its success. Hence, it is important to have trial runs with your partners before finalizing one for a long term engagement.

Concluding Remarks

Outsourcing can turn into a boon or a bane depending on who your outsourcing partner is. Today, there is no dearth of talent in any country, and countries like India are leading in AI adoption on the world stage. Hence, there is no shying away from outsourcing + offshoring for any leading company in the world. If you are deciding between building an in-house team in India or outsourcing, consider the pipeline of projects that you currently have, and the growth you are expecting in that pipeline. Unless you have an AI strategy that you want to implement over the next 5 year period, it is beneficial to outsource the current pipeline of projects to an agency. I have explained the thought behind this in another blog.

Now if you are deciding to outsource + offshore your AI development, it is very important to choose the right vendor. If you are looking to “cheapshore” instead of offshore for critical projects, you will face disappointment without fail. You will burn money and see no results. However, if you choose the correct partner to work on your project, you will assuredly see a high positive ROI. Choosing the correct partner is an art, just like recruitment.

The mindset while choosing a partner should be to build a win-win relationship, instead of exploiting the partner for maximum short term gains. Make sure that value is added by the partner upon project completion, and make sure that the partner receives a fair share of the pie. To ensure that the partner is able to create a pie that is large enough, check for the partner’s talent pool, network of Subject Matter Experts, prior experience in similar projects, and finally, your rapport with the Founders. The last one is often undervalued, but turns out to be the most important point of all for a long term relationship. Having trial runs at the beginning helps in figuring these things out.

Once you get the right partner on-board, it is a matter of time before you can turn the pipeline of AI projects into real money for your company.

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