‘AI is the runtime that is going to shape all of what we do going forward in terms of the applications as well as the platform behaviour.
‘Machine learning is the most transformative technology of our time. It’s going to transform every single vertical’.
Satya Nadella, CEO of Microsoft
Artificial Intelligence (AI) and Machine Learning (ML) are two rapidly advancing technologies that are already transforming the technological landscape. Both are having a significant impact on many fields including healthcare, finance, transportation and entertainment.
Experts and industry leaders across the world understand the impact that both artificial intelligence and machine learning for business processes will make, and how they will shape our world and give them a competitive advantage.
‘AI and machine learning are becoming more and more ubiquitous in our everyday lives, revolutionising the way we interact with technology and with each other’.
Fei-Fei Li, computer science professor & AI expert
‘Machine intelligence is the last invention that humanity will ever need to make’.
Professor Nick Bostrom, Swedish Philosopher at Oxford University
While artificial intelligence and machine learning outsourcing certainly hold enormous potential for businesses and their business processes, developing effective solutions can be complex and highly resource-intensive. Because of this, many companies are opting for AI outsourcing and ML outsourcing to external partners. In this comprehensive guide, we detail the benefits, challenges and key considerations of AI and ML outsourcing and how to effectively outsource AI and ML projects, choosing the right partner and maximising benefits.
Why should your company consider outsourcing machine learning and artificial intelligence projects?
Artificial intelligence and machine learning outsourcing projects can be beneficial for businesses looking to leverage the latest technology without investing heavily in infrastructure and talent acquisition. AI outsourcing in particular is not a new concept, but there is a noticeable shortage of artificial intelligence and machine learning engineers across the UK, USA and the EU which can make it very challenging to find a dedicated team with expertise in developing AI and machine learning strategy. Because of this, AI and ML consulting is a valid and increasingly popular activity in business, as outsourcing AI and ML development services can offer companies access to greater knowledge, resources and data science tools.
Outsourcing AI projects also helps businesses to scale up or scale down their projects according to their needs. It offers access to specialised expertise, cost savings and faster time-to-market for AI and ML solutions.
It can provide scalability and risk management, as businesses can work with experienced partners with a proven track record of delivering successful AI and ML solutions.
Accenture’s 2022 AI report (1) found that over 75% of companies are already integrating AI into their business strategies, making it a significant value driver. They also went on to say that there are indications that AI transformation may occur faster than digital transformation.
Ultimately, outsourcing their project management for AI and ML projects enables businesses to leverage the latest technologies without incurring significant costs and risks, thereby helping them stay competitive in today’s fast-paced business environment.
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The pros and cons of outsourcing AI and ML processes
There are a number of pros and cons for companies to consider when deciding on whether to outsource some or all of their artificial intelligence and machine learning project versus carrying them out via their in-house AI team.
- Specialised expertise: outsourcing AI and ML services offers companies access to highly specialised experts that they may not be able to find in their in-house teams.
- Cost savings: outsourcing negates the need for companies to hire staff or invest in additional infrastructure, saving money. Depending on the outsourcing location, labour costs may be lower as well.
- Faster time-to-market: this is due to greater access to dedicated development teams and highly specialised expertise that improves quality and reduces the time required to complete the projects.
- Scaling projects: outsourcing AI and ML projects allows companies to scale their operations up or down according to their needs and without incurring significant infrastructure or personnel costs.
- Risk management: risks can be more easily managed when outsourcing as the team of experts will have a proven track record of delivering successful artificial intelligence and machine learning projects.
- Lack of control: by outsourcing, companies risk losing control over the development process, leading to communication breakdowns that can affect the quality of the results.
- Communication issues: companies may face communication problems if, for example, there are language barriers, cultural differences or time-zone conflicts. This impacts the business, resulting in less-than-efficient collaboration between the outsourcing partner and the business.
- Quality compromise: at times, outsourcing may result in lower-quality results than if the task had been done in-house. Quality is particularly at risk if there are any communication breakdowns that lead to misunderstandings.
- Security risks: working with an external outsourcing partner risks data breaches, loss of sensitive data and possibly even intellectual property theft.
Key considerations for successfully nearshoring ML and AI projects
While offshoring (when companies move their operations or manufacturing to another country, primarily for cost-saving reasons) is a widely adopted strategy that has been taken by companies for many years, another trend has started to gain momentum – nearshoring.
Nearshoring is a term that refers to relocating a company’s operations or manufacturing to a nearby or neighbouring country (as opposed to a significantly far away country).
Nearshoring is a highly popular global trend that shows no sign of slowing down. According to Bloomberg (2), 80% of companies in North America are actively considering nearshoring. In addition, it helps to cut costs significantly, with a staggering 59% of companies choosing nearshore software development as a cost-cutting tool, according to Deloitte (3).
Below are some key considerations that companies need to take into account when seeking to successfully nearshore outsource their machine learning and artificial intelligence projects. The nearshoring partner should have:
- expertise in ML and AI technologies and applications. This includes knowledge of algorithms, platforms and tools used in ML and AI development
- a cultural fit with the business to ensure smooth collaboration and communication – this includes language proficiency, time zone compatibility, and similar work ethics and values
- intellectual property (IP) protection policy – it is crucial that companies protect their IP before outsourcing their machine learning and artificial intelligence projects, this includes implementing data protection policies and non-disclosure agreements
- cost policy since the cost of nearshore outsourcing the company’s artificial intelligence and machine learning projects must be reasonable and viable
- quality assurance process in place to ensure high-quality results that are accurate and reliable
- project management process in place to ensure that projects are delivered on time and within budget
- clear communication channels and a plan in place to ensure timely and effective communication between both parties
How to effectively outsource AL and ML projects?
Outsourcing AI and ML projects can be a fantastic option for companies to propel their operations. However, in order to outsource in the most effective and efficient manner, businesses need to follow certain best practices. Here are some key steps for effectively outsourcing AI and ML projects:
- Define your project goals: the problem to be solved, the target audience and the desired outcomes.
- Collaborate with the right partner: they should have expertise in AI and ML technologies and applications and a proven track record.
- Build effective communication from the outset: regular updates, meetings and reports will help, along with clearly defined roles and communication frequency agreements.
- Fix a project plan: develop a detailed project plan, including timelines, milestones, deliverables and budget. Identify potential risks and develop a risk mitigation plan.
- Ongoing progress monitoring: track milestones and deliverables against the project plan and adjust as necessary.
- Maintain quality assurance: effective QA (including testing and validation) is essential to getting high-quality results.
- Protect intellectual property: establish non-disclosure agreements and data protection policies.
Choose the right AI and ML partner for your business
Choosing the right partner to outsource your artificial intelligence and machine learning projects to can be difficult. Who would be a good fit for your company? Can you be sure they have the required expertise? Will there be any unforeseen issues? These are all good questions that cannot be easily answered.
First and foremost, expertise is crucial. Your potential outsourcing partner must have the required knowledge and ability to actually carry out the tasks required, by having a deep understanding of the algorithms, platforms and tools used in AI and ML development.
Going hand-in-hand with expertise is experience. Your outsourcing partner should have enough experience in the relevant industry so that they understand your business needs and can develop solutions that meet your specific requirements. A suitable partner with significant experience will have a proven track record of high-quality work and will be able to produce case studies and references to verify this.
Two further important factors to look out for that were previously mentioned were good quality assurance practices and effective communication. Both of these are extremely important when choosing an outsourcing partner.
Another important consideration is the cost and value proposition of the outsourcing company. How much will outsourcing your AI and ML cost you and what will you be getting for this fee? Make sure to consider the following:
- Pricing models
- Cost vs value
- Ongoing support and maintenance costs
Maximising AI and ML outsourcing benefits: Tips for successful business collaboration
There are a huge number of benefits to outsourcing which is why so many companies across the globe are adopting this strategy. In order to maximise your outsourcing operations, here are our top tips for successful business collaboration.
Firstly, make sure to establish the right strategy along with your outsourcing business partner, as this is essential for the success of the project. Experienced outsourcing companies will be able to work collaboratively with you to create a comprehensive plan of action, assign team members to the appropriate roles, and implement necessary strategies at each stage.
Secondly, ensure that your outsourcing team has the required specialist knowledge and up-to-date technology. Highly qualified and experienced professionals can ensure that the project is delivered on time and within budget.
Thirdly, groundwork preparation is important for creating a successful environment for remote software development. This requires a shared understanding between your business and nearshore outsourcing vendors regarding the desired workflow, which can help streamline the process.
Finally, performance analysis is necessary to evaluate all the details and aspects of the project and determine potential results. Your outsourcing partner should continuously analyse the progress results and report back to you regularly.
By considering these factors, businesses can maximise the benefits of outsourcing for AI and ML projects and ensure successful outcomes.