In 2021, several corporations have involved AI tools and automation in their daily operations. However, there continues to be a degree of mystique and uncertainty regarding the technology’s ability to guarantee optimal performance and efficiency in work operations. The uncertainty stems from the fact that most organizations using AI-powered systems hire third-party service providers to install, repair, and update the technology for them. This takes away some control from the organization’s hands. To execute AI implementation and maintenance on their own, the chief decision-makers in such organizations must assemble a dedicated team of experts. An in-house team can bring the much-needed finesse in AI’s functionalities while being able to carry out the regular system updates and repairs without hassles. This piece explains the reasons why you may want to have an in-house team to set up and manage your organizational AI tools and processes in a hands-on way.
Cross-Platform AI Testing, Implementation, and Maintenance
Big organizations oversee several operations that cover each of their distinctive product or service lines. In many cases, these operations may be carried out across different hardware and software platforms. As a result, AI tools need to be involved in many cross-platform operational channels. This is where an AI team’s contributions are needed. The initial AI implementation phase in an organization involves several datasets, algorithms, data models, and machine training to set up the intelligent technology extensively across all those platforms. Additionally, these data models and algorithms need to be updated with time.
Naturally, depending on outsourced service providers may not be the best or most practical option for organizations to handle such processes (from a time and convenience point of view). Therefore, having an AI team helps as the experts involved in them are deeply knowledgeable and experienced in the field of data modeling and machine learning. They understand the sequence of tasks that must be carried out to get the AI systems up and running. An AI team can work cross-functionally with all the line managers to develop fine-tuned AI applications for each operation. The performance quality of these applications depends on the coordination between the managers and the AI experts.
As specified earlier, AI-powered systems will be used to simplify different operations. For example, in an automobile manufacturing company, AI systems will be involved in the conception, design, and creation of interior components (steering, switchgear, upholstery), suspension and powertrain systems, each process vastly different from the other. Seeking external help to manage AI’s involvement across these operations can pose problems for organizations from time to time. Also, it is difficult to attain data experts who have the knowledge to be involved in every operation. An AI team, on the other hand, will have individuals who have the required know-how to test, implement and update AI applications in all the areas of an organization.
Secure and Convenient Data Labeling
It is rightly believed that AI is only as good as the data used to train it. The quality and size of training data dictate how useful or effective an AI model will be for various organizational operations. Therefore, a large percentage of time and effort put into the implementation of AI in organizations is spent on collecting and decluttering raw data for machine learning purposes. Data labeling is the process of classifying the collected information into organized datasets that are ready to be used for machine training. The process involves identifying and tagging data samples that are gathered for supervised machine learning in organizations. Labeling is a crucial step in data pre-processing while building elaborate AI models and systems.
An AI team’s high-quality data labeling skills can be incredibly useful for your organization. Firstly, the team can directly oversee the entire data annotation process during the machine learning phase. Thereby, their presence offers a greater degree of control to organizations over the AI initiation and implementation steps. Having an AI team supervising the entire phase can put managers and directors of an organization at ease during the AI implementation period.
Secondly, data security and confidentiality are the main reasons to employ a team rather than having external agents handle all the AI-related operations. Generally, organizations are rightly concerned about the possibility of their confidential data (used for training AI models) being leaked or transmitted to cybercriminals over the web. The prospect of third parties accessing the organizational data comes with its own set of data breach worries and a compromise of overall security standards. The presence of an in-house team ensures that data security-related issues do not land organizations in trouble.
Lastly, larger datasets necessitate the presence of an in-house team so that the data flow is not interrupted, and models and algorithms can be trained in the shortest time possible.
Flexibility and Customization in Operations
Another primary advantage of having an AI team in your organization is the sheer variety of options they provide for different operations. The management can take their time while making major operational decisions as they are not severely bound by time constraints. Additionally, the convenience factor is dialed up as the in-house AI experts are omnipresent to receive orders and requests from the management at all times. The team can also make operational improvisations on the fly. An in-house AI team is always on hand to handle customization requests from clients. As a result, organizational operations can be highly customized for each project at short notice. Basically, an in-house AI team is a part of an organization’s personnel, so they can understand the business requirements better and provide a greater degree of flexibility across all the operations. While outsourced data experts may also provide decent levels of customization and possess the operational understanding to some extent, they are comprehensively overshadowed by in-house AI experts in those aspects.
Other Reasons to Have an In-house AI Team
AI solutions can be a tangible asset for your organization. The recommendations, market estimates, and decisions provided by an AI-powered system can be possessed by the organization as their own Intellectual Properties (IP). This ownership is only possible if the AI models and systems have been developed in-house by an employed team of experts. On the other hand, seeking the services of external agencies and companies may not allow organizations to possess such assets. The ownership of these assets provides organizations with an edge over their market rivals.
An in-house AI team uses original ideas and innovation to come up with unique AI solutions. These ideas and solutions hold monetary value (as IP rights) in the balance sheets of organizations. Unique AI ideas and solutions can be worth billions in the financial market and, if they are developed in-house, can be a massive revenue source for organizations. As a result, having your own AI team allows organizations to boost their income along with other advantages.
As explained in the previous point, organizations can exert greater levels of control over operations when they have an in-house team at the helm of their AI operations. Managers within an organization can keep themselves updated about the status of a project while the AI team is working on it. This is not possible when organizations use external AI management companies for this purpose.
If organizations do not have any experience with the implementation and use of AI in their daily operations, they may face difficulties if they choose to adopt the technology in their daily operations. Additionally, they may find it challenging to find the right individuals or companies to carry out the tasks. Mind you, the third-party service providers will, first and foremost, need to have knowledge about your company’s core operations before commencing the AI implementation processes. There are many companies that claim to provide AI services to their clients, but there are several questions that may be posed by your organization.
Firstly, what kind of AI operations are the third-party service providers into? And how can their knowledge suit the operational requirements of your organization? These problems lead to third-party contractors not having any ready-made plans in place to go about the AI implementation process for your organization. Most importantly, it is tough to know how suitable a service provider is for your organization if their credibility is unknown. To know that, your organization may have to conduct deep research about all the various AI service providers available in the market. As we have seen, having an in-house AI team is simply the correct option for organizations as the presence of qualified and experienced experts at the helm of AI operations can mostly yield positive results in the long run.
