Many enterprises that consider AI implementation don’t know how to go about it successfully. Companies complain that they don’t have the necessary in-house knowledge or skills, and building a team from scratch is too time-consuming and risky. Because the effective implementation of AI projects requires building interdisciplinary teams – both from business and technology – executing the process successfully is quite a challenge.
The answer to such a challenge may be to combine in-house and outsourcing models. In one respect, an in-house team provides crucial know-how and knowledge about their company’s operations. An AI vendor, meanwhile, provides cutting-edge knowledge of AI technology and a flexible, innovative approach to realizing the use cases. Ultimately, the vendor’s team becomes an integrated part of the client’s team. Sounds promising… but before starting cooperation some key questions should be answered.
Where is the business value?
The first element that needs to be decided is what business case the cooperation with the vendor should address. While enterprises are more and more certain about AI’s potential there is still a lot of hype around, making it difficult to decide which use case to implement first. It is not only about not losing money on an AI project, but above all about identifying such business processes where automation will bring visible business values.
The unique approach and work methodology developed by deepsense.ai allows us to quickly identify the most relevant use cases. Our cooperation with a client always begins with an opportunity discovery phase that helps map and understand the business needs. Joint ideation sessions define the main processes and data sources that can become part of the implementation. This approach makes it possible to efficiently determine the scope of cooperation and set up success metrics.
Who from my team will be involved in the project?
Effective implementation of AI projects requires building interdisciplinary teams. Ones that command extensive expertise in available technologies and approaches to data analysis, while at the same time possessing a thorough understanding of the business processes taking place in the enterprise. They must be able to recognize the factors that influence the core operations. As business challenges go, this one certainly qualifies as daunting. However, it is important to consider who – within the organization – will be able to become a partner for cooperation with an AI vendor. This person, or persons, should not only take care of the smooth development & implementation of the project, but also have a full picture of the business situation within the analyzed use case.
What kind of data can we share with the vendor?
The garbage in/garbage out principle is well known in data science. But, it should also be considered from a business stakeholders perspective. Certainly, most enterprises possess data that can provide valuable insights. However, it is important to review whether the dataset covers all data sources available in the organization as well as assess data quality and consistency. Usually the process of collecting and preparing data for analysis – to the customers’ surprise – takes more time than it was expected. To speed up the process, the deepsense.ai team always begins to review and analyze the data in close cooperation with the customer’s domain experts. At this stage, it is crucial to discuss basic questions, and check features and labels.
An additional element worth paying attention to is the data security. While working with an external vendor the knowledge of core business processes somehow goes beyond the enterprise. That’s why we always implement adequate security measures, such as encryption, network isolation, and strict data access policies.
How much time do I give to see results?
Valuable data analysis requires patience. Often, before reaching the final solution, several models are trained simultaneously using different perspectives and approaches. The duration of the cooperation depends on the specifics of the project. Usually it takes several months from the data audit and development plan to the operational rollout and commercialization of the entire project. The first working proofs-of-concept are usually presented within 4 weeks from the start of the project.
In what model do I want to work with a vendor?
At deepsense.ai we have the flexibility to work along two different models. The first one is focused on team augmentation. We work in cooperation with our customer’s teams to expand their technical skill-set within a broad set of roles. Our data scientists, data, and software engineers are ready to tailor to any specific needs. Our people are equipped with the full tech stack required to help clients reach their milestones quicker, and ready to work hand-in-hand with internal business and technical teams.
The second model provides end-to-end project delivery: from identifying the needs to commercial deployment. We also specialize in handling highly customized challenges for which ready-made solutions don’t exist.
The dynamic development of AI makes it difficult for companies whose core operations are not related with hi-end technology to keep up with the latest solutions. Close cooperation with an AI vendor allows them to maximize the potential of state-of-the-art technologies and focus on the industry-related aspects of building competitive advantage. We might go so far as to say that truly remarkable results are not possible without combining in-house and outsourcing models. At least not without a great deal of time and much larger financial outlays. Cooperation with a reliable AI vendor ensures flexibility, cost control, and also brings a fresh perspective on the data being analyzed.