Applied AI & AI in business /

Enterprise onboarding

December 15 2021/6 min read

Organizations without basic knowledge of AI tend to search for unrealistic use cases. Teams without in-depth domain expertise seldom find valuable use cases. By combining the former with hands-on testing of your own AI models, we produce high engagement and fast adoption of the latest AI technology in your team.

In this article, we’ll go through what a typical onboarding process can look like for a large Enterprise, based on previous work performed with our current customers. We will touch on why onboarding is important and effective, the structure of the program, tips on how to prove value and then we wrap up with some key takeaways.

02/ Real-world onboarding

Operational AI is Everyday AI

The goal of operationalizing AI is just that - to put AI in daily practice across your organization. By placing the power of AI in the hands of domain experts, the AI models that will be used in the everyday work are ones thought of and created by the “non-AI-experts” of the organization, freeing up scarce Data Scientist/MLE time and speeding upscaling and time to value. Without the domain experts being enabled to come up with and validate AI use cases, the possibilities for value-increasing AI projects diminish significantly. The work done during the onboarding process is a crucial part of the journey to operational and democratic AI.

03/ Onboarding Quick Sheet

During onboarding, we work to enable and empower individuals and teams to solve AI problems on their own. The goal of onboarding for each domain expert who will be involved in the project is to have high-level knowledge on how to solve typical AI problems, and how to do this in practice using the Peltarion platform

The onboarding process is divided into three work streams, where our AI experts run a parallel of theory and practice on learning AI theory, training AI models and solving AI problems, so that non-AI expert teams are able to successfully and self-sufficiently ideate, train and try out their models, even put some of them into production. At the end of the onboarding process, each member should be able to achieve the learning goals by solving a problem of their own - from data collection to using a deployed model.

Good to know: Personal use-cases drive the progress and keep the motivation high, however, it’s not a necessity to have a use case when starting the onboarding, as we will also work with the ideation process to come up with use cases.

04/ Why is Onboarding important?

Onboarding is where we bring together the key ingredients to a successful and valuable AI Use Case while placing the power of AI problem solving in the hands of the actual individuals who can identify the problems and matching their desired abstraction level. 

The domain experts - the non-AI-experts - working in the business units are the ones who have the best knowhow on the information used and created, and the business value needed. Empowering them with AI-knowledge and the right AI-tool gives them the opportunity to develop and solve problems with AI in their field of work, allowing Enterprises to scale their AI efforts more quickly and more efficiently. 

It is through this process of technical capability enablement that happens during onboarding that Enterprises can bridge the different abstraction levels, from a technical abstraction level - of say, a data scientist - to the business abstraction level - of for example, the CEO.

05/ Proving value from the start

An important differential of having an Onboarding process versus not having one is that instead of going through a proof of concept to a proof of value journey, Enterprises start off at the proof of value stage - if there’s no immediate value in the use case concept, it doesn’t go forward, saving valuable time and resources and getting value from the start.

By starting out small and with business-focused use cases, you’re likely to create value much faster. Involving the domain and business experts from the start avoids prolonging the process.

06/ Key takeaways of Onboarding

The humans in the business are the ones who know and identify what they want to optimize for, and they know the regulations and the guidelines for best practice in their fields of work that they have to adhere to, and when that domain expertise is combined with theoretical and practical AI knowledge, teams can use the AI models to come up with all kinds of different solutions for valuable optimizations, and iterate on these challenges and find innovative ways to solve them in a completely different way from what they’re doing today. 

These newfound capabilities add significant value and increase the competitive advantage of organizations more quickly and affordably than when done without an AI tool and an organized process of democratized technical enablement. 

Through the onboarding process of concomitant theoretical and practical learnings, use case ideation, model creation as well as group sessions and individual support - teams should be confident in developing the ability to solve AI problems self sufficiently in their own projects.

07/ Next steps after onboarding

Typically, after the onboarding process above is concluded, Enterprises usually move forward with:

  • Supporting the individuals that have completed the onboarding sessions, allowing them to continue building their AI-solutions
  • Q&A sessions are arranged together with our AI experts to provide guidance in how to increase the scope of the already developed solutions or improve performance for example.
  • A general and permanent support channel is enabled or extended to the onboarded teams.
  • New teams are identified to be part of a new cohort, including individuals from the business lines.
  • New onboarding sessions with new groups are kicked-off (including licenses and access to the platform).

In conclusion, whether your Enterprise aspires to make complex predictions, improve your efficiency or has already identified an area where AI capabilities would give your organization the competitive advantage it needs to scale, AI holds the capacity to get you there. Through accessible AI - decentralized, democratized, affordable and operational - your organization can reach new heights.

Send us a message and take your Enterprise AI efforts to the next level.

  • Björn Treje

    Björn Treje

    Head of Technical Enablement

    Björn has a Master of Science in Electrical Engineering. He strives to put engineering into the business and business into the engineer. Secretly he hopes all projects involves helmets or reflex vests at some point.

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