Apart from the solutions most frequently asked by our clients, such as demand forecasting and fraud detection, each organization and industry is unique having its very own AI opportunities. Our team has significant experience in exploring new domains, problems and data (we actually enjoy it!) and we have a successful track record delivering for them. These custom solutions may have to do with some of the following:
- Automation of an existing business process
- Reengineering of a business process i.e. amplifying process intelligence or introducing data-driven sub-processes
- Product development, i.e., new AI features you may want to add to your product
Whatever the case is, it all starts with a series of our signature structured sessions which will help us formulate the problem or process at hand in computational terms, understand your needs and your expectations from the aforementioned solution. These structured sessions may also allow you to understand the problem at hand more profoundly or even challenge the existing processes, leading us to the critical question “Optimize or Disrupt?”. Depending on the current status of your organization, business strategy and industry, the answer to this question will be different and we can you help you understand the differences and the expectations for each path you might take.
Contact us for more information on the process we follow to design and develop a custom solution exactly fitting your needs and to learn more about relevant successful cases we had in the past.
We offer a premium AI consulting service through a series of structured sessions to answer all your questions and develop your very own AI strategy. This process is based on the following four-fold:
ΑΙ and Machine Learning are not the “jack of all trades”. The process you want to optimize or the problem you want to solve may have better or simpler solutions. In this first part of our series of sessions we will mostly focus on the following:
- Is the process you want to automate or optimize suitable for an AI solution?
- What are the key strengths and weaknesses of developing an AI strategy in your organization? How are they related to your expected results?
- Is your organization ready to initiate AI projects? This question is related to data governance, technology infrastructure and human resources.
- Should AI be part of your core business? And the related question of whether you should develop an in-house AI team or trust an external partner.
AI strategy development
Ιn this phase we can provide you with an understanding of the state-of-the-art AI that is related to your unique case. Mostly based on your data strategy, products and industry we can build a plan of AI adaptation, identifying the most suitable pilot AI project and the steps towards building your AI technology environment. The AI strategy that we will formulate together will result in a number of initiatives and these initiatives will be mapped to specific implementation steps.
Starting to implement your AI strategy is already a big step for your organization towards ripping the actual benefits of AI solutions. In this phase we focus on timid and precise execution of the following steps:
- Preparing your technology infrastructure. Whether that is a cloud service provider or a distributed computation framework, we can propose vendors and technologies appropriate for your case and taking into consideration the technologies you already use
- Building a team of product owners, professionals that will communicate business requirements to AI engineers
- Building your AI evaluation metrics as to be able to measure the effectiveness of any solution developed, related to your business goals and KPIs
Train and prepare your business and product owners for the AI era. In this context, our contribution can be that of training and preparing your people so as to be able to define requirements and specifications for your AI engineering team, whether that is in-house or external. This is crucial since the most significant threats for the success of an AI project are misspecification, poor evaluation metrics and inefficient communication between the business and technology teams. Eventually, the training phase will result in a culture shift in your organization, that of developing and adopting effective AI initiatives.
- SWOT analysis