DATA & AI DIAGNOSIS
If you want to assess the potential of your company's data, if you do not know what is possible today with state-of-the-art technologies or if you want to be aware of the risks and opportunities that Artificial Intelligence present for your activity, we recommend that you start with a data diagnosis.
Scopeo experts will support you through several steps:
- Identification of data available internally, in the public domain and from private sources
- Definition of a few applications that would best meet your challenges
- Writing an achievable action plan with a measurable ROI
Thanks to our iterative approach and our experience in various industries, you will have a reliable idea of the options available to you to get the most out of your data.
Data & AI diagnostic methodology
Phase 1: evaluation
Investigation
By requesting access to the right people, i.e. business experts on the one hand and IT managers on the other, we become aware of the company's challenges, its activities and its data streams. We request a description of the infrastructure and application ideas already identified, and take an in-depth look at key activities, unique assets, market position, available resources and digitalization. The objective is to assess the maturity of the company on the data front and identify the main gaps, the potential for value creation/loss (risks and opportunities), particularly regarding market evolution, growth and the appearance of new data and economic models and the projects to be implemented
Full data access and exploration
We request full access to the available data in order to explore its content and determine signal quality and quantity. We transform and visualize data to answer our questions with multivariate statistical analyses. It is not a question of producing an algorithm at this stage, but simply of estimating the information contained in the data. In the case of an artificial intelligence application based on very mature algorithms, we will carry out tests to estimate the performance of these algorithms on the client's data.
At the end of this phase, we identified the main potential for exploiting data and artificial intelligence technologies which are linked to the client's activity and situation.
Phase 2: definition of the framework
In this phase, we look for projects with high added value for the client company and assess the associated risks. The objective is to propose a breakthrough strategy project for the client and not a gimmicky application.
Co-construction
The conditions for implementing a disruptive project are established by orchestrating joint reflection with management to identify priority areas of transformation. We identify one or more AI use scenarios by performing a review of options, evaluating key indicators and formalizing the challenges to be addressed.
Conditions for success
For each project identified, we make an inventory of the conditions necessary for its implementation: constraints on data, on the IT infrastructure, legal constraints, rapid confrontation with the user, minimum level of performance to be obtained, authorizations to be obtained … This step allows a project to face all the tasks, conditions, obstacles and difficulties that will have to be addressed during its development.
Impact of success
We discuss with the client and quantify the impact of the identified applications, roughly estimate the scale of the work involved and their costs and choose a reduced number of AI projects to maximize the return on investment.
At the end of this phase, we chose one or more artificial intelligence project use cases.
Phase 3: roadmap
Work inventory
We take inventory of the data to be collected and any annotation work to be carried out as well as the important constraints for the creation of a quality training dataset.
We then take inventory of the work to be carried out to implement an MVP (“Minimum Viable Product”) and estimate the cost more precisely. We identify one or more methods depending on the maturity level of the targeted technologies.
Recommendations and writing an action plan
Based on previous analyses, we establish an action plan to optimize data management if necessary, and define the transformations required to implement the identified AI use scenarios. This plan includes project prioritization, risk and opportunity assessment, and implementation planning. We thus set up a project development roadmap from the current state of the project to the production of an MVP by clearly showing the blocking stages and the dependencies of the project.
We issue recommendations which aim to give the manager the information necessary to move forward towards subsequent stages, such as a feasibility study or the launch of a strategic project.
At the end of this phase, the client has a roadmap to implement its disruptive strategy with one or more use cases to try, and all the cards in hand for implementation, either with Scopeo if necessary. is relevant and that he wishes, or by an alternative method (internalisation, integration of an off-the-shelf tool, etc.)
F.A.Q.
You don't need anything in particular, but you should be willing to give us a few hours a week to answer our questions and give us access to useful data.
The price of a complete Data & AI Diagnostic is 13 euros excluding tax.
Tell us about your situation to test your eligibility.
The Data & AI diagnosis aims to lift the veil on the risks and opportunities and to build an action plan to launch one or more data and/or AI application projects.
Yes ! The BPI is offering a grant for the Data & AI diagnostics.
The AI diagnostic is designed to assess the current state of the company's IT infrastructure, skills and processes in relation to the possibilities offered by artificial intelligence. It helps identify potential areas for improvement and innovation using artificial intelligence, assess the company's maturity in terms of data structuring, and define a strategy to effectively integrate artificial intelligence. in operations. This diagnostic helps understand the challenges, risks and opportunities associated with the adoption of artificial intelligence.
The AI diagnosis is primarily aimed at medium-sized companies and all sectors of activity that wish to explore or improve their use of artificial intelligence. It is particularly useful for business leaders, IT (CIO) and innovation (DI) managers and decision-makers who are considering integrating artificial intelligence into their processes. This diagnosis is particularly relevant for companies that are at the beginning of their AI journey and looking to understand how they can benefit from this technology.
Artificial intelligence is an expression whose contours are quite vague, but we can differentiate Artificial Intelligence based on:
– what they take as input (image, text, Excel tables, sound, etc.)
– the task they are asked to do (question-answer, segmentation, detection, forecasting, etc.)
– the type of algorithm used (neural networks, gradient boosting, auto-regressive models, etc.)
Recent advances in generative AI have revealed to the world the significant potential for productivity and efficiency gains from these tools, but many other types of algorithms exist and can also have a significant impact on the transformation of a business.
Artificial intelligence can be defined as the simulation of human intelligence by machines, especially computer systems. It involves the development of systems capable of learning, reasoning, perceiving, understanding human language, and solving problems. This definition encompasses a wide range of technologies, algorithms and techniques, from machine learning and natural language processing to robotics and predictive analytics. AI aims to augment human capabilities and automate processes for increased efficiency and new possibilities for innovation.