Putting AI to work for your business
Technology only ever has one mission: to deliver business value that can withstand the test of time and everyday emergencies. AI is no exception to the rule, and behind the media hype, this is the only yardstick that matters.
However, the evidence stops there and leaves many players stranded in midstream. Find out how we propose to help you move forward.
Four phases make up our AI approach to support our clients:
1 Onboard & Inspire
2 Target & Prioritise
3 Audit & Design
4 Operate & Adopt
4 drivers to move forward
Through our consulting, training and implementation offer, we propose 4 actions to take advantage of what Artificial Intelligence has to offer throughout your transformation project.
Onboard and Inspire
Developing creative thinking around artificial intelligence requires a wide gap between understanding how the technology works and an overview of its applications.
Why is it so difficult?
Thinking about technology is never an easy exercise. With artificial intelligence, the usual difficulties are often added to the usual difficulties:
- The injunction to transform everything: not knowing where to start
- Abstract concept: low mobilisation or lack of interest in the trades
- Unclear concept: difficulty in deploying creative thinking about AI
- Poor feedback: poor evaluation of effort, etc.
Artificial intelligence is elusive and can have a demotivating effect. It is the first lock that needs to be tackled.
To see more clearly
Confronted with the fascinating and confusing landscape of artificial intelligence, we suggest that you take a closer look:
- Customisable popularisation conference. Ideal for mobilising your teams
- Our inspiration paths that map the use cases in production
- Our ideation workshops designed to identify the potential of AI on your perimeter
Target & Prioritise
The potential of artificial intelligence is so vast that it is sometimes difficult to know where to start, where to go first.
Building the roadmap together
The prioritisation of subjects requires two complementary analyses: the value analysis on the one hand, and the effort analysis on the other.
The value analysis makes it possible to identify what artificial intelligence is able to bring to each use case. Whether it is a reduction in time, effort, costs, risks, a gain in quality, information or scaling, it is a question of identifying the main levers of value creation. Our experts are there to help you identify them.
Effort analysis consists of confronting project ideas with the difficulties and challenges that lie ahead. How much data can be gathered within the given timeframe? How reliable is it? These are all questions that enable us to anticipate the project’s hard points and make an initial estimate of the load to be expected.
Mapping use cases on these two axes then makes it possible to very quickly identify the procedure to be followed, the subjects to start with, those to be discarded and those to be reserved for later.
Audit & Design
The implementation of artificial intelligence projects requires a team of qualified experts: from Data Scientist to Machine Learning Engineer, each brings a unique vision to the success of your project.
A team of specialists
The democratisation of artificial intelligence should not hide the difficulty of this type of project, and the spectrum of skills that it is necessary to bring together to carry them out.
Beyond the usual questions of software engineering, it is necessary to ensure the proper management of data (governance, versioning, processing and storage at scale), the traceability and reproducibility of the experiment plan, and the deployment and management of models capable of ensuring an appropriate level of service. All of this while remaining focused on the use value of the system under development.
Whether in-house or outsourced, entrust these subjects to a team of specialists, who will be able to interact with your business teams to get from the idea to the solution, smoothly. The SCALIAN AI Consulting teams have been carrying out this type of project on a daily basis since 2015.
Operate & Adopt
The missing link between POCs and value creation: operationalising and operating predictive services.
POCs and value creation: operationalising and operating predictive services
Machine Learning projects often lack operationalisation. The model is ready, it works and its performance has been validated. But the next step, deploying the service and making it accessible to end-users, requires new expertise.
Good practice is to integrate these considerations from the start of the project. We generally aim to deploy a model at the end of the first sprint. Subsequent iterations will improve the performance of this model, but deployment is no longer an issue. It is never too late to take up the subject and we can also help you to operationalise an existing model.
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