Today, AI already plays a key role in our daily lives. It has been proved capable of introducing new sources of growth, changing how work is done, and helping you to better serve your customer. Artificial intelligence (AI) is the ability of software to do tasks that are usually done by humans and which require anything remotely like the intelligence of a human being.
However many people when they speak about Artificial Intelligence are thinking about deep learning, machine learning, or natural language processing. In reality, all these terms are only subsets of AI and are only used in a few Artificial intelligence solutions.
Using neural networks algorithms inspired by the human brain to learn from large amounts of data.
Giving machines the ability to learn without being explicitly programmed.
Natural Language Processing (NLP)
Giving a machine the capability of "understanding" the contents of documents.
The main challenge of an AI initiative is not to define which neural networks or NLP algorithm to use. You will first have to define how AI can bring value to your business. The good news is that there is a very large set-up of opportunities and a few are listed below.
Automated customer interactions
AI helps you to better serve your customer with services as:
Service desk automation
Collecting and evaluating customer feedback
Early detection of major incidents
Insights and value from data
AI helps you to continuously improve with services as:
Gathering product and customer Insights
Detecting micro-trends for tailor-made marketing & campaigning
Personalizing product recommendations
Improving KPIs & creating new business opportunities
Your AI Journey
We apply a proven methodology to support our clients in their AI journey which is based on six pillars:
Roadmap of initiatives
Create a list of AI projects, derive a roadmap that prioritizes the identified projects and define KPIs and objectives to measure impacts.
Organization and governance
Define the needed skills and organization and setup a set of guidelines and rules as well as an orchestration team to drive the implementation.
Select and feed the systems with relevant datasets.
Framework and rules
Select or develop suitable algorithms and setup or train the algorithms to reach the desired outcome and integrate with applications.
Leverage a secure cloud infrastructure to operate at scale (quantity of data, respond time, …).
Conduct user research and create as well as test prototypes of different fidelity levels to ensure a desirable solution is implemented.