Are you planning to implement Artificial Intelligence (AI) in the future, as are 46% of CIOs according to Gartner? We've compiled a shortlist of crucial advices extracted from the trenches to avoid common pitfalls and for you to get fast, impressive, and inexpensive results. Let's begin with an understanding about AI to know how it can assist you.
1. Get a clear picture of AI
AI is a branch of computer science that makes a machine think, learn and adapt, as if it had a brain. What does that mean exactly? AI is in fact the most advanced data analytics tool of data science, an R&D process that enables you to solve business issues, using the scientific method to prove or disprove hypotheses. So how can AI help? At Wizata, we think of AI as a revolutionary technology that can empower professionals in their daily jobs by exploiting data in a totally new way to discover new opportunities. The key technology powering our advanced AI deployed in the manufacturing sector is machine learning, a technique that builds expertise by assimilating past data.
2. Focus on specific business issues
Trying to implement AI everywhere without thinking of what you’re looking to do is unlikely to work, as would data science without knowing what to test. It’s therefore crucial to focus on concrete problems you’re aiming to solve, so that you narrow down what to test: are you looking to maximize yield? Are you wishing to detect quality problems before they happen? Are you having difficulties to find the root cause of faults? Are you aiming to lessen your costs by reducing scrap? Is bringing down energy consumption a priority? Are you thinking about predictive maintenance to maximize the uptime of your machines? What is making you lose or win the most money? What could impact your future the most?
In the initial stages of a project, some clients can generate an enormous amount of ideas, while others struggle to see how AI and data science can generate real value. Our expert data scientists can either help you prioritize emerging concepts depending on data quality and availability, or help you produce testable hypotheses and suggest further research.
3. Dream big but start small
To build trust in what AI can do to enhance your business and to get fast and clear results, it’s important to start with small projects. It’s great to enable free flows of ideas, to consider numerous use cases, and prioritize those possible plans, with the help of a partner if needed. To determine what to focus on, you can evaluate the practicability of your ideas before forging ahead (at Wizata, we do this during the “exploratory workshop” phase). It’s also quicker to tackle specific problems and then expand to general matters and grand visions than the opposite. The more specific you are, the more data scientists can determine the right algorithm and the data they need, and the more your employees can help pinpoint the issue to specific data and formulate concrete hypotheses that you can test.
4. Involve the right people
Companies often work in silos, especially for knowledge, inhibiting the emergence of novel ideas. If possible, set up an innovation team to lead the projects independently and to brainstorm with key impacted stakeholders. Thinking horizontally with a view on all the departments instead of just an IT or a business perspective allows this team to collect the needs of every part of your company and to develop a global strategic project. Eventually, this team can act as an ambassador for the future of your company, sharing the results of this collaboration and expanding on the resulting solutions that enhance business processes company-wide.
5. Experiment from the beginning
A common mistake is to be thinking that you must accumulate a large amount of clean data in a centralized system to begin data science and AI experiments. Moreover, it doesn’t require a large budget or a considerate amount of data and you can invest little by little depending on the outcomes, so don’t refrain from evaluating your ideas early on. At this early stage your goal should be to figure out what to do and to find a good data science and AI expert. What you need to start is just the right people around a table: prioritization of possible use cases and ROI evaluation will help you to get the right focus and decide the next steps that should be taken.
6. Balance the importance between quick and accurate results
If you’re generating 70% of correct AI predictions in three months of experiments and could enhance it to 80% at the cost of two years of research, would it make sense to do it for your business? Don’t hesitate to calibrate the results you’re aiming for with your innovation team and your AI partner. At Wizata, you only pay for the results you’re getting.
Contact our experts
Want to start your own AI project and use predictive analytics to turn data insights into foresight? Check out what advanced AI can do with prescriptive analytics and don’t hesitate to contact us via email@example.com to start evaluating your vision or to check the viability of its sub-components in an exploratory workshop. If you have questions about the data you'd need to develop an AI project, be sure to check our interview with Ziad Benslimane, Lead Data Engineer at Wizata.