Artificial Intelligence is already helping many industries to do things faster, smarter and more efficiently, and we’ve only just begun to explore the opportunities offered by this exciting technology. If ever there was a time to investigate how AI could help your business, it’s right now.
Get ready for an AI Proof of Concept
But how can you make the most of it? Where do you start? What’s the process? These pages will help you take the first steps to incorporating AI into your organisation. Maple can help, working with you to determine where AI can best add value and then putting together a proof of concept to take your business case forward.
AI is revolutionising the way the Life Sciences, Agriculture and Pharma sectors operate. Here are just some of the ways AI can benefit your organisation:
Improve image recognition and interpretation of CAT scan, X-ray and MRI, and looking for anomalies and potential.
New models of analysis:
Combine image recognition with genomic data and better analyse phenotypic or expression.
Drug discovery and analysis:
Use image training and recognition to improve molecular analysis (crystallography).
Step 1: Define your objectives
Today, the question has moved from why you might start an Artificial Intelligence project to when it should begin. Knowing where in the business to begin a pilot project is the first objective.
Consider your value potential
Consider your existing technology
Work with someone you can trust
- Where in the business could you use data for the most impact to help rapid business growth?
- Where do you find the most routine? For example, is the finance team always chasing late payers?
- Where might you save the most money? For example, does reducing customer churn offer the biggest opportunity, or is it speed of
- How would you compare the accuracy of your fraud analytics solution with your competition (or your ideal solution)?
- Are there any barriers, such as performance, skills, support, or concern about new technologies, to adopting deep learning or AI?
- Start narrow and expand. Consider a low cost or free pilot to gain support from departmental heads and your board.
- What equipment do you have? Have you considered that today’s lower software costs combined with the latest hardware could be
- Will you need to go on-premise, public cloud or private cloud?
- Do you have enough storage? If not, how easy will it be to expand your existing facilities?
- Are limitations in image size or image resolution getting in the way of accuracy in diagnostic models, predictive models or molecular analysis?
- Who is working on machine learning / AI / deep learning in your organisation?
- Who is responsible for improving accuracy for fraud analytics?
- How important will your business be to your IT partner?
- Look to the judgement of others. Choose a partner vetted by the vendor.
- Technology projects can be challenging to get off the ground. Find a partner brave enough to disagree when others say yes.
Discover how Maple can lower the cost of your AI proof of concept
Step 2: Decide how to execute your AI projects
When deciding on the most prudent way to deliver the project it’s important to know the underlying assumptions:
- Will your AI project deliver critical differentiation inside the business?
- What’s going to be more important to prove – cost and set-up time, or how it differentiates you from competing companies?
- How much help will be needed from the frontline team, crunching the numbers or implementing the technology?
- Think team. Who are your advocates, and do they have the bandwidth to help?
Choose this option when:
- You have an enthusiastic team with appropriate skills
- You have spare time available
- Speed of results is not critical
- You like a lot of control
Choose this option when:
- You’re ready to learn about the benefit but don’t want to distract from BAU
- You don’t have the internal skills
- You need to get this done to keep pace with competitors
- You need lots of customised features