In January 2019, Dell EMC hosted an event around AI and Digital Innovation for the Ministry of AI. Guest speaker, Cory Minton, Principal Systems Engineer and founder of Big Data Beard travelled to Dubai to educate attendees on the fundamentals of digital innovation and AI, and how this can be applied in the real world.
When looking at the impact of AI, it is first important to understand the 3 core elements of digital innovation. These are:
- Agile Development – Cycler processes to speed up delivery time and improvement
- Edge to Core to Cloud – Connecting and analyzing data in real time for smart actions
- Data Analytics – Using data to drive changes in behavior
AI is a bucket term, which involves designing and building intelligent agents that receive data in real time and act on it to affect the environment. Included in this is Machine Learning (using data to learn how to respond rather than needing programming) and Deep Learning (using artificial neural networks to analyze data).
All of this is well and good, but what does it mean in the real world? To start with, AI has huge potential to help with automation. We’re already seeing real businesses use AI to cut out mundane tasks for staff, increasing accuracy and efficiency. This will also allow organizations to better utilize their resources, resulting in higher moral and greater job satisfaction.
Looking at AI in the future, it has endless possibilities, but for it to be useful to organizations, it has to add value – being novel is simply not enough. Organizations need to use the technology to cut costs or increase revenue (directly or indirectly). For this to happen, you need teams who have 3 core skills: mathematical algorithm understanding, knowledge of IT systems and business acumen. In other words you need a team that understands the technology very well and knows how to apply it to the real world needs of your business.
For example, in a finance organization a common problem faced by many organizations today is fraud. AI could be used to analyze transactional activity and raise the alarm when there is a transaction that doesn’t fit the commonly established pattern. Or in healthcare, where diagnosing patients fast is vital, cell data could be used to check anomalies for cancerous characteristics. Even in security, where finding wanted criminals might be an issue, AI could be used for facial recognition in real time to locate offenders.
All these examples have one thing in common; they are fixing a problem. In order for AI to stay relevant, to progress and to get funding, the crucial aspect for organizations to consider is how AI can add real measurable value.
So, which problems are you going to solve with AI?
Stay tuned for more about AI and Digital Innovation.