By Ruban Phukan, VP Product Cognitive First at Progress
Everyone wants to upgrade their business with artificial intelligence (AI) these days. Higher efficiencies, improved services, kudos in the eyes of customers, there is little that is not to like. The problem is knowing where to start. Although there are products in the market that allow you to implement AI and cognitive learning into your existing processes, there’s still a lot to be done before seeing the benefits.
So who do you turn to in your business to help guide you through the process?
Intelligence is a team effort
There is no one individual who should be responsible for your strategy and implementation. Often, many people assume that the
responsibility lies with the data scientists in your organisation, but they are just one part of the team you need to assemble. This team consists of four key roles, easily referred to as the ‘4 Ds’: data scientists to leverage data and create models, designers to work on UI and UX, developers to develop the right software or application that applies the models to business processes and DevOps to manage and update the infrastructure, integrations, deployments and model management.
Successfully deploying a cognitive application requires a phased approach of these different disciplines, meaning that everything from the conception to the final integration into your business processes is seamless.
How to set up the right infrastructure
Before you embark on making your business more productive, you need to have the right foundations in place. Everything starts and ends with data and you need the right tools to collect it. The DevOps team managing your infrastructure will be responsible for collecting and storing the data, before handing across to your data scientists. This all takes place in a research environment, allowing for models to be created and then used for predictions and, eventually, decisions.
This is already quite a complicated process, but once you start dealing with larger volumes of data, the task becomes even more difficult. Imagine only the amount of data generated through Industrial Internet of Things (IIoT). In order to be able to draw useful conclusions at this scale, you need to be able to create AI-enabled analytical models. These models can’t be created without the work of data scientists.
Once you have your models, then it’s time to bring in your other teams to help.
Taking it from research to the real world
It’s one thing for your models to be working well in a research environment. To become a key working part of your business processes, though, they need to be tested in the real world. This is where it becomes not just a data science problem, but a software engineering one. You need to plan how the data is going to be analysed, why it’s being used in that way, how it’s being updated throughout the process and ultimately how it’s going to be used by end users. For this to happen, you need to involve developer teams. They will be the ones to build the software and applications that will take the data models to the production process and the end user.
One area that is crucial to get right when it comes to the end user is the user interface (UI). Time and time again, fantastic technology has fallen down at the last hurdle because it’s not intuitive, appealing and easy to use. This is where your designers come in, not only to improve the UI, but to make it easier for everyone within your organization from the start.
Finally, the DevOps team is responsible for managing and updating the production systems. It ensures that the whole process throughout its lifecycle, from design to development and production, runs as smoothly as possible.
People are always behind the success of AI
The benefits of technology such as AI can improve countless areas of a business, but this is impossible without the right teams and collaborative processes in place. Not engaging the relevant departments in your organisation from the start is the reason why many AI projects fail. To be successful, you need to remember that the technology will not build itself.
