The data a business possesses is a critical asset, offering deep insights into customer needs, operations and products. Leveraging this data effectively can drive organisational transformation, foster sustainable growth, and secure a competitive edge. However, efficiently collecting and analysing this information can be challenging. If not managed correctly, it may even yield counterproductive results.
This is why data scientists are always looking for innovative new AI models that would allow them to effectively manipulate data and uncover unique insights. But their efforts are often wasted, as many models are either never deployed widely within a business or don’t fully align with the overall strategy.
There are various reasons this disconnection exists but an important element that can hold back a business could be due to the data scientist team that is building and operating the AI tool not efficiently communicating with others within the business – specifically, business and IT teams. This comes to no surprise as data scientists usually operate in silos, concentrating on specific issues and datasets, instead of collaborating across teams to find a holistic solution. Developing a model though without consulting others, receiving their perspective or aligning on a strategy could result in the end-product being misunderstood and misused.
It is therefore critical for a business to ensure it encourages an open and connected ecosystem for AI and enhances transparency across the teams that would help break down silos and foster trusted data and explainable models.
So, what does this look like and what do data scientists need to do for their AI models to be widely deployed and successful?
Communication is key
Before designing an AI model, it’s important to ensure the team comprehends the overall business objectives. What are you trying to achieve and how will this model support the goal? Are there any operational constraints to consider before jumping into the AI lab? Bringing the business and IT teams on board early on to gain these insights will help you understand the problem at hand.
In addition to familiarising yourself with the business objectives though, it’s also essential to understand how employees will be leveraging the AI models to achieve their everyday goals. If possible, it will be beneficial to experience this yourself to truly comprehend how they’re putting this to action. Is AI being used to its full potential and are there any areas that can be improved? Once these questions are fully answered, you’ll be better equipped to build a model suitable for the needs of the business.
It’s also essential to recognise the real-world value and cost of your work. Leverage such insights to guide the best way to adopt the AI model and how to maintain and support it while in production. Being able to align this understanding with the overall outcome will encourage others across the business to incorporate your ideas and recommendations.
However, integrating the above within the model is not enough. Data scientists should not simply hand over their innovation for the business to use and IT team to support. They should make sure there’s an ongoing open and connected ecosystem for AI and oversee how that model is performing. For example, employees might need to be retrained to be able to properly use the new tool. Not only that but it’s vital for data scientists to acknowledge when the AI model needs to retire to either give space to a more suitable tool or to be tweaked to adjust to the evolving business demands.
Working collaboratively
Data scientists play a significant role in the development of an AI model, but the success of the product does not just fall under their remit. It is a group responsibility that the IT and business teams need to proactively be part of. Both teams need to make an effort to better understand data analytics and what data scientists do. While business managers are not expected to make decisions on the product design and its capabilities, they should know enough to involve data scientists at the right points for their guidance.
Working together also requires transparency so that all teams can fully understand how and why an AI-driven result is concluded. To achieve this, a business should offer complete visibility into the data used to make decisions, including how a model leverages information and complies with the regulations.
Being transparent across the business also means that the teams need to be able to explain what makes the AI model fair and equitable. The data sources used must be validated and proved trustworthy before any AI adoption takes place, and the reasoning behind the model’s output must be explainable as well as accountable.
This level of governance is essential in ensuring the AI model used is not only ethical and trusted but it’s also respected throughout an open and connected ecosystem. Any technology tools being integrated to partner companies with whom the business interacts will also be held equally accountable at making sure they follow the same process.
Developing an AI solution to fit a business’ needs can be a challenging process with the success of its adoption depended on teams being able to work well together. Close collaboration, open communication, transparency, and playing to each other’s strengths is therefore vital to making this a reality.
Data scientists, business teams and IT must not just take responsibility for their role but also ensure they recognise and appreciate the expertise of others. Only by breaking down silos, developing a data-centric and team-oriented culture, a business can gain value from an AI model and the data it analyses.
