Data scientists: redefining business needs with AI

Displaying increasingly robust capabilities, AI looks set to rival data scientists who have been hired on mass in recent years. While AI does not make such scientists “obsolete” so to speak, it does challenge their influence within a company where best-in-class performance models will be established without expert knowledge.

On the face of it, Large Language Models (LLM) seem most likely to outshine data scientists in terms of performance. Though they show signs of promise, these smart machines are hampered by their shortcomings to date – particularly hallucinations which result in coding errors.

When it comes to shifting the skills landscape, one plausible case for generative AI is how it drives innovation using Low-Code/No-Code (LC/NC) platforms. These tools already power adequate performance without programming input. A trend that is accelerating. This “self-AI” – self-service – approach will practically downplay the need for technically astute data scientists in deploying high-performance solutions.

At this stage, AI production will take precedence with a focus on business knowledge – largely unshackled by technical restrictions. It’s an impactful strategy since it’s businesses themselves that know their activities, data, challenges and burdens like the back of their hand.

To fully harness the power of AI and transform their strategic business processes and decision-making, companies have everything to gain by democratizing “self-AI.” They are the ones responsible for creating a secure and stimulating environment for “Citizen AI development,” enabling business users to engineer specific apps and solutions while managing information systems and providing perspective to ensure consistency and foolproof security in IT.

As businesses become the new “Data Makers” without any coding input, the need for out-and-out data scientists as we know them will inevitably change. Their sophisticated modeling skills will remain vital for companies operating at the forefront of data innovation. This includes Big Tech and a rapidly emerging cluster of data-native companies.

What’s more, priority will be given to people who can make AI produce meaningful results for companies. Implementing and integrating solutions within an existing ecosystem, and managing their lifecycle, requires skills beyond the scope of LC/NC platforms. 

It’s up to them to design the Data/AI infrastructure as well as develop and maintain a predictive app that complies with stringent software standards as opposed to a “basic model.” The above engineers will also be tasked with building and sustaining production data flows to run the apps and produce those all-important results.

The real challenge lies in assisting businesses as they evolve, to unlock the boundless potential of “self-AI.” It involves reliable access to priceless data. This is why you need modular, open platforms designed for the architecture made by Data Architects who expedite access to datasets for business that can be used for modeling, with a lifecycle monitored by Data Managers and Data Stewards alike.

Don’t forget, businesses need convincing and educating! Though simplified and seemingly easier to adopt, tools will prove pointless or even counter-productive if they are under- or badly-used due to a lack of trust or understanding. As such, it is critical that businesses be assisted to properly oversee these platforms and analyze statistics accordingly.

Granted, AI has tremendous potential. But there’s no magic formula! If business teams cannot articulate a genuine purpose for AI, with active support from the right Data and ML engineers, the revolution we long for will be nothing more than a short-lived evolution.