In 2023, artificial intelligence (AI) and data science made headlines, largely due to the explosive growth of generative AI technologies. So why then are we calling 2024 the year of data science and AI?
Put simply, 2023 laid the foundations; 2024 will be the year that we believe businesses will start adopting generative AI on a large scale. This will impact the internal skills required across sectors, and we believe drive an uptick in professional development that lets individuals leverage AI and support its use within workplaces, from designing policies to monitoring against shadow IT.
These are the top trends to watch:
Generative AI has seized the spotlight, attracting significant interest from businesses and consumers alike. However, the real question remains: Is it delivering tangible economic benefits to its adopters? Insights from surveys conducted by microsoft, Thoughtworks and Wavestone indicate that despite the high levels of enthusiasm for the technology, it has not yet translated into widespread value realisation. The potential of generative AI to enact substantial change is widely acknowledged; 80% of those surveyed by AWS believe it will revolutionise their organisations, and 64% in the Wavestone survey view it as the most revolutionary technology of their era. Investments in generative AI are increasing among a large majority of these survey participants. Nonetheless, most firms are still in the exploratory phase, with initiatives taking place at the individual or departmental level. Only 6% of organisations in the AWS study reported any generative AI in actual production, with a mere 5% in the Wavestone study reporting scaled production deployments.
The surveys point out that while generative AI is met with great enthusiasm, it is yet to be harnessed for substantial value.
At Torque IT, we believe that a focus on upskilling, continuous development, and a growth mindset that embraces agility will be the key factors behind AI adoption.
The pace at which data science models need to be developed is prompting companies to shift from a bespoke, craftsman-like approach to a more streamlined, industrial process. There’s a growing investment in infrastructure platforms, processes, methodologies, feature stores, and Machine Learning Operations (MLOps) systems all aimed at enhancing productivity and accelerating deployment. MLOps platforms are crucial as they keep tabs on the performance of machine learning models, ensuring they continue to provide accurate predictions, and flagging when retraining with fresh data is necessary.
The creation of data models has evolved from a niche, handcrafted endeavour to a more systematised and large-scale operation. As with any shift that requires advanced tools, the need for skills that understand and can use these technologies are in high demand. Generative AI is only as good as the data it is based on, and so we expect to see a large focus on ensuring that internal data science is a priority along with the skills to drive this priority forward.
According to the Thoughtworks survey, 80% of leaders in data and technology report that their companies are either actively leveraging or contemplating the adoption of data products and their management. A data product captures data, analytics, and AI within a software offering designed for either internal stakeholders or external clients, managed from its inception through to deployment and continuous enhancement by data product managers. Data products span a range of applications, from recommendation engines that suggest subsequent purchases to customers, to pricing optimisation tools for sales personnel.
All emerging technologies particularly technologies that combine other existing capabilities require new skills. Data product managers may in many cases be responsible for their deployment and enhancements, but the ability to use these platforms effectively will also require upskilling and embracing new technology and possibilities.
Data scientists, once lauded as ‘unicorns’ due to their unique capabilities in driving data science projects to success, are experiencing a shift in their industry status. Developments within the field are enabling new methodologies for handling key components of data science work. Among these developments is the emergence of specialised roles designed to tackle specific segments of data science tasks. This growing professional range includes data engineers for data preparation, machine learning engineers to scale and deploy models, liaisons to bridge the gap with business stakeholders, and data product managers to helm the entire project. It’s a whole new world for anyone interested in technology and the power of data and opens multiple doors for specialisations and career development.
Data scientist positions are becoming more specialised and data product managers are taking an increasingly important role, but in an interesting reverse to that, data is no longer split across various leadership roles such as chief data officers (CDOs) and chief data and analytics officers (CDAOs). The tasks previously designated to data and analytics leaders haven’t disappeared; instead, they are progressively being incorporated into a more extensive range of responsibilities under the umbrella of technology, data, and digital transformation. This consolidation is typically under the jurisdiction of a senior ‘supertech leader’ who generally reports directly to the CEO. This shift in executive functions was a key point in the Thoughtworks survey. A vast majority of the respondents 87%, comprised primarily of data leaders and some technology executives agreed that there is a general confusion within their organisations about whom to consult regarding data and technology matters. A significant number of C-level executives admitted to a shortfall in collaboration with their tech-centric peers, and 79% acknowledged that their organisation’s progress had been previously obstructed by a lack of cooperative effort. The solution? In anticipation of the increasing importance of data science and data analytics for the use of AI, upskilling in these areas now is a solid future-proofing, career development and succession planning strategy.
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