The potential of generative AI to transform business operations for the better is increasingly being recognised.
From customer services to forecasting, to internal operations, if used properly, this technology can bring huge efficiencies to these areas and more, giving valuable insights as well as time back to employees. However, there is a considerable hurdle standing in the way of generative AI adoption: getting the C-suite on board.
New research reveals a concerning forecast for the integration of AI, estimating that only 17% of organisations will incorporate this technology by 2030. This data emphasises a disparity between AI’s potential and the realities of integrating such a technology into a company’s business.
To fully utilise generative AI, companies need tech teams to close the gap by shifting their focus from technicalities to creating a compelling business strategy that aligns with the organisational goals.
The transformative potential of generative AI
The capabilities of this revolutionary technology are vast, spanning from automating intricate tasks to producing content that mimics human writing. It can generate ground-breaking designs, aid in scientific exploration, and revolutionise customer service by creating individualised responses at scale. Additionally, generative AI empowers marketing teams to develop customised content for diverse audiences. In data analysis, it uncovers unprecedented insights that might otherwise go unnoticed.
And this isn’t all the examples of the potential of generative AI.
If leaders are hesitant about investing in generative AI, they run the risk of falling behind their competition. Early adopters have already enjoyed significant advantages such as increased productivity and innovative product development. As this technology continues to evolve, organisations who embrace it will take a greater lead than those who resist generative AI.
Developing data intelligence across the organisation
In order to power generative AI initiatives, the presence of a scalable data architecture is absolutely essential. These highly sophisticated systems demand copious amounts of information in order to operate at peak efficacy and generate results that are genuinely significant. When organisations establish strong foundations for data intelligence, they can guarantee that their generative AI models will be grounded in thorough and current insights which translates into outcomes that are more dependable and influential overall. Enterprises can address these needs with a data intelligence platform which allows an entire organisation to use data and AI. One of the key advantages of a data intelligence platform is its ability to be trained on an enterprise’s specific data and concepts, allowing it to be tailored to the organisation’s exact requirements. This customisation enables the platform to understand industry-specific jargon, resulting in more accurate responses. Users across all levels of the business can navigate and analyse their organisation’s data.
By leveraging generative AI, these platforms allow users to input queries in natural language and receive highly relevant responses. This democratises data access, enabling both technical and non-technical employees to extract insights from enterprise data. These platforms are built on different data architecture, a popular one is a lakehouse architecture as it combines elements of data lakes and data warehouses, which support diverse data types and formats, enabling more comprehensive analytics.
A well-considered data strategy like this can automatically optimise performance and manage infrastructure in ways that are unique to an organisation.
Getting on the same page
Both tech teams and C-suite executives are excited about the potential of generative AI, but their different perspectives can sometimes lead to misunderstandings or clashes. While tech experts focus on the innovative technical aspects, executives are more concerned with how these advancements translate into business outcomes. Bridging this gap requires clear communication, emphasising both the technical benefits and their impact on revenue growth, operational efficiency, customer satisfaction, and competitive advantage.
Building a compelling business case for generative AI involves more than just outlining its technical merits; it requires a deep understanding of the organisation’s strategic goals, market challenges, and financial considerations. For C-Suite buy-in, there must be clarity on quantifiable metrics that demonstrate the potential return on investment, such as projected cost savings or productivity improvements. KPMG predicts that the adoption of generative AI could add £31bn additional output in the UK per year due to increased productivity in employees. It’s also important to address potential risks and implementation challenges, showing a realistic and comprehensive view of the AI initiative.
To enhance leadership engagement, secure required resources and ensure successful integration of AI across the company, tech teams can effectively present generative AI’s strategic significance along with its alignment to the organisational vision.
Uniting vision and strategy
Although the potential for generative AI to transform organisations is evident, many are finding it difficult to fully incorporate due to a gap between technical teams and executive leadership.
By 2030, the organisations that are able to effectively bring together technical teams and executives in bridging the AI gap will have a distinct advantage in leveraging generative AI’s capabilities. Through promoting mutual understanding of its strategic significance and integrating it with wider business goals, companies can unleash fresh opportunities for innovation and expansion. Those who can translate the potential benefits of generative AI into real business value will be at an advantage in shaping the future. Therefore, it is essential for technology teams and executives to come together, communicate with each other, and work harmoniously towards a shared goal of steering their organisations successfully towards an era powered by AI.
About the Author
Richard Shaw is AVP Field Engineering at Databricks. Databricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Condé Nast, Rivian, Shell and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI. Databricks is headquartered in San Francisco, with offices around the globe, and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.
