By David Irecki
Late last year my colleagues posed a question on where I saw the market heading and what trends I think would reshape how businesses interact with data and AI. The following were top of mind and they made it to my 2024 predictions:
- Data and AI Democratization: Empowering Decision Makers
- Data Insights at the Point of Capture: Enhancing Real-Time Decision Making
- The Rise of the ‘Prompt Engineer’: Bridging Data Management and Analytics
But before delving into these, let’s first take a step back and look at where we are today.
The Current State of Business
The hype around AI continues to be huge but adoption is still building. According to a TechTarget and Enterprise Strategy Group report, GenAI in production is ramping slowly, with only 4% of businesses actively using GenAI in production across the enterprise. Organizations want practical uses for GenAI and are often looking for those use cases that are low risk but high reward to start. For example, programmatic copilots in Software Engineering, efficiency boosting resolution bots in Customer Support, time saving summaries in Sales & Marketing, etc. But most companies are realizing they are not AI ready.
Many are still on their digital transformation journeys, grappling with the digital fragmentation and complexity that comes along with it. Underpinning that complexity is the myriad of SaaS applications a business has, along with hundreds or even thousands of data sources. But as we know, intelligent integration and automation play a huge role in digital transformation and setting an organization on the path to practical AI in the enterprise, by solving the problems of today.
There is good news though. According to various industry analysts, budgets for GenAI use cases are on the rise in 2024, helping prepare organizations to deliver on AI within this year and drive more adoption of GenAI into production.
So going back to the trends I highlighted at the start of the blog, here’s more details on each and how they can impact your business.
1. Data and AI Democratization: Empowering Decision Makers
The first major shift we’re observing is the move towards data democratization. Enterprises are increasingly enabling access to data for non-specialists, eliminating the need for specialized tools or skills. This paradigm shift is not just about accessibility; it’s about empowering decision-makers across various levels of the organization with faster and more efficient access to critical business insights. Coupled with this is the democratization of AI tools and platforms.
However, according to an ADAPT CIO Edge Survey, data culture and data literacy are the most significant factors that impact AI readiness for many organizations at the moment. These gaps need to be effectively closed to truly capture value from AI. Afterall, the old adage “garbage in / garbage out” still applies to AI as well, with many organizations identifying that 70% of the effort to deliver on an AI initiative will be around working on the data.
2. Data Insights at the Point of Capture: Enhancing Real-Time Decision Making
The second trend focuses on delivering data insights at the point of capture. In a world inundated with data, organizations need to invest more in metadata and its management to transform data lakes into actionable insights. The days of data swamps and data scientist teams scrambling to decipher the contents of their data lakes into business values are over. This is where AI steps in, offering real-time insights that businesses can act upon immediately.
This approach is particularly crucial as we continue to embrace Industry 4.0 technologies and start to even look at Industry 5.0, which focuses on the potential of humans and machines working together. By leveraging intelligent integration and automation with AI, we are forging smarter factories and business environments where decisions are data-driven and timely. This trend underscores the need for businesses to be agile and responsive, turning data insights into valuable business outcomes.
3. The Rise of the “Prompt Engineer”: Bridging Data Management and Analytics
The role of the “prompt engineer” is one of the most intriguing developments in the intersection of AI and data management. Initially perceived as specialists, crafting prompts for AI to yield optimal outputs, the role is expanding. Also, the essential skills behind the role are now being found across an organization, whether it’s someone in finance using an LLM to provide a summary of financial data or a software engineer using a copilot to write code.
For more advanced use cases, prompt engineers are also leveraging tools in integration and automation to enhance AI responses. This involves crafting effective prompts, using data from the business to provide a deeper context, and also integrating additional data sources, like real-time analytics, to enrich AI outputs.
Moreover, the scope of their role extends beyond just generating AI responses. Prompt engineers are increasingly using the same tools to automate subsequent actions based on AI insights. This holistic approach ensures that AI is not only providing the best possible answers but is also actively contributing to the decision-making process, thereby streamlining operations and enhancing efficiency.
Adapt and Innovate To Stay Ahead
The landscape of 2024 is marked by these pivotal changes in how we approach and utilize data and AI. The democratization of these technologies, the imperative for instant data insights, and the evolving role of prompt engineering are collectively shaping a future where AI and data are not just tools but integral components of business strategy. As organizations navigate this changing terrain, adapting and innovating will be key to staying ahead in a rapidly evolving, data-driven world.
To learn more about how businesses are using GenAI, read the “The Impact of AI on Enterprise Integration and Automation,” a new survey-based study from 451 Group and S&P Global Market Intelligence.