2024: The Year Ahead in Digital Transformation

Here’s what the experts we reached out to had to say about how AI will further enterprise digital transformation efforts in the year ahead:

AI Touches all Things Digital Transformation

2023 was the year most enterprises acquainted themselves with GenAI and began understanding its capabilities and weaknesses. To date, much of the work performed by GenAI has been limited to single tasks, such as content creation and research. In the year ahead, experts expect those use cases to broaden significantly.

Christopher Rogers, chief operating officer at Carenet Health, anticipates more widespread adoption of large language models, deep learning and neural networks to advance AI beyond singular objectives. “AI tools will not only do singular tasks but will also become increasingly versatile and capable of handling a broader range of applications,” he shares.

For example, Rogers expects AI to improve the patient experience within health care. “[AI] will evolve to offer a deeper understanding of patient data and support complex decision-making,” Rogers says.

GenAI Model Strategies: Go Big or Go Home?

While some LLM models are built from more minor, specific datasets — others are built upon larger, more generalized LLMs. Experts predict the trend will shift to more enterprises using the larger, more generalized LLMs.

John Hayes, CEO and founder of Ghost Autonomy, says that while AI model developers have started with knowledge-rich yet small, finely-tuned LLMs, the trend will be toward larger models.

“In 2024, we’ll increasingly see large models replacing the many smaller, purpose-built models that companies have previously relied on to create and train their AI models. As large models come to dominate across use cases in the coming years, new types of data centers optimized with compute at scale to run large models, such as being kitted out with supercomputers, will replace traditional data centers outfitted with stacks of x86 machines,” says Hayes. 

“While specialized models might still find niches in scenarios demanding specific performance or latency requirements, the trend is clear: eventually, the current explosion of special-purpose models will consolidate into a “supermodel” with general intelligence that will be able to solve a wide array of particular problems in AI directly. The rise of these supermodels will usher in an age where AI solutions are not just intelligent and deliver superior performance but are also economically viable and easier to build,” predicts Hayes.

Renewed Focus on Data Optimization

The success of enterprise AI efforts greatly relies on data quality, and this year, there will be an increased focus on the accuracy and reliability of enterprise data. However, finding solutions to data challenges may prove recursive as AI platforms can help to improve data efforts that will, in turn, improve enterprise AI performance, especially when it comes to data governance data management such as classification, security, and data integration.

“Next, generative AI will allow customers to use unstructured data easily. The fact is that most enterprise data is in this form. But for the most part, it is unused because it requires tedious and complicated processes to transform the data into a usable format.

But, generative AI is ideal for understanding this type of data. This is one of the biggest benefits of this technology. It will mean enterprises will get more insights from its data,” adds Ghost Autonomy’s Hayes. 

“Data will become more usable and more accessible. Companies have collected data like it was the next major natural resource for years. Still, very few companies have the capability to utilize and gain tangible insights from that data at scale. AI will change that almost overnight. The ability to query data using natural language will mean that anyone can “ask a data set” a question about their users and gain instant access to detailed segmentation and knowledge,” adds Danny Nathan, founder and CEO at Apollo 21.

Enterprises Look Inward for Valuable Data

Due to increased privacy laws, regulations surrounding third-party data, and heightened consumer concerns around privacy — including Google’s promise to depreciate third-party cookies throughout 2024 — enterprises will be focused on how to gain more first-party data on their customers and prospective customers.

Notably, experts contend that Google’s decision to limit marketers’ ability to anonymously track users across websites through cookie trackers will significantly reduce the amount of shared consumer information. “Marketers will have less access to information on visitors, impacting targeting capabilities,” says Guy Hellier, VP of product management at OpenText.

This will result in an inward look for data. “We will see much more first-party data collection from brands on owned channels like email subscribers, loyalty programs, and website behavioral data. Companies will tailor web experiences and advertising based on content consumed rather than visitor identity. Authentication will take on more importance as brands entice users to sign in with email addresses or other logins to provide an even better experience,” Hellier says.

Low-Code/No-Code Acceleration

Low-code/no-code platforms are poised to play a significant role in accelerating digital transformation in 2024. Experts say such platforms will continue democratizing app development, reducing development time and costs and enhancing organizational agility. “With the help of low-code and generative AI, developer teams will reach new heights of digital transformation in 2024 and create applications at unprecedented speeds,” says Miguel Baltazar, VP of developers at OutSystems.

“Low-code and generative AI will not only help teams do more with the same resources but will also help close the communication gap between IT teams and business leaders by allowing developer teams to present and demo code in a visual way. Developers will be able to show how projects connect back to the business’s objectives and ultimately gain stakeholder buy-ins,” Baltazar continues.

Baltazar believes development managers will increasingly harness low-code to help their teams address technical debt. “With evolving customer demands, developer turnover, and a growing quantity of obsolete code, complex technical debt is eating away at organizations’ time and budget, and most are not investing at the level they need to rework a solution to this problem. By standardizing architectures and identifying areas where improvement or updates may be warranted, low code helps developers and organizations minimize technical debt and strike a balance between coding speed and quality. New team members can onboard quickly without losing time trying to understand and rewrite legacy code,” says Baltazar.

While uncertainty abounds, it does appear that how enterprises utilize GenAI to further their digital transformation goals will look much different toward the end of 2024 than it does today.