Generative AI in 2024: Reality Check, Security Concerns, and the Road Ahead
It's 2024, and generative AI is no longer science fiction. These powerful models are rapidly reshaping our world (and only occasionally stealing our jobs). With so much buzz, it's easy to get swept away by hype.
As ClearScale CTO Pavel Vasilyev points out, “The challenge is where and how we apply GenAI. Identifying the optimal processes for GenAI integration is crucial for maximizing benefits and mitigating risks.”
“Interest in AI and GenAI among enterprises has been "off the charts," claims Lemongrass CTO Eamonn O’Neill. “However, while interest is high, actual adoption remains very low, indicating that these technologies are still in their early stages within the business sector. I predict that as enterprises become more familiar with AI capabilities, the adoption rate will accelerate, moving beyond basic applications to more transformative AI-driven processes that could significantly impact industry practices and operational efficiencies.”
In this article, I'll sift through the hype and explore the concrete realities of generative AI in 2024. What groundbreaking advancements have taken root? Can we debunk some of the overblown promises, and peek into the exciting possibilities that lie on the horizon? Will artificial intelligence platforms take out jobs? Let's find out!
GenAI – Initial Hype vs. Reality
According to Niranjan Ramsunder, CTO of UST, GenAI was initially expected to be a game-changer, offering powerful AI for everyone. It promised dramatic improvements across three areas:
- Peopleless experiences: Smarter, faster interactions with AI-powered customer and employee services.
- Creative work: Generating music, images, and videos based on simple prompts.
- Developer productivity: Accelerating code writing, testing, and modernization.
However, the reality is slower than anticipated. "Companies are focusing on measuring impact, managing costs, and ensuring safety. Scaling up GenAI initiatives while controlling costs and delivering value is proving challenging," says Niranjan.
The Looming Power Shift
Looking ahead, Niranjan expects a power shift. "Companies that effectively use GenAI will gain a significant advantage. However, access to resources like GPUs and top talent is becoming monopolized by a few, well-funded organizations."
This will likely lead to:
- Alliances: Non-tech leaders partnering with tech giants.
- Pay-to-play: Smaller companies forced to rely on expensive IP from these oligopolies.
- Widening wealth gap: The uneven distribution of wealth could worsen.
GenAI’s initial hype has settled into a more measured approach, with a potential future dominated by a few powerful players.
Generative AI Challenges
One of the biggest challenges for CTOs in implementing GenAI lies in identifying the most suitable applications.
As Pavel emphasizes, “Understanding "where and how" to integrate GenAI effectively is the key. ClearScale, a consultancy specializing in cloud services, tackles this challenge by building business cases, assessing risks, and conducting proof-of-concepts before scaling up deployments. This cautious approach ensures that companies leverage GenAI strategically, maximizing benefits while mitigating potential downsides.”
Security remains a top concern for businesses venturing into GenAI. Pavel highlights the importance of securing training data, ensuring it's free of errors and biases.
“Similarly, as with any app involving databases or data management, GenAI data storage demands rigorous security. It’s critically important to store data at all stages—from sourcing and preparation to training and operational scaling—to safeguard against risks like AI supply chain attacks, data poisoning, and prompt injections.”
Building a Data Strategy for GenAI
Data management, the foundation for successful AI initiatives, is experiencing a boom in the cloud.
“‘Data readiness’ and ‘data preparation’ are keywords here,” says Pavel V. “Companies can benefit from their data only if it is prepped. The fact is, you can’t have a data science strategy if you don’t first have a data strategy. Many IT leaders put the cart before the horse – they’re investing in GenAI before having a clear understanding of how to unify, store, analyze, and apply data at scale.”
Pavel emphasizes that data readiness isn't a one-size-fits-all approach. It depends on your specific business goals. However, a solid data strategy is the foundation. This strategy should outline the technology, processes, people, and guidelines needed to manage your organization's data assets effectively. In today's data-rich world, a well-defined data strategy is essential for getting the most out of your information.
Eamonn notes that “Some enterprises have begun to explore AI applications, particularly in managing unstructured data, yielding quick and effective results in areas such as document management. This use case serves as an entry point for companies dipping their toes into AI technology, providing them with tangible benefits and setting the stage for more complex applications.”
Employee Empowerment
Beyond the technical considerations of Generative AI, fostering employee buy-in is equally important. A key challenge for CTOs is addressing employee concerns about job displacement due to automation.
“Explain to your employees that they will not be replaced with GenAI,” encourages Pavel. “This is not a risk for them, but those who are not using it will be replaced by those who are using it.”
Pavel suggests using this conversation as “a "soft" trigger” to frame GenAI as a tool for enhancing professional development and reducing mundane tasks. By encouraging employees to leverage GenAI and upskilling them in its utilization, companies can foster a positive and productive work environment where employees view GenAI as an ally rather than a threat.
Cloud, Hyperscalers, and the Future of GenAI
The hyperscaler landscape also plays a role in GenAI development. GenAI is a major focus for leading cloud providers like AWS.
“In addition to that, I would mention Broadcom’s acquisition of VMware," shares Pavel V. Broadcom's plan to terminate all VMware partner programs and shift partners to Broadcom's invite-only partner program is a problem for many companies. There’s a lack of clarity among partners around getting into the Broadcom program, leaving VMware users in the dark."
However, this disruption could be an opportunity to reevaluate IT infrastructure and explore the benefits of cloud-based solutions like AWS, which offer cost efficiency, scalability, and enhanced security.
Eamonn, emphasizes the growing importance of cloud for GenAI: "As AI becomes more embedded in enterprise operations, CTOs are exploring more dynamic and robust AI applications in the Cloud beyond basic automation, aiming to significantly transform business processes and customer interactions. For example, instead of buying expensive microchips for AI use, why not leverage Cloud – it's much cheaper, scalable, and just as effective as on-prem hardware, if not more so.”
Final Thoughts
As we navigate through 2024, we have witnessed significant progress in GenAI adoption. While businesses are actively implementing GenAI solutions, a cautious and security-conscious approach prevails.
Eamonn, looking towards the future, raises an interesting point: "As AI becomes more embedded in enterprise operations... there are the potential impacts of quantum computing on data security. There is a growing consensus that future security measures must address quantum computing threats, which underscores the importance of Cloud-based solutions for their advanced security capabilities.”
Data management remains a hurdle on the road to wider adoption, and fostering employee buy-in is crucial for successful implementation. However, the potential of GenAI to transform industries is undeniable. With continued advancements and a focus on responsible development, the future of GenAI holds immense promise to transform industries and redefine how we work and interact with technology.
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