With today's technological landscape rapidly evolving, what are the key trends to expect in the year ahead? In an enlightening conversation, Pascal Bornet (author of Intelligent Automation and global tech influencer) sits down with Vijoy Pandey (Senior Vice President at Outshift, Cisco's incubation engine) to discuss these predictions. Notably, two topics were at the forefront of the conversation: generative AI (GenAI) and quantum computing.

Here’s a recap of some critical insights from Pandey, focusing especially on how these advancements will shape enterprises and the broader tech industry in the foreseeable future.

Enterprise adoption of GenAI for business-critical use cases

Until now, the journey of GenAI in enterprises has predominantly dealt with easily attainable capabilities: chatbot assistants, enhanced search, translation, and automated content generation. However, Pandey expects 2024 to bring a pivotal shift.

Generative AI is going to expand into business-critical use cases. This year, we will see generative AI solve business-critical problems across the globe.

This sentiment aligns with the first trend described in Cisco’s recent eBook on tech trends for 2024: GenAI will fast expand into B2B with natural language interfaces (NLIs), custom large language models (LLMs), tailored applications and business context.

What are the implications of this trend? Businesses will commit more resources to managing their data so that it can be fully leveraged for training LLMs. In addition, enterprises will need to keep a watchful eye on AI usage and cost to track ROI and implement continuous improvement.

At first glance, it’s encouraging that Cisco’s AI Readiness Index reports that 84% of companies think AI will have a significant impact on their business. However, only 14% of organizations are fully ready to integrate AI into their businesses. Here’s the bottom line: Enterprises know that AI integration will be critical to their business, but they don’t know how to get started.

So then, how can your organization transition from merely experimental to impactful uses of GenAI?

• Start with low-risk use cases. Find GenAI projects in your organization that have a low cost of failure. These can serve as valuable experiments. Aside from the immediate outcomes, you’ll gain an understanding of the gaps in your organization's capabilities—such as skills, data, and processes.
• Data is the cornerstone. Without high-quality data, the true potential for AI in your organization will remain untapped. In addition, tools like retrieval-augmented generation (RAG) will enable businesses to enhance general-purpose models with their internal data.
• Address the data challenges head-on. Cleaning data and ensuring its trustworthiness and security are paramount. From combating issues in training data (such as bias, toxicity, and hallucinations) to safeguarding against compromised models, the foundation of effective GenAI use requires robust strategies for data management and security.

The impact of quantum computing: Security, networks, and scalability

Pandey also predicted significant advancements in the domain of quantum computing. Quantum computing is unlike classical computing, which uses bits (represented as either a 0 or a 1) to perform calculations sequentially. Quantum computing uses quantum bits (qubits), which can exist in multiple states (0, 1, or both at once), enabling the representation and processing of a large amount of data simultaneously. This leads to quantum’s ability to solve complex problems much more efficiently than with traditional computing.

Promising advancements in quantum computing could bring profound implications, radically reshaping how we approach encryption, networking, and scalability.

With quantum computing, certain classes of problems actually become much simpler to solve. Where this is most applicable is security.

Pandey touched on how the advent of quantum computing introduces potential vulnerabilities in traditional encryption methods. While initiatives like Post-Quantum Cryptography (PQC) from the National Institute of Standards and Technology (NIST) are stopgap measures meant to mitigate these vulnerabilities, the ultimate solution might lie within quantum computing itself.

Separately, advancements in quantum computing will lead to the need for quantum-ready networks. Along with making security quantum-safe, our world will need networks capable of transporting quantum information and creating scalable quantum nodes.

This portion of the conversation was a nod to tech trend #4 from Cisco’s eBook: Quantum computing is rising as the future of cryptography and networking will continue taking shape. The major implications of such a trend include increased research and investment into quantum from sectors that intersect with data security and processing.

The challenges of GenAI: Content authenticity and pipeline security

The last big topic in the conversation revolved around GenAI and trust. We now live in a world where GenAI can generate various forms of multimodal content—text, code, images, audio, and video. Distinguishing between what's real and what's artificial becomes increasingly challenging. The rise of deepfakes is a stark reminder of this reality.

Presently, many organizations—including Outshift—are looking for solutions to solve this problem of content authenticity. One promising solution is AI watermarking, a technique that embeds unique data—a watermark—within content, to help identify the origin, authenticity, and ownership of that content. This makes it easier to track back to the creator or the AI model that produced it, helping with the verification of authenticity and the distinguishing of faked content.

Another technique is the use of Generative Adversarial Networks (GANs). These networks have two models: a content generator and a discriminator that tries to distinguish between real and generated content. As these two models continuously challenge each other, the GAN improves over time. Discriminators become trained to detect anomalies or signs of manipulation in content, effectively identifying whether a piece of content is genuine or AI-generated.

In addition to the challenge of ensuring content authenticity, the increased adoption of GenAI will require more investment into securing the entire content creation pipeline—from model training to the final output—to ensure the trustworthiness of AI-generated content. Tech trend #2 from the eBook underscores where the industry is headed: A movement for responsible, ethical AI will begin with governance that respects rights and values.

In the end, we need to make sure that the entire AI data pipeline is made more trustworthy, safe, and secure.

Confronting tomorrow's challenges and possibilities in tech

Pandey’s insights into the intersection of technology, enterprise strategy, and security offer a glimpse into a future where GenAI and quantum computing redefine the boundaries of what's possible. While the conversation touched on three of the eight tech trends outlined in the Tech Trends eBook, there's much more to explore about the evolving tech landscape.

In wrapping up, Pandey introduced Motific, the latest SaaS product from Outshift. Motific helps organizations in how they use AI models to build GenAI solutions. Motific brings together the various personas—from engineering, business, and legal/compliance—for a GenAI endeavor to ensure collaboration and control over data, security, and costs.

If you’re interested in diving deeper into these 2024 tech trends, check out the following wealth of resources:

• Watch the full video for more insights from Vijoy Pandey and Pascal Bornet.
• Dive into the Tech Trends eBook to discover all eight trends shaping the future.
• Find out more about how your enterprise can use Motific to accelerate your GenAI adoption journey.
• Explore Outshift for further information on leveraging these technologies.