Technology Vision 2024

Shoring up your data foundation

New technologies and techniques can help enterprises shore up their data foundation and prepare for the future of data-driven business. Wherever companies start from, LLM-advisors will demand a data foundation that’s more accessible and contextual than ever.

The knowledge graph is one of the most important technologies here. It’s a graph-structured data model including entities and the relationships between them, which encodes greater context and meaning. Not only can a knowledge graph aggregate information from more sources and support better personalisation, but it can also enhance data access through semantic search.

In addition to knowledge graphs, data mesh and data fabric are two ways to help map and organise information that businesses should consider as they update their overall architecture.

Exploring LLMs as your new data interface

On their own, knowledge graphs, data mesh, and data fabric would be a huge step up for enterprise knowledge management systems. But there’s much value to be gained in taking the next step and shifting from the librarian to advisor model. Imagine if instead of using a search bar, employees could ask questions in natural language and get clear answers across every website and app in the enterprise. With an accessible and contextual data foundation, enterprises can start to build this—and there are a few options.

First, companies can train their own LLM from scratch, though this is rare given the significant resources required. A second option is to “fine-tune” an existing LLM. Essentially, this means taking a more general LLM and adapting it to a domain by further training it on a set of domain-specific documents. This option is best for domain-specific cases when real-time information is not necessary, like for creative outputs in design or marketing.

Enterprises are also beginning to fine-tune smaller language models (SLMs) for specialised use cases. These SLMs are not only more efficient, running at lower cost with smaller carbon footprints, and can be trained more quickly and used on smaller, edge devices.

Lastly, one of the most popular approaches to building an LLM-advisor has been to “ground” pre-trained LLMs by providing them with more relevant, use case-specific information, typically through retrieval augmented generation (RAG).

The field of generative AI and LLMs is moving fast, but whatever way you choose to explore, one thing will stay constant: your data foundation needs to be solid and contextual, or your LLM-advisor will never live up to its promise.

Understanding and mitigating risks

First and most importantly, as businesses begin to explore the new possibilities LLM-advisors bring, they need to understand the associated risks.

Take “hallucinations,” an almost intrinsic characteristic of LLMs. Because they are trained to deliver probabilistic answers with a high degree of certainty, there are times when these advisors confidently relay incorrect information. And while hallucinations are perhaps LLMs’ most notorious risk, other issues must be considered. If using a public model, proprietary data must be carefully protected so that it cannot be leaked. And for private models too, data cannot be shared with employees who should not have access. The cost of computing is something that needs to be managed. And underlying everything, few people have the relevant expertise to implement these solutions well.

All that said, these challenges shouldn’t be taken as a deterrent, but rather as a call to implement the technology with appropriate controls.

The data going into the LLM—whether through training or the prompt—should be high quality data: fresh, well-labeled, and unbiased. Training data should be zero-party and proactively shared by customers, or first-party and collected directly by the company. And security standards should be implemented to protect any personal or proprietary data. Finally, data permissions must also be in place to ensure that the user is allowed to access any data retrieved for in-context learning.

Beyond accuracy, the outputs of the generative AI chatbot should also be explainable and align with the brand. Guardrails can be put in place so that the model does not respond with sensitive data or harmful words, and so that it declines questions outside its scope. Moreover, responses can convey uncertainty and provide sources for verification.

Finally, generative AI chatbots should be subject to continuous testing and human oversight. Companies should invest in ethical AI and develop minimum standards to adhere to. And they should gather regular feedback and provide training for employees as well.

Among the many other security implications already discussed in this trend, companies should also think about how LLM-advisors may change user data dynamics.

We have an opportunity to reinvent the ethos of search and restore trust between businesses and their customers.

Companies can now act as stewards of their own information—storing, securing, analysing, and disseminating their data and institutional knowledge directly to customers through digital advisors. This is a big responsibility: your company must ensure that your data remains secure while yielding high-confidence responses in your advisory services. It’s an even bigger opportunity: without search providers mediating the exchange of information, companies can serve as a direct source of reliable insight and win back their customers’ trust.

As AI assistants mature into proxies that can act on behalf of humans, the resulting business opportunities will depend on three core capabilities: access to real time data and services; reasoning through complex chains of thought; and the creation of tools—not for human use, but for the use of the agents themselves.

Starting with access to real time data and services: When ChatGPT first launched, a common mistake people made was thinking the application was actively looking up information on the web. In reality, GPT-3.5 (the LLM upon which ChatGPT was initially launched) was trained on an extremely wide corpus of knowledge and drew on the relationships between that data to provide answers.

But new plugins to enable ChatGPT to access the internet were soon announced that could transform foundation models from powerful engines working in isolation to agents with the ability to navigate the current digital world. While plugins have powerful innovative potential on their own, they’ll also play a critical role in the emergence of agent ecosystems.

The second step in the agent evolution is the ability to reason and think logically—because even the simplest everyday actions for people require a series of complex instructions for machines. AI research is starting to break down barriers to machine reasoning. Chain-of-thought prompting is an approach developed to help LLMs better understand steps in a complex task.

Between chain-of-thought reasoning and plugins, AI has the potential to take on complex tasks by using both tighter logic and the abundance of digital tools available on the web. But what happens if the required solution isn’t yet available?

When humans face this challenge, we acquire or build the tools we need. AI used to rely on humans exclusively to grow its capabilities. But the third dimension of agency we are seeing emerge is the ability for AI to develop tools for itself.

The agent ecosystem may seem overwhelming. After all, beyond the three core capabilities of autonomous agents, we’re also talking about an incredibly complex orchestration challenge, and a massive reinvention of your human workforce to make it all possible. It’s enough to leave leaders wondering where to start. The good news is existing digital transformation efforts will go a long way to giving enterprises a leg up.

What happens when the agent ecosystem gets to work? Whether as our assistants or as our proxies, the result will be explosive productivity, innovation and the revamping of the human workforce. As assistants or copilots, agents could dramatically multiply the output of individual employees. In other scenarios, we will increasingly trust agents to act on our behalf. As our proxies, they could tackle jobs currently performed by humans, but with a giant advantage—a single agent could wield all of your company’s knowledge and information.

Businesses will need to think about the human and technological approaches they need to support these agents. From a technology side, a major consideration will be how these entities identify themselves. And the impacts on human workers—their new responsibilities, roles, and functions—demand even deeper attention. To be clear, humans aren’t going anywhere. Humans will make and enforce the rules for agents.

Rethinking human talent

In the era of agent ecosystems, your most valuable employees will be those best equipped to set the guidelines for agents. A company’s level of trust in their autonomous agents will determine the value those agents can create, and your human talent is responsible for building that trust.

But agents also need to understand their limits. When does an agent have enough information to act alone, and when should it seek support before taking action? Humans will decide how much independence to afford their autonomous systems.

What companies can do now

What can you do now to set your human and agent workforce up for success? Give agents a chance to learn about your company, and give your company a chance to learn about agents.

Companies can start by weaving the connective fabric between agents’ predecessors, LLMs, and their support systems. By fine-tuning LLMs on your company’s information, you are giving foundation models a head-start at developing expertise.

It's also time to introduce humans to their future digital co-workers. Companies can lay the foundation for trust with future agents by teaching their workforce to reason with existing intelligent technologies. Challenge your employees to discover and transcend the limits of existing autonomous systems.

Finally, let there be no ambiguity about your company’s North Star. Every action your agents take will need to be traced back to your core values and a mission, so it is never too early to operationalise your values from the top to the bottom of your organisation.

From a security standpoint, agent ecosystems will need to provide transparency into their processes and decisions. Consider the growing recognition of the need for a software bill of materials – a clear list of all the code components and dependencies that make up a software application – so as to let companies and agencies under the hood. Similarly, an agent bill of materials could help explain and track agent decision-making.

What logic did the agent follow to make a decision? Which agent made the call? What code was written? What data was used and with whom was that data shared? The better we can trace and understand agent decision-making processes, the more we can trust agents to act on our behalf.

Critically, new standards, tools and technologies are making it easier—and cheaper—to build spatial apps and experiences that feel familiar.

Think about the websites you frequent or your favorite apps on your phone. Even if their purposes are different, something feels undeniably universal across even the most disparate experiences. Why? They all used the same foundation.

For a long time, spatial never had such a foundation. Enter Universal Scene Description (USD), or what can best be described as a file format for 3D spaces. Developed by Pixar, USD is a framework that lets creators map out aspects of a scene, including specific assets and backgrounds, lighting, characters and more. Since USD is designed around bringing these assets together in a scene, different software can be used across each one, enabling collaborative content building and non-destructive editing. USD is quickly becoming central to the most impactful spatial applications, notably within industrial digital twins.

Enterprises need to understand they will not be operating spaces in isolation. Just as no webpage or app exists on the internet alone, the next iteration of the web promises to bring these parallel experiences even closer together.

Sense of place

One emerging capability that differentiates spatial computing from its digital counterparts is engaging our senses. New technologies are letting engineers design experiences that address all types of senses, like touch, smell and sound.

In past VR attempts, adding haptics, or touch, could be bulky or underwhelming. But University of Chicago researchers recently proposed using electrodes to better mimic touch. 

Scents can make digital spaces lifelike, too, by evoking memories or triggering the all-important fight-or-flight response. Scentient, a company trying to bring olfactory senses to the metaverse, have been experimenting with the technology for training firefighters and emergency responders, where smells, like the presence of natural gas, can be critical for evaluating an emergency.

Of course, sound, or spatial audio, is also critical to realistic digital scene-building. Lastly, immersive spatial apps will need to respond to how we naturally move.

Spatial computing is not coming to replace desktop or mobile computing, but it is becoming an important piece of the computing fabric that makes up enterprise IT strategy.

We’ve already seen the early stages. Digital twins make more sense when you walk through them. Training is more impactful when you can live the experience rather than watch a video. While these were often standalone pilots, a careful consideration of the unique advantages of spatial computing can help shape and guide enterprise strategy. The market is still maturing, but it is quickly becoming clear that spatial apps thrive when applied in three ways: conveying large volumes of complex information; giving users agency over their experience; and, perhaps counterintuitively, allowing us to augment physical spaces.

When it comes to conveying complex information, the advantage of the spatial medium over the alternatives is probably clearest. Since a space can let users move and act naturally, information can be conveyed in more dynamic, immersive ways. We’ve already seen it in action. Some of the earliest examples of successful spatial apps were industrial digital twins, virtual training scenarios, or real-time remote assistance.

The second advantage spatial has over older mediums is the ability to give users agency to shape their in-app experiences. Because spatial computing lets us build digital experiences that embody a physical sense of space, we can design experiences that give users more flexibility to move and explore.

Lastly, spatial applications bring advantages to physical spaces; they can augment, enhance, and extend physical places without materially changing them. Imagine a future office where physical monitors, projectors, and displays are replaced by spatial computers and apps. People will have the flexibility to design simpler spaces, lowering overhead costs, and to change their surroundings more easily.

As the working world goes spatial, businesses will also need to think about security. There will be more devices than ever – employees will use spatial devices for work, customers will use them to access experiences. And with this ever expanding device ecosystem there will be more entry points for attackers too. So how do you put borders on the borderless? Businesses’ spatial strategies will need to be designed with zero trust principles.

Additionally, businesses should recognise that spatial is unfamiliar territory, so both vendors and users should expect to have blind spots. One line of defense won’t be enough, but Defense in Depth strategies that leverage multiple layers of security (like administrative, technical, and physical) can be deployed to defend this new frontier.

Attempting to understand people—as individuals, target groups or populations—is a centuries’ old business challenge. And in recent decades, using digital technology to do this has been the ultimate differentiator. Digital platforms and devices have let businesses track and quantify people’s behaviors with enormously valuable impact. Now, the “human interface” is changing the game again, making it possible to understand people in deeper, more human-centric ways.

Recent technologies used to understand people have been based on tracking and observing patterns that still lack specificity. People may read or watch familiar content, but they may actually want something new. We’re very good at recognising what people do, but we don’t always understand why they do it.

How the “human interface” measures intent

The “human interface” isn’t any one single technology. Rather, it encompasses a suite of technologies that are deepening how innovators see and make sense of people.

Some are using wearable devices to track biosignals that can help predict what people want or understand their cognitive state. Others are building more detailed ways to understand people’s intent in relation to their environments.

Another approach to human intent is through AI. Consider human-robot collaborations. People’s state of mind, like if they’re feeling ambitious or tired, can impact how they approach a task. But while humans tend to be good at understanding these states of mind, robots haven’t been. But efforts are underway to teach robots to identify these states.

Lastly, perhaps one of the most exciting “human interface” technologies is neurotech: neuro-sensing and BCI. Many new neurotech companies have appeared in the last decade, and the field holds clear potential to read and identify human intent.

Neurotech highlights the pace of “human interface” advances

Many may think neural-sensing and BCI are years away from widespread commercial use, but recent advances tell a different story.

Skeptics tend to assert that neurotech will stay limited to the healthcare industry. But new use cases are being identified by the day. Two key advances are driving this. The first is decoding brain signals. Advances in AI pattern detection, as well as greater availability of brain data, are making a big difference.

The second area to watch is neuro-hardware – specifically, the quality of external devices. Historically, EEG (electroencephalogram) and fMRI (functional magnetic resonance imaging) have been two of the most widely used external brain sensing techniques. However, until recently, capturing either type of brain signal required a lab setting. But that is starting to change.

As more enterprises start to build “human interface” strategies, they should begin by scoping out the different business areas and challenges that can be transformed.

First, consider how “human interface” technologies are raising the bar when it comes to anticipating people’s actions. Some of the most promising use cases are in areas where people and machines operate in shared spaces. For instance, enterprises could create safer and more productive manufacturing systems if robots could anticipate what people were about to do.

Another area that can be transformed is direct human-machine collaboration: how we use and control technology. As an example, think about how neurotech is letting us tap into our minds and connect with technology in new, potentially more natural ways.

Lastly, the “human interface” could drive the invention of new products and services. Brain-sensing, for instance, could help people “get” themselves better. L'Oréal is working with EMOTIV to help people better understand their fragrance preferences.

Still others are thinking about the “human interface” as a safety measure. Meili Technologies is a startup working to improve vehicle safety. It uses deep learning, visual inputs, and in-cabin sensors to detect if a driver has been incapacitated by a heart attack, seizure, stroke, or other emergency.

Business competition is changing—and trust is more important than ever

Businesses need to start assessing the risks posed by these technologies, and what new policies and safeguards need to be put in place. Rather than wait for regulations to ramp up, responsible enterprises need to begin now, looking to existing biometric laws and to the medical industry for guidance.

If tin foil hats don’t prevent mind reading, what will? More than any other trend this year, security will make or break enterprise and consumer adoption of the “human interface."

Acceptance of more perceptive and connected tools hinges on humans’ ability to be the primary gatekeepers of what information gets shared, at a minimum. This practice needs to be integrated into the design of the next generation of human-computer interface tools, letting people either opt into sharing data or telemetry relevant to the task at hand, or opt out of sharing extraneous or sensitive information.