Accenture just released its Technology Vision 2024 report that suggests AI will unlock the next level of human potential. In particular, it points to the rapid pace of innovation in generative AI, agent computing, spatial computing, and new human interfaces.
Importantly, these new features combine to make computer interfaces more human-like, rather than requiring users to become more machine-like. In the long term, this could be as game-changing as the mouse, cloud, and mobile waves before it. In the short term, companies should focus on modernizing their data systems and increasing the capabilities of their people to take advantage of new opportunities.
Let’s discuss the most important trends in a little more detail.
Matching powered by AI: Resharing knowledge and our relationships
Generative AI technologies will play a key role in reorganizing knowledge in ways that foster human-like reasoning, rather than combing through reams of search engine results. This is essentially transforming the search experience into a holistic experience for business users and consumers. Accenture predicts that by 2029, AI advisors will receive more search traffic than traditional search engines.
However, these large-scale language model advisors require a data foundation that is more accessible and contextual than ever before. Accenture says:
Knowledge graphs are one of the most important technologies here. It is a graph-structured data model that contains entities and relationships between them, and encodes larger context and meaning. Knowledge graphs can not only aggregate information from more sources and support better personalization, but also enhance data access through semantic search.
This report highlights how Cisco’s sales team focused on metadata knowledge graphs using the Neo4J database, cutting sales reps’ data search time in half, saving them over 4 million hours annually. In this case, these benefits were achieved without the help of generative AI. Data mesh and data fabric also need to be explored outside of the knowledge graph when updating the overall architecture. Large-scale language models (LLMs) also play a major role in fleshing out ontologies that find connections between entities and their relationships with each other to populate graph databases.
Introducing my agent: AI ecosystem
First-generation LLMs are already helping build individual AI agents across companies like Bloomberg and Morningstar. The next wave of innovation will be in building a trusted ecosystem of domain-specific agents. According to a report from Accenture, 96% of executives agree that leveraging the AI agent ecosystem will be a major opportunity over the next three years. Early examples include AutoGPT, BabyAGI, MetaGPT, Google’s LATM, and the ChatGPT plugin ecosystem.
However, Accenture warns:
However, there is a catch. There is a lot of work to be done before AI agents can truly act on our behalf or on our behalf. And it takes a lot more work before they can act cooperatively with each other. In reality, agents still get stuck, use tools incorrectly, and produce inaccurate responses. These errors can get worse in a hurry. Without proper checks and balances, agents can wreak havoc on your business.
In the short term, it’s important for agents to build the necessary foothold to gradually gain the organization’s trust. This includes integrating LLMs, human agents, and existing enterprise apps while paying attention to governance and security. Human experts also need to embed and test their rules, knowledge, and reasoning skills to independently determine when and where they can be trusted. Don’t just teach your agents to learn new skills. We need to allow them to build the world we want to live in.
The space we need: Creating value in the new reality
The industry has made significant advances in tools, processes, and standards for extending user interfaces from 2D screens to 3D environments. This includes spatial computing, metaverses, digital twins, augmented reality, and virtual reality. They help blend the digital and physical worlds to create new experiences for business users, consumers, physical product developers, frontline workers, and engineers. Spatial apps help convey large amounts of complex information, give users agency over their experiences, and allow them to extend their physical space.
Accenture claims:
New computing media are so rare that a tipping point awaits. Spatial computing could grow to be as revolutionary as desktop and mobile, ushering in a new era of technological innovation. But to succeed, companies need to rethink their position on this issue, starting today. They need to break out of their slump and appreciate this moment for what it is. Tools are becoming more available every day. What matters now is how you apply them.
Apple’s entry into the market signals the arrival of a new technological medium. Qualcomm’s Snapdragon Spaces and SR SDK will encourage smaller competitors to offer more economical variants. Emerging 3D standards efforts like Universal Scene Description (USD) and glTF will play a key role in connecting workflows and experiences across different apps, services, and workflows.
Electronicizing our bodies: new human interfaces
Innovators are making rapid advances in AI-powered wearables, brain-sensing neurotechnologies, and new human interfaces centered around eye and movement tracking. These can unlock a better understanding of us, our lives, and our intentions to improve the way we work and live. Accenture research found that 96% of executives agree that human interface technology will transform human-machine interactions by providing a deeper understanding of behavior and intent.
However, this introduces a variety of new privacy and security challenges that businesses need to consider. Accenture suggests:
Think of it like mobile device management for humans. We already know how to control what mobile device data stays locally or goes to the cloud, but the stakes are even higher and more complex when it comes to sharing human biological, behavioral, and sensory data. It will be. While rule-based approaches can lay the foundation for data sharing systems, humans will require more flexible and interpretable safeguards to maintain control over their data. You also need to interpret the data in a way that doesn’t obscure what access you’re allowing.
Apple’s new visionOS may promise ingenious advances in new eye-tracking user interfaces. Accenture is also bullish that brain-computer interfaces (BCIs) are on the horizon, thanks to low-cost hardware and significant advances in algorithms. This could extend the use of this technology beyond scientific research and geeky meditation assistants to practical enterprise use cases such as improving training and speeding up processes. One interesting use case was an app that could monitor a person’s brain while showing airport security his scans and flag firearms at the rate of his 3 pieces per second.
my view
A closer look reveals that many of these innovations are still years away from being fully and safely deployed. Each new technology also brings with it a variety of new security, privacy, and regulatory risks that must be considered when building reliable, secure systems that deliver real business value.
But when they finally land, companies that master them will gain a significant advantage in the industry. All four areas are definitely worth some investment and experimentation to see how they fit into your company as the surrounding ecosystem begins to mature.
But don’t dive headfirst into it just yet. To reap the maximum benefits of this coming wave, companies must focus on increasing their talent capabilities and consolidating their data infrastructure. In terms of human resources, all four innovations require significant investment in human resources tasked with building trust and applying it to specific business cases.
On the data side, this is not just about wiring data systems. They also need to use knowledge graphs built across graph databases, ontologies, and taxonomies to weave together the context, significance, and meaning of data across different use cases. Generative AI could play an important role in the near term in automating the process of building these knowledge graphs.
