Monoglot systems, or 'one-trick ponies', are extinct; they were only capable of doing a few things and performed poorly at handling complex tasks. Most of the systems in use today are polyglots performing multiple tasks:

• Enterprise Search Platforms can search for knowledge within diverse enterprise data sources.
• RPAs and Integration Platforms (e.g UiPath, Workato, Zapier) work with multiple applications and websites.
• LLMs can mimic multiple human skills including language skills, reasoning etc.

While polyglot systems have evolved across one of the dimensions (among search, taking actions or reasoning), they are unable to bring these abilities together. They fall short compared to humans who are able to think, reason, and act across many different dimensions. Humans can easily switch between various types of tasks and adapt to new situations, something that even the most advanced polyglot systems today struggle with. But does that make humans the ideal solution for enterprises?

Where do Polyglot Systems fall short?

Enterprise Search

Enterprise search systems are great at collating information from different sources within a company. However, that’s the limit of their capabilities – akin to a really smart library catalog. Unlike human workers, these systems can't draw inferences from the information they receive and take actions. For example, a search system finding a historical sales proposal, can't understand it and then update it based on the company’s latest capabilities. A human employee would excel at this task.

Also, search systems employ rudimentary mechanisms to understand human queries and struggle to understand what people really want. They might give results that match the exact words used, but miss the real meaning behind the question. Humans however are much better at understanding a question, even if not perfectly worded.

In summary, while enterprise search systems are useful for finding information quickly, they're still far behind human employees when it comes to understanding context and taking appropriate actions.

RPAs and Integration Platforms

RPAs (Robotic Process Automation) and integration platforms are digital robots capable of undertaking tasks across different computer systems (e.g automatically enter data from an email into a customer database). However, they too have some big limitations. They can't search for information like enterprise search systems. If asked to find all customers meeting a certain criteria in their workflow, they would struggle – unless the logic is "hard-coded" in the workflow.

They're also very fragile. If the systems they work with undergo even minor changes, they often stop working. While humans have the creativity and intelligence to adapt to such changes, these platforms fail and are unable to tolerate even a slight deviation from their “hard-coded” tracks.

As these digital robots lack the intelligence to make decisions based on new information, they are hence unable to dynamically cater to customer questions or requests that aren’t explicitly pre-programmed. Also, they only understand computer language, not natural human language resulting in a necessity to provide them with exact, step-by-step instructions in computer code or through a clunky graphical interface.

For example, imagine an RPA set up to process refund requests. It may work fine till the refund form changes slightly and then stop working entirely. Additionally, if a customer asks for a special type of refund the RPA wasn't programmed for, it wouldn't know what to do. These failings severely reduce the usability of these platforms for enterprises.In contrast, a human employee could easily adapt to changes, intelligently cater to unusual requests, and communicate directly with customers in normal language.

Large Language Models

Large Language Models (LLMs) can understand and speak human languages, which is quite unique. However, just as humans have differing aptitudes for different skills, LLMs too vary widely. Some LLMs are really good at understanding natural human language tasks, whilst others are great at taking lots of information and summarizing it. Some others are experts at presenting information in a nice, easy-to-understand way. But here's the catch - performant LLMs can be very expensive to use, and may even be slower than others.

The other challenge with LLMs is the lack of an enterprise’s business context - they don’t know what your company does, who your customers are and how your business processes function. While they have a huge general knowledge base, they lack the context to be useful to an enterprise. They also don't know how to work with your company's applications or data-sources. So whilst they may be good at general reasoning, they can't intelligently read a customer support ticket and update a customer's address or check on an order status.

There are ways to connect LLMs to company data and applications using tools like Langchain. However, this is extremely effort-intensive and requires specialized expertise to build and maintain. It's like trying to build a car in your garage - a worthwhile passion project, but unlikely to ever be even half as good as one made by a professional manufacturer. This do-it-yourself approach is known as 'hand-coding' in the realm of data and application integration. In both cases, enterprises have learned through the years that building by hand isn't worth the effort. Instead, they found it was better to use specialized, easy-to-use integration platforms that are faster, easier, and more efficient.

In summary, while LLMs have powerful general reasoning capabilities, they have single-dimensional expertise, lack enterprise business context and are unable to work with enterprise systems and data-sources. Adding in these missing capabilities to LLMs is an effort-intensive process requiring significant expertise.

Are human employees the ideal solutions for enterprises? Is there a better option?

The human brain is the original intelligent, do-it-all system. As humans, we can understand different contexts and perform diverse tasks in response, which is why we call human thinking 'omniglot' - meaning 'all-language' or 'all-skills'. We can learn new tasks, solve problems, and switch between different types of work easily. However, humans have limitations to their cognitive abilities and availability too. We need sleep and need breaks to recharge our brains. We are able to learn, but acquiring a new skill or unlearning an old skill can take months, sometimes years.

In the dynamic and competitive world of today, enterprises need employees with a rapid ability to learn combined with high availability to deliver impact across business functions. Hence humans too, due to the limitations in our capabilities fall short of being the ideal employees.

In summary, polyglot systems perform well on individual dimensions of accessing enterprise knowledge or taking actions through enterprise tools but lack an ability to reason. Individual LLMs are able to reason and communicate conversationally but lack the piping to connect to knowledge sources or take actions. While humans are able to perform most functions, they learn slower and are not available around-the-clock.

Introducing Ema - an omniglot agentic platform serving as a digital brain that mimics how humans think and act but without their limitations. Ema operates similarly to humans, surpassing all polyglot systems - understanding natural language and business context, drawing inferences from myriad data sources using best-of-breed skills and taking actions working with enterprise applications. However, unlike humans, Ema has a supercharged ability to learn and is available 24x7 thus making her the ideal employee for an enterprise.

How does Ema’s Agentic AI platform work?

Knowledge workers need skills, domain knowledge, and the ability to interact with enterprise systems. Ema's omniglot agentic platform combines these multidimensional capabilities, providing a foundation on which individual agents with human-like capabilities are built. Ema's proprietary Generative Workflow Engine™ then creates an agent mesh that is configured and trained. Once primed for action, the agent mesh resembles a team of highly coordinated experts working together, which we call a AI Employee. Ema’s AI Employees display the same multidimensional capabilities as humans and surpass them in their ability to learn, unlearn, and relearn quickly, as well as operate continuously without breaks. Thus, they provide a powerful tool that combines the best aspects of human cognition with the tireless efficiency of AI.

Want to know more about Ema’s omniglot-like capabilities and the underlying proprietary technology powering our platform? Check out our blog where we dive deep into Ema’s capabilities and technology.

How can Ema help your enterprise?

Ema's versatile Agentic AI platform creates value across business functions and use cases. Our technology allows you to build customized, multi-agent AI Employees that function revolutionize your workforce. Ema supports both multi-cloud and on-premises deployments, ensuring flexibility for your infrastructure needs.

Leading enterprises are already leveraging Ema's platform to supercharge their operations across Customer Support, Employee Experience, Sales & Marketing, Legal & Compliance.

Our focus on security and compliance including SOC 2, ISO 27001, HIPAA, NIST and GDPR makes Ema the most performance and trusted Agentic AI platform to deliver value to your enterprise.

Hire Ema to transform your enterprise and discover what you can achieve.