Unless you’ve been off-grid and offline for the past several years, you’ve witnessed the rapid rise of artificial intelligence (AI) as its powerful capabilities have moved from the realm of theory to everyday reality, in the form of AI Copilots.
With the mainstreaming of large language model (LLM) generative pre-trained transformers (GPTs), web users now have immediate access to detailed information drawn from unified internal and external databases by AI copilots. This includes multiple applications, such as personal assistants, search engines, shopping sites, office platforms and CRMs like Microsoft 365 and Salesforce, and many others.
In this guide, we’ll review the evolution of AI copilots, learn how they work, gain a clearer understanding of the types and developers of copilots currently available in CRM, and discover those best suited to your needs.
What Does Copilot AI Do — and How Does Copilot AI Work?
What is an AI-powered copilot?
So, what is an AI copilot? It’s a specialized AI assistant designed to serve a wide variety of needs and circumstances, giving users the functional equivalent of a personalized digital assistant at their beck and call, and eliminating the need for a vast number of traditionally manual tasks.
“An AI copilot is a conversational interface that uses large language models (LLMs) to support users in various tasks and decision-making processes across multiple domains within an enterprise environment.”
Margo Poda, What is an AI Copilot?, Moveworks, 6 July 2023
To put it short, AI copilots can quickly and accurately accomplish complex tasks by leveraging LLMs across multiple systems and applications, all in response to a user’s conversational query.
As technology has become ubiquitous, the development of AI copilots has further leveraged its potential by combining the power of data across multiple applications to perform increasingly complex personal and business tasks.
AI Copilot Origins and Evolution
“Have you ever chatted with a customer service representative only to realize they weren’t a person, but a bot? That’s a type of copilot. With AI copilots, the interactivity becomes even more conversational, with your own AI assistant working behind the scenes to help improve everything you do.”
Ari Bendersky, What is an AI Copilot?, Salesforce, 26 February 2024
While the capabilities of AI copilots have rapidly expanded as technologies have matured, the concept isn’t new. For years, we’ve encountered automated, AI-enhanced assistants in many contexts, with capabilities ranging from simple to complex.
A few examples include:
AI Chatbots
These real-time online helpers have become familiar on financial, healthcare, or retail sites. They draw from designated internal and/or external databases to produce subject matter-specific responses to user prompts — ideally reducing or eliminating the need for human interaction to save time and cost to the organization and support user autonomy and satisfaction.
AI GPTs
Services like Chat GPT, Google Gemini, and Claude.ai have rapidly become ubiquitous, acting on conversational prompts to produce detailed, tailored responses drawn from vast internal and/or external databases.
These tools are now integrated into technologies ranging from search engines to social media and corporate websites.
AI Assistants
Tools like Salesforce Einstein Copilot, Microsoft 365 Copilot, and Riva Copilot pull from multiple applications/APIs and both internal and external databases to process complex, multivariable tasks that traditionally involved a series of manual actions.
These copilots act as full-fledged digital assistants — and actually improve as their AI learns.
AI Copilot Features
AI assistants were once relied on to perform a limited number of specific tasks, new AI copilots are capable of accomplishing a range of multivariable tasks that might once have required hours — even days — of manual effort.
Some of the features of contemporary AI Copilots include:
Context awareness
AI offers the unusually powerful ability to inform its task processing with context beyond the terms of the query to produce proactive results.
Continued learning
Over time and repeated use, AI copilots learn — and their output becomes progressively more targeted and refined.
Generative capabilities
Is copilot generative AI? In most cases, copilots leverage AI’s ability to synthesize information based on prompts — think content, analytics, and code — relying on LLM data.
Manual task automation
AI copilots can be directed to handle an unlimited range of manual tasks — from data capture to scheduling to coding — freeing workers to focus on more productive objectives.
Data analysis
AI copilots’ LLM capabilities can process vast volumes of data and produce complete, accurate analyses in near real-time.
Streamlined communication
AI copilots’ generative capabilities can improve clear, consistent, timely communications with co-workers, customers, and partners alike, drawing context from a range of designated data sources and applications.
Unified use of disparate data sources and applications
Copilots are capable of drawing data from an unlimited range of designated internal and external data resources — and utilizing or mimicking the capabilities of a similarly unlimited range of third-party applications.
How does Copilot AI Work?
“In a nutshell, an AI copilot acts to simplify complex tasks and provide valuable guidance and support, ultimately elevating the user experience and driving businesses toward their goals effectively and efficiently. As AI copilots continue to evolve with enhanced capabilities and deeper integration into enterprise ecosystems, they hold the potential to redefine the way businesses operate and compete in the coming years.”
Margo Poda, What is an AI Copilot?, Moveworks, 6 July 2023
From the user’s perspective, AI copilots work like search engines: you type in a prompt, and you get results. The difference, of course, is how AI copilots process natural language queries.
To do so, copilots leverage:
- AI search algorithms
- Large Language Model AI
- Cross-system and application integration.
Let’s discuss them in more detail.
AI search algorithms
Different copilots apply different algorithms to the natural language queries entered by users. These algorithms inform the basic parameters of the search and forward the query to the LLM and system resources designated to inform the response.
Large Language Model AI
User prompts leverage LLMs to produce relevant, responsive results. AI copilots are often differentiated by the LLM domain or domains they’re authorized to search.
Cross-system and application integration
Copilots are designed to simultaneously leverage AI LLM and the power of a nearly unlimited range of systems and applications.
AI Copilot Applications and Key Players
“In addition to Salesforce, a number of other companies have introduced copilot products to the market, including Microsoft and GitHub, and even Apple is working on one. There are more niche industry-focused AI copilot companies like real-estate digital marketing company LuxuryPresence, healthcare-focused Nabla, and finance-focused ArkiFi.”
Ari Bendersky, What is an AI Copilot?, Salesforce, 26 February 2024
Given their potential to harness a nearly unlimited universe of data and systems, the applications for AI copilots are limited only by imagination. As the technology matures and copilot capabilities expand, the pace of innovation and productivity improvement in these and other areas is likely to accelerate:
1. Personal productivity
AI copilots like Microsoft Copilot and Riva Copilot are a natural complement to office work, helping workers to streamline and automate email correspondence and cadences, scheduling, data analysis, and more. As user sophistication grows and copilots learn, workers will rely on copilots to perform increasingly complex tasks, using contextual knowledge involving multiple systems.
2. Software development
AI copilots from companies like GitHub and Microsoft allow users to tap the capabilities of multiple software project platforms — all using natural language prompts. Task-specific copilots can also produce and debug code, resulting in significant reductions in development timelines.
3. Creative development
Graphic design and content development are among those fields enhanced by generative AI, and copilots like Microsoft Copilot AI, Chat GPT, Claude, and Riva are transforming the way creatives complete everyday tasks. From content creation to design to multimedia development, these copilots can automate multiple tasks across multiple systems, simplifying the creation of campaigns, web development, and campaign components.
4. Business
AI copilots streamline and simplify business tasks ranging from communication to predictive modeling to product and marketing design and automation. Using copilots — including Salesforce Enterprise Copilot and Microsoft Copilot — workers can easily automate complex tasks, leveraging unified data, diverse systems, and situational context. Daily activities that might once take hours or days can now be achieved with a well-crafted prompt.
What is AI Copilot Embedded in a CRM, and How Does it Work?
Embedded CRM AI copilots like Salesforce Einstein Copilot, Microsoft AI Copilot, and Riva AI Copilot can significantly boost their utility to users — providing the means to accomplish complex, multi-variable tasks that draw on multiple databases, systems, and applications to automate a range of actions.
Salesforce Einstein Copilot
What is Einstein Copilot in Salesforce? Working within the familiar Salesforce interface, Einstein Copilot’s natural language query can tap designated data, systems, and applications to stack actions to automate tasks, analyze data, and produce outcomes that become increasingly accurate over time as the technology learns.
If you are wondering how to enable Einstein Copilot in Salesforce, you need to first check whether Einstein Generative AI is enabled for your organization.
Recommended reading: Guide to Einstein Activity Capture: All You Need to Know
Microsoft Copilot
What is Microsoft Copilot AI? Offering many of the same capabilities as Einstein Copilot, Microsoft’s AI copilot boosts CRM power through its ability to pursue queries across multiple databases and systems, all based on natural language prompts.
What AI does Microsoft Copilot use? Microsoft Copilot uses an advanced model of GPT-4, a large language model developed by OpenAI. Through this technology, Microsoft Copilot can utilize both web and workplace data to answer user prompts. Copilot also utilizes Dall-E to create images from text-based user prompts.
If you are wondering if Microsoft Copilot is generative AI, the answer is yes: it’s a generative AI service that can be used to create original text, images, and more in response to user prompts.
Riva AI Copilot
Capable of integrating seamlessly with nearly any CRM, Riva AI Copilot scrapes signature data from emails and other correspondence to ensure current, reliable CRM data.
Riva’s copilot also offers comprehensive LLM content generation and editing capabilities, making content creation for email bursts and email cadences creation simple, fast, and effective.
Riva AI Copilot is a part of Riva Sales Engagement. Get in touch with us to book a demo.
Capabilities of CRM AI Copilots
While the interfaces, technologies, and strengths may vary, these and other CRM AI Copilots all leverage the same foundational capabilities, which include:
- Integration
- Natural language processing
- Context-aware assistance
- Automation
- Predictive analytics
- Real-time recommendations
- Continuous learning
Let’s address them one by one.
1. Integration
CRM-embedded AI copilots can access and analyze data from internal and external databases, diverse systems, and countless third-party applications. By unifying these resources, users can achieve significant improvements in efficiency and productivity.
2. Natural language processing
AI copilots respond to natural language prompts and are capable of providing sophisticated, accurate, and comprehensive responses. They eliminate the time, effort, and, in many cases, technical knowledge necessary to utilize multiple tools to achieve similar results.
3. Context-aware assistance
AI copilots are designed to consider key factors about their users, including their roles, common tasks, and histories, to provide relevant insights and improve query outcomes.
4. Automation
By automating data capture, scheduling, and report generation, and more CRMs with embedded copilots allow users to focus on other priorities — including customer engagement.
5. Predictive analytics
Through their capacity to process and analyze large volumes of data, AI copilots can identify patterns to predict customer behavior, sales opportunities, and potential issues.
6. Real-time recommendations
CRM copilots can offer suggestions for the next best actions, optimal communication strategies, and personalized content.
7. Continuous learning
Like other LLMs, AI copilot performance improves over time by learning from user interactions and outcomes. These improvements are further enhanced as users grow more adept at formulating more targeted queries to achieve better, more sophisticated results.
Summary: Examples of CRM AI Copilots
| AI Copilot Name | Description |
| Microsoft Copilot | Office-oriented AI copilot to help users with complex cognitive tasks, including the development of sales pitches, communications, and presentation-level images |
| Riva Copilot | AI-enhanced copilot designed to securely unify LLM data sources and apply generative AI to streamline and automate outreach content and sales material development — while enhancing its performance through predictive analysis. |
| Salesforce Einstein Copilot | AI copilot that taps designated data and leverages machine learning to produce predictive analytics and help workers and organizations engage effectively with customers — and build actionable, reliable business strategies |
Benefits of AI Copilots — and Potential Challenges
It’s important to monitor and balance your use of enterprise AI copilots like Microsoft Copilot, Salesforce Einstein Copilot, and Riva AI Copilot.
While the benefits are impossible to deny, reliance on these technologies should be thoughtful, strategic, and measured.
Benefits of AI Copilots
Increased efficiency
AI copilots’ ability to process interconnected data across multiple applications provides enormous efficiency advantages over traditional manual processes.
By giving users the power to analyze huge data sets, generate tailored, targeted content, and automate complex activities involving multiple variables, AI copilots amplify and scale each worker’s efforts and productive capacity.
Read also: How to Increase Sales Productivity: 5 Essential Steps
Improved accuracy
By automating tasks like data capture, hygiene, administration, and integration, AI copilots’ automation capabilities yield dramatic improvements in data accuracy.
This translates to higher data quality, data trust, and technology adoption, all while boosting data security, and compliance — and AI reliability.
Related reading: Your Revenue AI is Only as Good as Your CRM Data
Learning and adaptability
As AI copilots work, they learn, gaining a continually improving understanding of user roles and the subject matter of the resources they use.
As a result, copilot performance improves with use, producing better, clearer, more targeted results.
Potential Challenges of AI Copilots
Over-dependence on technology
AI is in its infancy, and its output still requires careful, methodical human oversight. In many cases, AI falls prey to “hallucinations,” where it fills in gaps in its research with conjecture — often even creating content from whole cloth.
Without careful review and analysis of a copilot’s work, users run the risk of perpetuating incomplete or inaccurate information — ranging from fabricated cites to speculative, often misleading assertions.
Users must be mindful of AI’s limitations and take careful steps to ensure the accuracy of a copilot’s response to any prompt.
Technical limitations
AI copilots are capable of analyzing huge volumes of available data. This means that some information — content behind paywalls, or outside of designated search parameters, for example — won’t be available when a copilot researches a prompt.
Just as workers are limited by their knowledge and their resources, AI can only work with available data and applications. And while it often presents its results as definitive and complete, it’s only capable of working with available resources when processing its responses.
Privacy concerns
AI’s use of available data resources is a double-edged sword. Because copilots will utilize any information within its designated universe, there’s a constant risk that it will capture and convey private and protected information.
To prevent the misuse of sensitive data, copilot resources must be carefully governed by administrators and reviewed by informed users.
Tips to Use a Copilot Efficiently
There’s a lot to know about AI copilots — and a lot of AI copilots to know about. Like any new technology, there’s a steep, ever-evolving learning curve. But with focused effort, you’ll be better prepared to choose and use them effectively.
Here are a few considerations to simplify the process and make your AI copilot experience smooth, seamless, and rewarding:
1. Match your AI copilot to your needs
Not all copilots are created equal. While all are designed to simplify, streamline, and enhance workflows and outcomes, it’s vital to select the right copilot for the task at hand.
CRM-embedded copilots like Salesforce Einstein Copilot are ideally suited to leverage CRM data, utilizing cross-system capabilities to accomplish complex, compound tasks. Enterprise copilots can access data from any number of internal and external sources and applications, bound only by designated parameters.
Integrated third-party copilots like Riva can access and use enterprise and external data to develop, design, and automate complex communications efforts — ranging from marketing outreach and customer service to internal collaboration.
Special purpose copilots like GitHub AI Copilot can perform specific, often highly technical tasks like code development and testing.
The upshot? Depending on what you need to do, the right AI copilot is likely to simplify, accelerate, and enhance the process.
2. Begin with the basics
Given their capacity to perform complex, compound tasks, it’s tempting to expect an AI copilot to bring instant, transformative change to daily workflows.
And while copilots do have the capacity for transformation, it’s wise to learn the ropes with smaller, simpler tasks — and work your way up to increasingly complex requests.
3. Learn the art of the prompt
While your AI copilot can do lots of heavy lifting on your behalf, it’s ultimately dependent on you to ask the right questions. Prompt writing is a blend of art and science, and your ability to do it well will improve over time.
A quick Google search will produce lots of useful advice on effective prompt writing. As the technology gains acceptance, prompt-writing strategies will continue to evolve, but these basics will always serve you well:
- Use clear, straightforward language
- Be specific in your objectives
- Provide relevant background to define your expectations
- Include examples and references
- Specify the format, tone, and style of the desired response
- Utilize follow-up prompts for clarification
- Learn through experimentation
4. Verify output
As AI copilots evolve, the accuracy of their output will improve. But current technologies are limited not only by the information to which they have access — but by their tendency to provide results that are inaccurate, speculative, and, in many cases, fabricated based on context.
Your responsibility to vet the output of your AI copilot is paramount to ensure its credibility — and yours.
AI Copilots: A Giant Leap Toward Intelligent Productivity
“The right AI copilot drives growth, streamlines operations, and elevates employee and customer experiences.”
Solulab, “What is an AI Copilot?”, Medium, 2 May 2024
AI copilots offer the potential to change the way you work, and to dramatically enhance both the quality and volume of your productivity. With new AI copilots entering the market at a rapid pace, including CRM, you’ll have a range of available options to use.
The challenge will be to choose those that work best for the demands of your job — and to learn how best to leverage their capabilities. By taking a deliberate approach to learning those options, and developing the skills necessary to attain useful, targeted results, you’ll have both the abilities and tools you need to optimize your use of AI copilots.
Now that we have discussed what AI copilot is and how it works, do you want to learn how they can transform your workflows? We’re eager to share the capabilities of Riva AI Copilot — and show you how they’ll fit into your role. Contact us to schedule a live demo.
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