What is a co-pilot: AI assistant explained

If you use the Internet regularly, chances are, you’ve heard of generative AI (sometimes called gen AI). Perhaps you experimented with one of the generative AI platforms when they were still a novelty straight out of sci-fi: had ChatGPT write a limerick about cheese or got DALL-E to paint a cat, just like yours, in the style of van Gogh. By now, you may have used them to draft e-mails, summarise information, or help with complex maths. So, how do you use generative AI for work and everyday tasks?

One practical application of gen AI is a new type of tools: AI co-pilots. The name seems fitting because what is a co-pilot if not a trusted partner to the captain? Like its human namesake, an AI co-pilot can help the person in charge—you—to navigate complex and important tasks.

So, in the world of AI, what exactly is a copilot? Simply put, an AI copilot is a virtual assistant that can use data and computation to help you get things done more efficiently: from generating content in seconds to gaining data insights with a single prompt. As an AI assistant itself might put it, a copilot helps you “enhance productivity and efficiency”—in other words, do your best work while spending less time and effort.

Co-pilots come in a variety of forms: some are standalone applications, like the famous ChatGPT by OpenAI, while others can be built into larger infrastructure. In the latter case, copilots can be integrated into other apps, such as workplace productivity tools, retail websites, or entire software ecosystems. Across all applications, the purpose of an AI co-pilot is to assist the user—you.

Generative AI tools you might have come across:

• ChatGPT: a standalone gen AI assistant that can generate, summarise, and translate text, answer questions, and maintain a conversation like a chatbot
• DALL-E, Midjourney, and Stable Diffusion: text-to-image AI models that can generate images from description prompts in natural language
• Jasper: a full-service AI co-pilot that can generate marketing content, including text and images, and provide insights
• Microsoft Copilot: an AI-powered productivity tool that provides real-time intelligent assistance, enabling users to enhance creativity, productivity, and skills
• Joule: an integrated generative AI copilot built into workplace productivity software to assist with content generation, analytics, code completion, and optimisation of business processes

Microsoft Copilot and Joule will be connected via a deep bi-directional integration, allowing employees to get more done in the flow of their work through seamless access to information from business applications in SAP as well as Microsoft 365.

How does an AI co-pilot work?

So, what is a copilot from a technical standpoint, how does it work, and what does it have to do with generative AI? To understand how AI co-pilots work, let’s get our nomenclature in order.

In the context of artificial intelligence, an AI co-pilot is a smart virtual assistant that can help a human user work faster and more efficiently, thanks to three underlying technologies:

• Natural language processing (NLP)
• Large language models (LLMs)
• Generative AI

These technologies are the crucial step in the evolution of virtual assistants: from a simple algorithm-based chatbot with scripted behaviours—to a true partner in productivity, which the copilots can be today. A specialised AI copilot can even be used with first-party data and integrated into productivity tools and workflows, but we’ll explain more about this later.

What types of AI co-pilots are there?

There are multiple types of AI co-pilots—and multiple ways to classify them. Here are three classifications we think you’ll find helpful:

By means of accessing: a co-pilot can

• Be accessible directly as a chat interface in your browser, like ChatGPT
• Come as downloadable standalone software (for example, like Parthean)
• Have integration options with your other tools, the way Jasper has extensions for your browser and integrations with third-party apps, such as Google Docs
• Be entirely embedded into an existing productivity ecosystem or business infrastructure, the way Joule is integrated with SAP solutions

By knowledge base: a co-pilot can

• Rely solely on external information (i.e., the Internet and datasets it was trained on) and what it learns from interacting with you
• Be able to integrate with your or your company’s structured and unstructured data, such as customer databases or HR policy documents

By application versatility: a co-pilot can

• Be an all-purpose general assistant, like Google’s Gemini
• Be an industry- or use-specific assistant or adviser, like GitHub Copilot, tailored specifically to developers and coders, or like the financial assistant Parthean AI
• Be a versatile work co-pilot integrated into an existing environment but serving multiple purposes. For example, Joule is integrated with multiple SAP solutions, catering to a variety of business use cases.

What’s the difference between an AI co-pilot and a chatbot?

One of the defining features of a typical AI co-pilot is that it can take your input in the form of conversational prompts. This means you can “talk” to it in natural language rather than code. So, are AI copilots simply a kind of chat bot? Not quite.

Chatbots, in some form, have been around for a while. Some of the well-known early examples date rather far back and include ELIZA (1967) and Jabberwacky (1997). But the more sophisticated and dynamic interactions you’ve come to expect from chatbots today are owed to advances in machine learning and, specifically, generative AI. So, not every chatbot is powered by generative AI, but some gen AI platforms can be considered chatbots (for example, ChatGPT). Similarly, some platforms, like DALL-E and Midjourney, are generative AI but not chatbots. Certain generative AI tools can, in fact, be considered both a chatbot and an AI co-pilot: for example, Jasper and Google’s Gemini. However, there’s one crucial distinction between a generic AI assistant and a fully-fledged copilot: the intended use and how suitable they are for work.

A standalone assistant like ChatGPT can provide answers and perform certain tasks, but it relies on external information—such as data from the Internet (sometimes from specific time ranges). It doesn’t “know” you or your business, has no access to your productivity tools and data, and can lack context. In contrast, a dedicated AI co-pilot can be:

• Integrated with your data sources to provide context-relevant output, as opposed to using only the generic external data
• Embedded within other tools, including productivity software and business systems—for example, in the same way as SAP’s AI copilot, Joule, is integrated with SAP solutions
• Used for a broader range of action and output types, depending on the tasks
• Given access to your structured data, such as, for example, customer information, HR policy documents, various records, or supply chain data

Why should you use an AI co-pilot for work?

Both the adoption and development of generative AI are on the rise, and it’s quite likely that AI co-pilots are here to stay. So, what advantages and challenges can you expect when using an AI co-pilot for work?

For employees, AI assistants help to perform better at work: with an AI copilot, you can be at your best—but with less time and effort. And for employers, they hold the promise of increased productivity: if every employee performs better, the entire company can, too.

While both expectations are reasonable, generative AI does have limitations and nuances that are worth bearing in mind. In work settings, AI copilots need to be used responsibly, sensibly, and in a way that’s clear and acceptable to both the employer and employees.

What are the benefits of using a co-pilot for work and business?

What if your data could speak? An AI co-pilot can speak natural language, act, and generate insights or content. With nearly any task you put your mind to—your co-pilot is always ready to assist. Just like a fast, trustworthy, competent colleague who knows your business inside and out, the right AI co-pilot can help you save time and resources, get more reliable insights, and achieve better outcomes.

That is especially true for an AI co-pilot that truly understands your business. When it works with your tools, your business data, and your specific tasks, a co-pilot can be more effective:

Relevant

With AI built for business and embedded into your workflows, your copilot has the context and infrastructure to drive immediate business impact right where you want it.

Reliable

When the AI copilot is grounded in your business data, you can get quick answers and smart insights on demand, without bottlenecks and with less risk of human error. This helps you make decisions with more confidence.

Responsible

Proprietary data and tools require privacy, security, and compliance. A generic AI assistant that doesn’t “know” anything about your business can be less effective. But just as you wouldn’t trust a stranger with sensitive information about yourself, your business data shouldn’t be shared with AI models that can’t keep it safe. And that’s another reason to use a dedicated AI copilot embedded in your business solutions, where you can maintain full control over decision-making and your data privacy in a safe environment.

Real-time

For a while after ChatGPT first became popular, its knowledge included data only up to September 2021. For a business, relying on outdated information is a recipe for becoming yesterday’s news. But when your AI co-pilot is embedded into your business ecosystem, it can automatically keep up with every change in your data. Current knowledge is a prerequisite for more accurate insights—and better outcomes.

Rewarding

Hardly anybody knows your business and your customers better than you; the trick is to utilise that knowledge well. For example, first-party data is a treasure trove that surprisingly many businesses underutilise. An AI copilot that’s integrated with your data helps put that hard-won knowledge to good use and make your painstakingly collected first-party data, powerful tools and workflows, and infrastructure—accessible and serving you.

What are the common AI co-pilot use cases?

So, what can you do with AI co-pilots? Depending on your goals and the exact copilot you use, there’s a near-endless variety of applications across many industries and roles. Here are a few common examples of how you can use an AI co-pilot for work (this list is not exhaustive):

Software development and code completion

Useful for: software developers, IT personnel, and companies working on their own applications.

Your AI copilot can help you to: write code, configure application logic, and simplify generation of data models, service entities, sample data, and UI annotations.

Example: with SAP Build Code, you can use SAP’s AI copilot, Joule, for coding, testing, integrating, and managing Java and JavaScript application lifecycles.

Recruitment and people management

Useful for: recruiters, hiring managers, and HR.

Your AI copilot can help you to: write job descriptions, streamline candidate assessment, prepare for interviews, help empower professional growth within the company.

Example: with SAP SuccessFactors HCM solutions, you can use SAP’s AI copilot, Joule, to write accurate and equitable job descriptions faster, avoid cognitive bias during candidate assessment, generate questions for the interview based on the job description, and come up with learning suggestions tailored to each employee’s professional goals.

Sales enablement and business optimisation

Useful for: sales teams, business growth managers, and most core operations (from finance and logistics to manufacturing and procurement).

Your AI copilot can help you to: write emails and other communications to customers and prospective leads, gain relevant insights faster, navigate productivity software and other business tools with ease, maximise the efficiency of your business operations, optimise decision-making processes, and much more.

Example: with SAP CX AI Toolkit, you can use SAP’s AI copilot, Joule, to find hidden insights, generate relevant content, and deliver effective sales engagements.

Marketing and customer experience

Useful for: marketing teams, content development professionals, customer experience managers, and others.

Your AI copilot can help you to: quickly generate and iterate compelling content, automate and accelerate audience segmentations, enhance customer experience, and craft data-driven strategies.

Example: with SAP Customer Data Platform, you can use SAP’s AI copilot, Joule, to create customer journeys, segments, and indicators faster, visualise customer profiles, gain real-time customer insights, fuel personalised experiences, and more.

The future of AI assistants: trends to observe

The examples above represent only a fraction of potential applications for AI co-pilots. Many other uses already exist, and even more will likely be discovered or made possible in the near future as virtual assistants become widespread. Tech giants are already rushing to introduce and promote their general-use copilot products, even as companies in other fields are developing specialised AI copilots for their niches and industries: from education to healthcare and from finance to real estate. Most prominent analysts seem to expect that generative AI will profoundly impact the global economy and productivity—not to mention, people’s daily life and work. Given its current and forecasted prominence, it’s natural to wonder about the safety and regulation of technology powered by generative AI.

AI copilots: ethics, governance, and reliability

Like most revolutionary technologies, AI copilots come with ethical implications. Governments, companies, and societies will need to harmonise its progressive, positive potential—against the risks of misuse that most impactful technologies pose. Since AI copilots rely on generative AI, these ethical and governance considerations apply to them, too. Here are some concerns about generative AI that are worth bearing in mind, especially when using an AI copilot for work:

Bias

Even though artificial intelligence operates with data and AI models, rather than opinions and feelings, like humans do, AI-generated content is not immune to biases. Since developers can train the AI using a variety of datasets from external sources, such as the Internet or third-party data vendors—such data may contain biases. Moreover, the selection of data itself can be inherently biased and even discriminatory.

Questions to consider: what are the sources of training data, and how transparent are the AI models used by your AI co-pilot?

Unreliable output

Generative AI can sometimes produce misleading or faulty output. In a phenomenon called “hallucination”, an AI platform may generate plausible yet factually incorrect content. For example, an AI-powered chatbot may include random falsehoods within its responses.

Questions to consider: does the developer of the AI co-pilot you’re using take sufficient quality assurance measures? Is the user informed about the nature and limitations of generative AI?

Security and compliance

As people become accustomed to relying on generative AI at work, compliance with data privacy practices, not to mention security, may become a concern for employers. For example, an employee may share their company’s proprietary information with an AI copilot developed and managed by a third party, inadvertently giving an external source unauthorised access to internal data. This would create a security risk. Alternatively, an employee may infer sensitive or private information that they’re not supposed to have access to—by using a copilot that draws on company data.

Questions to consider: is the organisation AI-savvy, and does it provide AI training to employees? Is the developer of the AI co-pilot mindful of security and compliance?

Many of these legitimate concerns can be addressed by using a dedicated AI co-pilot embedded into your workflow, rather than a generic external one. For example, Joule, SAP’s generative AI copilot, can help recruiters, who use SAP SuccessFactors Recruiting solutions, to avoid cognitive bias during candidate assessment and write job descriptions that are free of gender bias. In particular for employers, it will be important to get ahead of the AI trend. If the company proactively offers a functional and effective AI co-pilot integrated with the work systems, employees can have an easier time learning to use it correctly and responsibly—in a safe environment.

How to get started with an AI co-pilot?

Depending on the type of AI co-pilot you want to use, getting started can be very easy. An all-purpose AI assistant, like ChatGPT, isn’t hard to figure out: they’re often accessible directly through your browser or a downloadable app. And many specialised copilots are even easier: if they’re already integrated into the tools you use, you just need to start putting them to work.

FAQs

Learn more about how AI copilots revolutionise businesses.

Read more