AI in Business Communication: Revolutionizing How We Connect and Collaborate

Ioana Andrei
By Ioana Andrei, Updated on July 03, 2024, 11 min read

AI will drive productivity and business growth, say six in ten business owners. And they’re right on the money—AI could enable $13 trillion worth of economic activity by 2030.


In business communications, generative AI (GenAI) helps you quickly produce original content while increasing staff productivity and message personalization. Whether you’re nurturing relationships with clients or with employees, you must learn to use AI tools effectively to stay ahead of competitors.


Sound daunting? Don’t worry. In this guide, we break down the top AI trends to be aware of and offer practical tips to polish your internal and external comms with AI tools.


Let’s start.



The AI Landscape: A Compendium of Artificial Intelligence

AI advances so quickly that benchmarks for its capabilities become obsolete in just a few years. But don’t fret. Whether you’re a tech newbie or an Altman groupie, we rounded up the top facts to know about the AI landscape in 2024.



What is AI and how does it work?

Artificial intelligence, or AI, enables a machine to simulate human-like abilities such as problem-solving, creativity, and reacting to context changes. Its value to people and businesses lies in productivity gains—for instance, helping us complete tasks more accurately, faster, or at a greater scale.


AI powers many of our day-to-day technologies, like Apple’s Siri assistant and Spotify’s personalized music recommendations. Plus, it drives many business-to-business (B2B) platforms’ ability to automate processes and give data-based suggestions.


How do AI systems achieve this?


In short, they use multi-layered algorithms that analyze data patterns, conclude, and produce outputs similarly to how human brains would. These analysis layers vary in complexity depending on the type of AI.


For instance, supervised models rely on human-made categories and instructions, while unsupervised models automate learning without human direction.


4 types of AI that enhance business communications

Let’s break down how different AIs support comms tasks.


1. Artificial general intelligence

Artificial general intelligence (AGI) is the ability of a non-living system to perform any task a human can. For instance, it could learn new languages, recognize people’s emotions, solve problems, and think and speak like humans do. While we’re still years away from this innovation, there’s a pressing problem.


There’s no agreed definition of AGI. In the mid-twentieth-century days of robotics, the Turing Test (not being able to tell a machine’s outputs from a human’s) was deemed sufficient to judge AGI.


However, modern organizations like OpenAI state that their internal governance board will determine when they’ve achieved AGI. So, we may only understand what it looks like when we’re closer to achieving it.



2. Narrow AI

Narrow AI, as opposed to general artificial intelligence, is optimized to complete a single type of task. For example, Amazon’s Alexa answers user questions, while customer relationship management (CRM) AI produces task recommendations based on sales data.


Most AI tools available today are narrow AI. Not only are technologies easier and faster to develop for targeted use cases, but they’re also easier to commercialize. That said, some narrow AI tools, like conversational chatbots, can serve many purposes, such as research, text editing, and code debugging.



3. Machine learning

Machine learning (ML) is a subset of AI that improves its performance over time through feedback loops and responses. In simple terms, its learning algorithm includes a data processing function, an error function that tests the accuracy of specific predictions, and an optimization process that aims for better predictions over time.


The more data it processes, the better ML performs at tasks such as text prediction and facial recognition. In practice, however, ML isn’t only used to make predictions. Advanced Large Language Models (LLMs) like OpenAI’s ChatGPT use ML to create new content (like words or code) based on the next most likely group of words in an answer.



4. Deep learning

Deep learning is a type of ML that learns from and acts on complex unstructured data using deep neural networks. These networks consist of connected nodes (inspired by brain neurons) across input and output layers, plus multiple “hidden” layers that learn complex data patterns.


The nodes pass on information if the results of an analysis pass a logical “threshold”. In a text generation example, the phrase “dogs are happy” might not pass the threshold to be considered for an answer to “What’s a good social media recruitment strategy?”


A key difference between deep and non-deep ML is that the latter needs human-labeled data. For example, say you’re creating a facial recognition AI. A deep learning system can learn how to identify face components on its own, while a non-deep ML needs human-made label inputs such as “nose”, “cheeks”, and “skin color”.



What are the latest innovations in AI for business communications?



While AI’s founding principles date back to mid-twentieth-century mathematics (see: Alan Turing’s experiments), its technology and commercialization have exploded since 2022. This is due in no small part to OpenAI’s ChatGPT-3.5 release, followed by subsequent releases of ChatGPT-4 and -4o, Microsoft’s Copilot, Google’s Gemini, and Apple’s Apple Intelligence.


These GenAI tools understand natural language queries, generate context-specific responses, and have human-like conversations. In turn, new platforms are emerging using text, image, and video AI generators, with business applications including:

  • Desktop assistants like Microsoft’s Copilot:

    Offering improvement suggestions and task automation in programs like Word and Excel.

  • Design generators such as Canva’s GPT

    Creating platform-centric (say, Instagram-ready) graphics based on conversational prompts.

  • Writing assistants like ChatGPT’s creative writing coach

    Giving writing feedback and recommendations based on context such as target audience and tone.

  • Research synthesizers like Consensus

    Scouring academic resources (for instance, from Google Scholar results) to summarize fact-checked findings.


Increasingly, many SaaS companies are producing in-house AI features to enhance their users’ productivity and business results. Examples include:


  • Salesforce’s AI-driven revenue forecast and sales funnel recommendations.


  • Spike’s Magic AI message generator and productivity assistant.
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What’s next for AI?

Here’s Spike’s take on upcoming AI developments in business communications.


B2B adoption of narrow AI

First, we’ll see significantly more B2B platforms integrating narrow AI. In part, this will happen through partnerships with AI model builders (like OpenAI), and mergers and acquisitions.


For example, Salesforce’s acquisition of, a customer service solution. For business users, this will translate to faster workflows and more conversion-optimized comms.


Accurate ML predictions

Second, machine learning predictions will become increasingly accurate—thanks to more users feeding data to ML systems and developers increasing ML’s ability to contextualize inputs.


As an example, a customer support ML system will accurately predict when unhappy clients are likely to shop elsewhere based on their comms history and prompt the vendor to offer freebies.



General AI

Third, in the long term, leaps toward general artificial intelligence will be made with:

  • Increased human emotion recognition.

    You might, for instance, receive an acknowledgment like “I understand your frustration” from a call or chat AI bot.

  • Multimodal communication with large language models.

    This means you can query a bot in a mix of mediums (like audio, video, and text) and receive replies in the most context-appropriate mediums. It’s as if a friend responded orally and then played a video when asked, “What sort of music have you been listening to lately?”

  • Broad intelligent assistants.

    They could, for example, book client meetings, transcribe video calls, and generate lead emails based on sales decks.



4 Practical Ways to Leverage AI For Business Communication

Some of the departments with the highest dollar return from AI use include communication-centered ones like sales, marketing, and customer operations. This is thanks to technologies like GenAI which can add up to $4.4 trillion in economic value across productivity-enhancing use cases.


Want a share of this AI pie? Here are 4 must-dos to enhance your business comms with AI.



1. Generate and polish comms content

About a third of firms produce content with AI, with six in 10 using it to optimize emails. No wonder—it helps you finish comms faster, freeing time to nurture internal/external audiences, bake strategic plans, and evaluate campaigns.


Plus, in some cases, GenAI proposes ideas you haven’t thought of, even after hours of internet research. You can use GenAI to plan and draft blog articles, staff announcements, customer newsletters, ad copy and graphics, social media posts, presentations, reports, and much more.


Using a combination of AI-enabled tools, you can:


  • Generate complete drafts for copy including emails, chat messages, online and physical ads, and social media posts.


  • Get input to plan your content, including blog post ideas, audience pain points, and report headings.


  • Turn a type of content (such as a white paper) into another type (say, an Instagram caption).


  • Generate images using verbal prompts like “luxurious sunset on the beach to attract vacationing couples to my hotel”.


  • Streamline comms research by summarizing content from PDF docs, long-read articles, and graph and chart data.


  • Ask for editorial feedback on elements like structure, logic, sentence flow, use of “fluff” words, and persuasiveness.


  • Change the tone of your copy—for instance, from academic to conversational, or from friendly to persuasive.


Pro tip: How you query GenAI can massively improve its output. Here are Spike’s expert tips for creative prompting.



  • Play with the wording that describes your ideal result. For example, get a more inspiring rough draft by requesting an email that’s “bold, magical, and thought-provoking” rather than “professional”.


  • Customize advanced bots like ChatGPT with rules to reduce time and repetition. Using the above example, your rule could be “Whenever I ask for email drafts, make them bold, magical, and thought-provoking.”


  • Give the AI plenty of examples of what your results look and sound like. For instance, when stuck, request “10 options for the end of this sentence: ‘Dissatisfied customers stop buying, discourage other shoppers, and…’”—rather than a broader “What can dissatisfied customers do?”


AI platform spotlight: Spike’s Magic AI removes writer’s block and engages team members with tailored emails, messages, and summaries.


Its natural language processing (NLP) capabilities help Magic AI generate original responses to prompts like “Write a quirky team invite to the product launch party.”



2. Automate tasks and processes

Half of companies use AI to automate processes. This benefit allows marketing, sales, and support teams to spend more time nurturing customer relationships.


Plus, more timely internal communications (like product updates and employee polls) lead to higher employee engagement and retention.


And the cherry on top?


Automation reduces human errors. Here’s a taster of how to automate tasks and processes using AI tools in CRM, marketing, helpdesk, and collaboration platforms.

  • Sales:

    Trigger basic email replies such as client check-ins; Schedule lead meetings and generate tasks based on email content; Automate workflows with sequences of calls, emails, and tasks.

  • Marketing:

    Schedule newsletters based on subscriber preferences and local times; Auto-translate copy for local audiences; Personalize sales email tone and products according to past purchasing behavior.

  • Customer service:

    Provide advanced search functionality on customer-facing blogs and FAQs to increase self-service resolution rates.

  • Internal communication:

    Use email smart replies (like Spike’s) to collaborate faster; Generate meeting summaries and action points from call transcription; Auto-filter emails and chat messages based on content.


3. Streamline customer experiences

Customer service teams can use GenAI to increase issue resolution by 14% and reduce handling time by almost a tenth. This is great news—you retain more clients who’d otherwise feel frustrated with long waiting times and ill-prepared support staff.


Here’s how AI tools help streamline customer experiences.


  • AI assistants interpret basic customer inquiries and summarize or link to existing resources about your products and policies.


  • LLM chatbots have human-like conversations, react to context (for instance—mentioning a product and price), and trigger actions for support agents (like arranging a refund or a callback.)


  • Your helpdesk software auto-categorizes and prioritizes customer comms based on query type and purchase history.



4. Make data-based comms decisions

The speed and accuracy of AI data crunching outdo human ability. (Remember how IBM’s Deep Blue program beat chess heavyweight Garry Kasparov in 1997 by calculating the outcome of every move?)


In turn, fast and factual data enables better comms decisions—like which customer niche to target or when to hold internal performance reviews. As poor business decisions lower profitability by 3%, it’s worth picking a good data cruncher.


Here are two examples of AI-informed communication decisions.



Sentiment analysis insights

Sentiment analysis platforms like Sprout Social analyze audiences’ feelings and thoughts about your brand based on social media posts and comments (including emojis.) This enables smart communication decisions.


For instance, when customers describe your support agents as “robotic”, train your staff on tone and responsiveness.



Chatbot as a sparring partner

An LLM chatbot can help you reach logical, unbiased decisions. First, give it plenty of context about a scenario. For instance, upload your company business plan and customer purchasing data. Then, tell it what logical frameworks you want to use. Describe, say, Porter’s Five Forces for market competition analysis.


Based on these inputs, ask the chatbot how it would reach a strategic decision such as picking a target market for maximum customer reach. Then, compare its response to your thought process and remove potential biases.



The Future of Communication Is More Intelligent With Spike

Put Magic AI ✨ to work for you

AI is advancing rapidly with machine learning systems like ChatGPT and Copilot, and soon it will learn to recognize human emotions and expand beyond narrow tasks.


For now, GenAI tools in business communication give you a competitive edge through personalizing content, making data-driven decisions, and increasing pockets of productivity. An AI-powered all-in-one communication app is your ideal partner.


Spike Teamspace brings all your emails and chats into one elegant inbox, complete with an AI assistant that generates smart replies, summarizes documents, plans your tasks, and more.


Plus, you get one-click video meetings, collaborative tasks, and file sharing in one virtual team hub.

Ioana Andrei
Ioana Andrei Ioana has worked for 4+ years as a management consultant in the tech and telecom industries. With a wealth of enterprise and start-up client experience, Ioana is also an accomplished SaaS and B2B tech writer.

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