Microsoft Copilot & ChatGPT: Trending Business Topics (Latest Insights)

Generative-AI copilots like Microsoft Copilot and ChatGPT have become the hottest conversation in business tech.

Over the past six months, search traffic has surged as companies try to understand what these assistants can actually do—from drafting emails and code to reviewing contracts, analyzing spreadsheets, and powering customer-service chatbots...

Leaders want to know how different industries (finance, marketing, legal) are putting them to work, which tools integrate best with existing software, and whether the promised productivity boost really delivers a solid ROI.

INDEX

• General Business Applications of AI Copilots

• Sector-Specific Applications

  • Finance

  • Marketing

  • Legal

• Key Areas of Interest and Search Focus

  • Business Use Cases & Examples

  • Integration Tools and Platforms

  • Pricing and Licensing

  • ROI and Productivity Impact

  • User Feedback and Adoption Experiences

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General Business Applications of AI Copilots

Search interest in AI tools skyrocketed after ChatGPT’s launch in late 2022... Generative AI quickly became a top trend for boosting productivity across industries. ChatGPT achieved 100 M users faster than any app in history and now dominates ~70 % of the paid AI tools market.

Business adoption has followed suit: 49 % of companies report currently using ChatGPT (with another 30 % planning to). In fact, over 80 % of Fortune 500 companies integrated ChatGPT into workflows within 9 months of its release – an unprecedented enterprise uptake. Key general use cases driving this interest include automating content creation, coding assistance, customer support, and document summarization.

Companies already using ChatGPT primarily leverage it for coding (66 % of firms), content/copywriting (58 %), customer support (57 %), and summarizing documents or meetings (52 %). This reflects a broad enthusiasm for AI copilots to handle tedious tasks, freeing employees for higher-value work. Business leaders are overwhelmingly optimistic: 97 % believe ChatGPT will benefit their operations, citing faster decision-making, more efficient processes, and enhanced communication as expected gains.

Search interest in generative AI has exploded since late 2022, reflecting massive public curiosity and adoption of tools like ChatGPT. Business leaders widely recognize that AI copilots can boost productivity, with 53 % expecting more efficient workflows and 48 % anticipating better decision-making.

Notably, Microsoft Copilot (the AI assistant baked into Microsoft 365 apps) has drawn intense interest from businesses. After its announcement, queries like “What is Microsoft Copilot?” and “Copilot vs ChatGPT” spiked as companies explored how it differs from standalone ChatGPT. Microsoft’s strategy to embed Copilot across Office, Teams, and more has resonated with organizations already using these tools.

This seamless integration appeals to businesses: if you already rely on Microsoft’s ecosystem, Copilot “fits right in” for everyday workflows. In general, business users are eager to apply AI copilots to common tasks like drafting emails, generating reports, analyzing spreadsheets, and providing conversational answers. For example, many professionals now use ChatGPT to write emails and documents, summarize meeting notes, or even draft slides – tasks that span industries. Summarization is a killer app: 53 % of business owners value AI’s ability to summarize information quickly.

Email assistance is another: 46 % expect AI to streamline email replies, and writing polished communications via ChatGPT has become a trending use case. Overall, the general business conversation around ChatGPT and Copilot centers on practical productivity gains – using AI to automate writing, research, and repetitive chores – and this is reflected in high search volumes for “AI business use cases” and “how to use ChatGPT for work.”

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Sector-Specific Applications

Finance

In finance, interest in ChatGPT and Copilot revolves around analysis, forecasting, and risk management. Financial teams are exploring AI to streamline reporting, generate insights from Excel data, and even assist with budgeting and forecasting. Many professionals search for ways to integrate ChatGPT with Excel for formulas and data analysis, as this can save hours in spreadsheet work. Fintech firms were early adopters – fintech accounts for ~7 % of ChatGPT-assisted purchasing journeys as of 2025, indicating robust usage. Banks and financial services are piloting AI copilots for customer service chatbots, fraud detection assistance, and market research.

Risk and compliance queries are also popular, as finance professionals ask how to use ChatGPT securely without leaking sensitive data. Notably, corporate IT policies have become a hot topic: by late 2024, 1 in 4 companies had banned generative AI tools like ChatGPT due to data privacy concerns. High-profile incidents (e.g., an employee unintentionally leaking source code to ChatGPT) fueled searches about “ChatGPT data security” and prompted finance leaders to seek guidance on safe use. Still, adoption continues – a Capgemini report found organizations piloting generative AI saw a ~6.7 % improvement in customer engagement in deployed areas, relevant for fintech customer experiences.

Finance thought leaders emphasize augmentation over replacement: AI is viewed as a “co-pilot” for analysts, not an autopilot for critical decisions. Overall, trending discussions in finance focus on use cases (automating reports, Q&A on financial data), integration (e.g., connecting ChatGPT with BI tools), and governance (setting policies to mitigate risk while reaping AI’s efficiency gains).

Marketing

Marketing teams have embraced generative AI at a remarkable pace. In fact, 73 % of marketers report using AI tools regularly for tasks like content creation and campaign ideation. The marketing sector has seen some of the largest search spikes related to AI: Google searches for “AI marketing tools” have surged 967 % over the last two years, with much of that growth in the past 6–12 months. Marketers are eagerly searching how ChatGPT can help write copy, generate ad creatives, draft social media posts, and even perform SEO tasks. Many “top use cases of ChatGPT in marketing” listicles and guides have trended recently, reflecting practitioner interest in applying AI to daily marketing work. Businesses see clear ROI here: AI can produce blog drafts, product descriptions, or email campaigns in a fraction of the time. Generative AI is changing how content is produced – from text to images. Marketers use ChatGPT or Bing Chat for brainstorming campaign ideas, while tools like DALL-E or Midjourney generate product visuals.

This mainstream adoption is evident in search behavior and surveys: one study found AI is already a mainstay in many marketing departments, especially in the U.S.. Marketers also value personalization via AI; queries about using ChatGPT for personalized marketing or customer segmentation have risen, aligning with trends of AI-driven personalized campaigns. Importantly, user interest in marketing AI goes hand-in-hand with ROI – terms like “improved ROI with AI marketing” are common, and marketers report efficiency gains that free them to focus on strategy. In summary, the marketing sector’s top trending topics include AI content creation, campaign optimization, AI-driven marketing tools, and case studies of brands successfully leveraging Copilot/ChatGPT (for example, Coca-Cola and Shopify have integrated generative AI into marketing and customer engagement initiatives).

Search trend for “AI marketing tools” – interest has skyrocketed (~10× growth) since late 2022. Marketers are heavily researching AI solutions for content creation and campaign automation. In fact, 73 % of marketers now use AI regularly, and searches for marketing AI tools reflect that surging demand.

Legal

The legal industry, traditionally cautious with new tech, is now a hotbed of AI discussions. In the last 6 months, lawyers and legal firms have ramped up searches and trials of ChatGPT-like tools. A recent ABA survey (2024) showed AI adoption in law firms nearly tripled from 11 % to 30 % in one year – a dramatic jump that mirrors the buzz in search queries about “ChatGPT for legal research”, “AI for contract review”, and “legal AI tools”. Notably, ChatGPT is leading the pack: among firms using or considering AI, 52 % are focused on ChatGPT as the tool of choice. Smaller firms are especially keen (over 60 % of solo and small firms are using or exploring ChatGPT), likely because of its low entry cost and huge time-saving potential for solo practitioners. Top applications drawing interest include automated document drafting, summarizing case law or contracts, and assisting with legal research – all tasks where ChatGPT or Copilot can act like a junior paralegal. Indeed, 89 % of lawyers are now aware of generative AI tools like ChatGPT, and 43 % of lawyers say they either use or plan to use generative AI in their legal work. This surge in awareness has led to many queries on best practices (e.g., “Is ChatGPT accurate for legal questions?” and “ethical guidelines for AI in law”).

Efficiency is the primary driver for legal AI adoption – attorneys report significant time savings on routine tasks (30 % of law firms saw productivity increase after adopting AI). At the same time, lawyers are discussing limitations; for example, the infamous incident of an attorney citing fake cases from ChatGPT (a story that went viral in mid-2023) has made “ChatGPT hallucination legal risks” a topic of concern.

Thus, user feedback in legal is mixed: enthusiasm for efficiency, tempered by caution around accuracy and confidentiality. Still, the trend lines are clear – legal searches and forums are filled with early adopters sharing how AI copilots help draft briefs, parse documents, and even prep for trials. The legal sector’s key trending topics include AI-driven legal research, contract analysis, and the ethics of AI in law – underscored by the expectation that generative AI will significantly transform law practice (nearly 47 % of lawyers believe AI will significantly change how law is practiced).

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Key Areas of Interest and Search Focus

Business Use Cases & Examples

Practical use cases are a top search focus as companies figure out “How can we actually use ChatGPT or Copilot in our business?” Common queries cluster around customer service, content generation, coding assistance, and data analysis. In fact, customer service chatbots are one of the most cited AI use cases – 56 % of businesses using AI employ it for customer service already. Entrepreneurs widely plan to deploy ChatGPT for customer-facing tasks: 74 % intend to use it for chatbot-style customer responses, hoping to improve responsiveness and personalization. Another major use case is content creation for marketing and communications; about 70 % of business owners say ChatGPT helps generate content quickly, and ~30 % of small businesses even plan to have ChatGPT write website copy. Coding is also a prominent use – technical teams use GitHub Copilot or ChatGPT to accelerate software development.

GitHub’s Copilot (an AI code assistant) is used by over 400 organizations and has been reported to cut certain programming tasks from 10 minutes to 30 seconds. This kind of dramatic efficiency gain generates a lot of buzz and search interest in tech circles (e.g., “Copilot for code examples” or “AI to fix coding errors” – indeed 41 % of company founders say they plan to use GPT to help debug code). Many recent articles enumerate “Top 8/10 ChatGPT business use cases in 2025,” indicating high readership and search volume for such practical guidance. Case studies and success stories are particularly popular as businesses seek validation from peers – for example, how Coca-Cola uses generative AI in marketing, or how PwC uses ChatGPT for research, etc., which have been highlighted by OpenAI and the media. In essence, interest in business use cases spans from generic (writing, researching, summarizing) to domain-specific (e.g., “financial report analysis with ChatGPT”, “AI for legal document review use case”), reflecting the broad applicability of these copilots.

Integration Tools and Platforms

Another key interest area is integrating ChatGPT/Copilot into existing workflows and tools. Users frequently search for how to connect these AI assistants with their everyday software. For example, “ChatGPT Slack integration” and “ChatGPT in Microsoft Teams” have been trending as companies want AI help within their collaboration platforms. In fact, Salesforce (Slack’s parent) introduced an official ChatGPT app for Slack in early 2023, leading to many inquiries on enabling it. Similarly, Microsoft has woven Copilot into Teams meetings (automatically generating meeting summaries and action items), which sparked interest in “Teams Copilot features”. API integrations are also hot topics: developers and IT managers search the OpenAI API or Azure OpenAI Service documentation to integrate GPT capabilities into their own apps or websites.

Tools like Zapier and Power Automate featuring ChatGPT connectors have seen increased searches as businesses aim to automate workflows (e.g., auto-generating a response or report when new data comes in). There’s also an emerging ecosystem of third-party copilots tailored to specific platforms – for instance, “Vena Copilot” for FP&A (finance) which connects to financial data in spreadsheets, or legal-specific copilots (Harvey AI, etc.) for law firms. Searches around these often include comparisons (e.g., “ChatGPT vs [specific] Copilot – which to choose?”). The overall trend is that business users want AI where they already work – hence integration with CRM systems, email clients, project management tools, and more is a frequent topic. Plug-ins and extensions have become popular search terms too (e.g., “Outlook Copilot email plugin” or third-party Chrome extensions that bring GPT into various SaaS applications). In short, “integration” is a buzzword – companies are looking up how to seamlessly embed AI assistants into their tech stack, indicating that isolated AI chatbots are less desirable than AI that’s embedded in business processes.

Pricing and Licensing

Cost is a major area of interest as organizations weigh these AI tools. Microsoft made headlines by pricing Microsoft 365 Copilot at $30 per user per month (on top of existing Office 365 licenses), a level that prompted many searches like “Copilot $30 cost worth it?”. Business owners and IT admins are actively researching pricing models, volume discounts, and overall cost of ownership.

Similarly, OpenAI’s ChatGPT Enterprise offering, launched in late 2023, has drawn pricing curiosity – though not publicly listed, it’s reported to be roughly $60 per user per month for enterprise plans. There’s also a ChatGPT Team tier for smaller teams (~$20–30 per user), which people often compare with Microsoft’s pricing. Many SMEs are asking: “Should we pay for ChatGPT Enterprise or stick with ChatGPT Plus?” (Plus being $20/mo for a single user). Comparisons of value – e.g., “ChatGPT vs Copilot – which gives more bang for buck?” – are common in forums and blogs, since businesses want to invest wisely.

The ROI angle also ties in here (see next section): Microsoft cites that Copilot can drive >100 % ROI, which companies factor against that $30/user cost. Pricing transparency and changes are closely watched; for example, any news of price adjustments or new tiers tends to spike searches (if OpenAI or Microsoft announce new plans, expect a flurry of queries). License requirements are another facet – Microsoft requires certain M365 plans to add Copilot, and users have been searching “Who is eligible for Copilot?” or “Copilot availability for Business Standard”, etc. In summary, pricing and licensing questions – including cost justification – are top of mind as organizations budget for these tools, making it a heavily searched topic in the past half-year.

ROI and Productivity Impact

Ever since these AI copilots hit the scene, businesses have been laser-focused on return on investment (ROI). A key question driving interest is: “Will deploying ChatGPT/Copilot actually save us money or time?” Recent data suggests yes – and these insights are fueling even more searches for ROI evidence. Microsoft commissioned a study for SMBs showing 132 % to 353 % ROI over 3 years from Microsoft 365 Copilot adoption, which has been cited in many discussions. Leaders are hungry for such figures; queries like “Copilot ROI case study” or “ChatGPT productivity statistics” have been common. Independent analyses show that even modest time savings translate to large ROI on the $30 Copilot cost. For example, saving just 1.5 hours per employee per month can yield roughly 68 % ROI, and ~2 hours/month yields 124 % ROI on Copilot.

Essentially, if Copilot helps an employee save an hour or two on writing, research, or coding, the tool more than pays for itself in wage terms. These calculations (often shared as infographics and calculators) are popular in tech blogs and LinkedIn posts, further spreading the data. No surprise, then, that “Copilot ROI” and “ChatGPT productivity gain” are trending phrases. Early adopters are sharing concrete outcomes: Some companies report employees reclaiming ~30 % of their time on certain tasks thanks to AI assistance. One Forrester study cited by Microsoft found Copilot early adopters achieved a 6 % increase in revenue and 20 % reduction in operating costs due to efficiency improvements. Such claims drive interest as others look to validate these benefits. On the flip side, user feedback on ROI can also be cautious – e.g., discussions on “hidden costs” (training staff, managing AI outputs, etc.) appear in forums. Overall though, the narrative in searches and articles is that AI copilots deliver strong ROI by saving time, and businesses are actively calculating and seeking evidence of these returns.

Example ROI calculation for Microsoft 365 Copilot: Even minimal time saved per employee can justify the $30/month cost. Saving ~2 hours monthly yields an estimated 124 % ROI, and ~2.5 hours jumps to 180 %. Such projections have spurred interest in ROI calculators, with leaders evaluating how quickly Copilot would “pay back” via productivity gains.

User Feedback and Adoption Experiences

Finally, businesses are keenly interested in peer feedback and real-world experiences with ChatGPT and Copilot. As these tools moved from hype to actual workplace deployment, searches for “ChatGPT business review”, “Copilot user feedback”, and “case study [industry] ChatGPT” have grown. Broadly, sentiment is very positive among adopters. Surveys show 55 % of business leaders rate ChatGPT’s work quality as excellent, and another 34 % as very good – an 89 % satisfaction rate. This kind of feedback (widely shared in Forbes and other outlets) reassures potential new users and thus gets referenced often. Many executives publicly tout the benefits: for instance, one SMB leader remarked that “in five years, running a business without Copilot would be like using typewriters instead of computers” – a strong endorsement that has been quoted in articles and social media, reflecting enthusiasm for Copilot. User communities (Reddit, LinkedIn groups) are abuzz with tips and pitfalls, which others closely follow.

There’s also interest in employee acceptance: a GWI study found over 60 % of employees using AI at work are comfortable sharing AI outputs with coworkers, and more than half are even comfortable doing so with their boss. This suggests that internal adoption is smoothing out as people trust the tools more, which is a positive feedback loop often noted in discussions. Of course, not all feedback is glowing – concerns around accuracy (hallucinations) and ethical use are frequently raised. Trending questions include “Can we trust ChatGPT’s answers?” and “How to prevent AI errors in business”. Nonetheless, companies report tangible successes: e.g., **48 % of firms using ChatGPT have already replaced some human work with AI (for better or worse), and many entrepreneurs credit it with boosting web traffic and customer engagement. This kind of feedback (savings, improved metrics) is highly sought after as others benchmark their own outcomes.

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