The New Wave of AI at Work

As Tobi Lütke (CEO of Shopify) recently declared in an internal memo, “Reflexive AI usage is now a baseline expectation (at Shopify).” Similarly, Fiverr’s Micha Kaufman wrote an email to his entire team with the subject line, “AI Is Coming for Your Job — Even Mine.” This might sound alarming, but the truth is simpler: AI is rapidly becoming a core skill for everyone, from HR to finance to engineering. AI might be coming for your work, but your title is yours to keep only if you’re leveling up with it.

2025 is the year of AI in the Enterprise - we’ll explore what new wave of AI tools means for you — whatever your role or level in the organization. And in case you’re not at all used to it yet, we’ll also share practical ways to start using AI tools like ChatGPT, Gemini, or Claude right away.

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1. The Changing Nature of Work: Insights from CEOs

“AI is coming for your job—heck, it’s coming for mine too,” said Fiverr’s Micha Kaufman. This kind of statement can feel jarring, but the real message is about adaptation, not fear.

Tobi Lütke put it bluntly: “Using AI effectively is now a fundamental expectation of everyone at Shopify… including me.” The subtext is that those who embrace these tools and continually refine their skills will thrive in the emerging job market. Those who resist may find themselves lagging behind as the organization looks for ways to do more with fewer resources.

Even Tommie Lo of Preface—an education-tech leader—echoes that “AI fluency is now a fundamental expectation.” This means it’s not just a North American phenomenon; it’s a global shift. Across the board, there is a growing policy mandate that AI is no longer optional. We’re seeing that shift extend beyond engineering roles into everyday administrative, creative, and strategic tasks.

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2. Why This “New Wave” of AI Is Different

For years, AI research simmered in labs, focusing on niche applications like image recognition or advanced analytics for big tech. Over the past 18–24 months, Generative AI—the type that can write text, generate images, even code—has blown that door wide open.

1. Easy Accessibility: Thanks to products like ChatGPT or Claude, you no longer need a PhD in machine learning to experiment with AI. If you can type a prompt, you can harness generative AI.
2. Broad Impact: We’re not just talking about coding tasks. AI can help with customer support emails, financial projections, marketing copy, data insights, and even product design suggestions.
3. Integrations in Existing Tools: Many companies are embedding AI into their existing software. For example, Google Workspace is integrating Gemini AI features into its suite of tools from Gmail to Google Meet, making it easier to draft emails or summarize meetings.
4. Speed & Scale: What once took weeks or months might now be done in hours. This means teams can iterate and experiment faster—often without the need for large, dedicated budgets.

Bottom Line: If you keep hearing “AI changes everything,” it’s not hype. The shift is real, and it’s reaching every corner of the org chart.

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3. What This Means for Your Workflows and Jobs

Here’s how generative AI is transforming various functions:

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a. HR: People and Talent Empowered by AI

Human Resources is seeing generative AI streamline both administrative and strategic people operations. HR teams are using AI assistants to automate repetitive tasks and enhance decision-making, freeing them to focus more on people. For example:

• Content & performance: Generative AI can act as a writing coach for HR. It helps craft internal content like training materials or policy FAQs, and even suggests personalized employee goals. In SAP SuccessFactors, an AI writing assistant can auto-draft learning course descriptions or performance goals in a fraction of the time [1]. This not only saves time but ensures consistency and clarity in communications.
• Talent acquisition: AI is supercharging recruiting by screening and generating content. Platforms like Workday and SAP now use AI to infer candidates’ skills from resumes and match them to job requirements [1]. Recruiters can get auto-generated shortlists or even first-draft job descriptions without starting from scratch [2]. This speeds up hiring cycles and helps uncover qualified candidates who might be overlooked otherwise.
• Employee insights: AI copilots give managers on-demand summaries and recommendations, turning raw HR data into actionable insights. For instance, SAP’s AI can instantly summarize an employee’s compensation and job history into key talking points [1]. This means before a performance review or promotion discussion, a manager gets a concise brief of the employee’s tenure, accomplishments, and pay history, leading to more informed and fair decisions.

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b. Finance: Automation and Insights for CFOs

Finance and accounting departments are embracing generative AI to automate tedious processes and reveal deeper insights. By embedding AI into finance software, companies are cutting down manual work (think invoice coding or report writing) and accelerating analysis. Key examples include:

• Expense management automation: Mundane finance chores like expense reports are being offloaded to AI. Workday’s new Expense Assistant is a great example – it can automatically draft entire expense reports by matching receipts to credit card charges, then submit and even approve them based on policies [2]. This eliminates the need for employees to manually compile and enter expenses, drastically reducing month-end paperwork.
• Conversational analytics: Modern ERP systems now come with chatty AI copilots that act like financial data concierges. Instead of digging through menus, finance teams (or any authorized employee) can simply ask a question and get an instant answer with context. In SAP S/4HANA, the generative AI assistant “Joule” lets users query business data in plain English [3]. For example, a controller could ask, “What’s our current cash flow from North America?” and Joule will pull the latest figures or even generate a quick report with charts. Early adopters report spending ~30% less time on such administrative queries [3], freeing analysts to focus on strategy.
• Financial narrative and forecasting: Generative AI can also draft narrative summaries of financial results and assist with planning. Some finance software now automatically generates written analysis of quarterly numbers for board reports, or builds preliminary forecasts based on historical data and assumptions provided. While human review is always needed, these AI-generated first drafts give finance teams a head start in analyzing scenarios and crafting the story behind the numbers.

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c. Engineering and Product Development: Coding and Planning with AI

In engineering and product teams, AI has essentially become a junior teammate – writing code, summarizing docs, and speeding up project workflows. Software developers and product managers are leveraging AI to handle grunt work and boost creativity. A few notable impacts:

• AI pair programming: Writing code is faster and less error-prone with AI “co-developers” in the IDE. Tools like GitHub Copilot suggest entire functions or fix bugs as you code, based on context [4]. By 2025, such AI coding assistants are ubiquitous – developers use them to generate boilerplate code, write unit tests, and even refactor legacy code. This not only accelerates development but also helps newcomers follow best practices by learning from AI suggestions.
• Automated ticketing and documentation: Generative AI is embedded in project management apps to keep product work organized. Atlassian’s Jira and Confluence, for instance, have AI features that can scan a project spec and automatically break it into Jira tasks or user stories for the team to tackle [5]. Confluence can also summarize long requirement docs or meeting notes into a quick recap for someone who missed them. By having AI draft these elements, product managers ensure nothing falls through the cracks – every requirement turns into a task, and every decision is logged in succinct form.
• Knowledge retrieval and Q&A: Engineering teams generate tons of documentation, from design docs to dev team wikis. AI assistants help make sense of this knowledge jungle. Notion, for example, introduced an AI that can answer questions by searching across your workspace wikis, Slack threads, and even PDFs [6]. A developer could ask, “How does our login authentication work?” and get an instant explanation drawn from the relevant design doc, instead of manually searching. This kind of AI-driven Q&A means less time hunting for information and more time building.

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d. AI in the Tools You Use Every Day

Perhaps the most exciting trend is how generative AI is no longer a standalone novelty – it’s woven directly into the everyday software that employees already use. Whether you’re in sales, design, or operations, chances are your go-to SaaS apps have gotten an AI upgrade in the past year. These built-in copilots and assistants are transforming workflows in place, without users needing to switch to a separate AI tool. A few shining examples of AI embedded in daily work apps:

• Salesforce’s Einstein Copilot: Salesforce has native generative AI across its CRM modules. A sales rep can ask Einstein to draft a follow-up email after a client call, complete with context from the call notes [7]. It can also answer ad-hoc questions – e.g. “Show me this quarter’s pipeline by region” – pulling real-time data from Salesforce. For marketers, Einstein can generate campaign copy or social posts tailored to past customer engagement [7]. All of this happens right within the Salesforce interface employees use all day, supercharging productivity without leaving the CRM.
• Canva’s Magic Studio: Canva, the popular design tool, introduced Magic Studio as an all-in-one AI design suite. A marketer or designer working in Canva can now do things that used to require separate apps or lots of manual work: use Magic Write to generate catchy text for a flyer or social post, Magic Design to instantly create a layout from a prompt, or Magic Media to generate custom images and videos [8]. For example, you can literally type an idea (“a cyberpunk-themed background with our logo”) and watch Magic Studio produce an image and slide design to match. It’s design on autopilot – though always with the user in control to refine the creative output.
• Notion AI in Workspace: Notion has become a staple for documentation and project notes, and with Notion’s AI integration, it’s like every user got a personal writing assistant. Notion AI can translate a rough bullet list into a polished paragraph, fix the tone of a memo to fit company style, or summarize a lengthy spec doc for an executive summary [6][9]. Moreover, it connects to your other tools: Notion’s AI can fetch answers from content in Slack threads, Google Docs, or past Notion pages [6]. That means when you’re drafting a project plan, you can ask, “What was our outcome from Project X?” and get an answer sourced from your knowledge base – without leaving Notion.
• ServiceNow’s Now Assist: Employees who use ServiceNow for IT or HR support are now greeted by a smart copilot. Now Assist is ServiceNow’s generative AI helper embedded in its workflows. If an employee needs help (say, “How do I update my VPN credentials?”), the AI will search internal knowledge bases and provide a conversational answer, or even perform an action like initiating a password reset. For IT agents, Now Assist can summarize incoming incident tickets and suggest likely resolutions, drawing on similar past cases [10]. All this happens in the same ServiceNow portal and chat interface that millions already use to get help at work, making problem-solving dramatically faster.

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e. Marketing and Sales: Accelerating Growth with AI

Marketing and sales teams have eagerly adopted generative AI, since it directly helps grow revenue and engage customers. From creating content to personalizing outreach, AI is working alongside marketers and sellers to scale their efforts. Here’s how:

• Content creation at scale: Generative AI is like a turbocharger for content marketing. Instead of spending days drafting blog posts, email campaigns, or product descriptions, marketers can have AI create solid first drafts in minutes. For example, HubSpot’s Content Assistant can generate a full blog article or landing page copy in your brand’s voice with just a prompt [11]. Salesforce’s Einstein Copilot likewise lets marketers whip up an entire campaign brief or social media calendar by describing their goals [7]. Of course, humans still edit and polish the content, but AI handles the heavy lifting of the initial draft. This means teams can produce more content in less time, reaching more audiences without scaling headcount.
• Creative design and personalization: AI is also helping create the visual and creative assets for campaigns. Adobe’s Firefly generative AI, now integrated into Creative Cloud tools, allows marketers to generate endless variations of images and designs by simply describing what they need. Need an on-brand banner ad in 10 different styles? Firefly can do that in seconds, while respecting your brand guidelines via custom style training [12]. Similarly, Canva’s Magic Studio (as mentioned) can produce everything from social posts to presentations on the fly, which is a boon for small marketing teams. On the personalization front, AI enables dynamically tailoring content to each audience segment. Marketers can have AI rewrite headlines or adjust imagery to better appeal to, say, Gen Z on TikTok versus Boomers on Facebook – all automatically. This level of personalization at scale was impractical before, but generative AI makes it achievable, leading to higher engagement and conversion rates.
• Sales outreach and CRM intelligence: For sales representatives, AI has become the ultimate sales coach and admin assistant. One major use is drafting personalized outreach. HubSpot’s Prospecting AI Agent can compose one-to-one sales emails enriched with CRM context, helping reps reach out to leads with messages that feel hand-written [11]. It considers contact details, past interactions, and tone, so a salesperson might have a ready-to-send email that reads as if they spent an hour researching the client – but it took seconds. AI is also summarizing sales calls and research: tools like Gong and Zoom IQ (and now Salesforce’s own AI) transcribe customer calls and highlight key moments or objections. A rep can get an automatic summary of a 30-minute call, complete with suggested next steps. And in CRM systems, AI assists with data entry and forecasting; mundane tasks like logging call notes or updating opportunity fields can be done by Einstein Copilot just by asking it to, or sometimes fully automatically based on emails and calls [7]. The result is salespeople spend less time on admin and more time selling, with AI prompting them on the best actions to close deals.

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f. Operations: Smarter Support and Processes

Operations teams – from IT and customer support to supply chain and logistics – are leveraging AI to run things more efficiently behind the scenes. A core theme is using generative AI to resolve issues faster and optimize resources. Here are some ways AI is embedded in operations workflows:

• AI support agents: Both internal IT helpdesks and customer support centers are deploying AI agents to handle routine inquiries. These aren’t clunky chatbots of the past, but intelligent assistants that understand context and language. ServiceNow’s Now Assist and Atlassian’s virtual agent in Jira Service Management can interact with users in chat to troubleshoot common problems [13]. They pull answers from knowledge base articles and company policies, thanks to retrieval augmented generation that keeps responses accurate to the organization’s data [14]. The impact is significant – Atlassian reported that their AI-powered support agent handled over 50% of incoming IT requests internally within the first month, saving their teams ~500 hours while maintaining a 4.5/5 satisfaction rating [13]. By deflecting FAQs and simple tasks (like password resets or order status checks), AI agents free up human support reps to focus on complex, high-touch issues.
• Incident and alert management: In IT operations and security, teams deal with floods of system alerts and incident logs. Generative AI is a game-changer here by translating these noisy alerts into concise, human-readable summaries. For instance, ServiceNow’s AI for IT Operations Management will take a cryptic system alert and explain in plain language what went wrong (“Server X is experiencing memory leaks affecting service Y”) [10]. It can even suggest likely fixes by cross-referencing past incident resolutions [10]. This context allows on-call engineers to grasp the situation faster and take action before issues escalate. Similarly, AI can summarize customer incident tickets or chat transcripts for support agents, highlighting the core problem and sentiment. When an agent opens a case, they get a one-paragraph AI summary instead of slogging through five pages of back-and-forth, leading to faster resolution times.
• Resource and process optimization: Beyond support, generative AI is helping optimize how work gets done in operations. A great example is staffing and resource allocation. SAP built an AI feature that automatically matches employees to projects based on their skills and availability [15]. Instead of managers manually searching for the right internal consultant or engineer for a new project, the system’s AI suggests the best fit in seconds, reducing time to staff projects and making sure talent is utilized effectively. In broader operations, AI analytics tools monitor workflows and can flag bottlenecks or inefficiencies. Workday, for instance, is introducing an “Optimize” AI agent that watches business processes (like procurement or invoice approvals) and identifies where things get stuck [2]. Ops leaders then get recommendations to streamline those steps. This kind of proactive ops intelligence was rarely possible before – now the AI is essentially an operations analyst continually looking for improvements.

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4. Leveling Up Your Skill

You might be thinking: “I’m not an AI engineer—do I really need to learn this?” Short answer: Yes. Long answer: AI fluency is becoming a core competency in many organizations—especially now that GenAI is baked directly into the SaaS tools you already use. Here’s how to dive in:

1. Self-Directed Experimentation

   • Open the apps you use every day—Notion, Canva, Salesforce, Google Workspace, ServiceNow, you name it—and click the AI icon. Summarize a meeting note inside Notion AI, ask Canva’s Magic Write for a first-draft headline, or let Salesforce Einstein Copilot propose next-step tasks. Because the AI is “in situ,” you aren’t context-switching; you’re super-charging the workflow you were already in.
   • Still love ChatGPT or Claude? Great. But start by testing the embedded AI your company subscribes to; it’s already wired for security and data privacy, so you can work with real docs and customer data instead of mock samples.

2. Take Advantage of Internal Trainings

   • Companies like Preface run “lunch-and-learn” demos that show how AI pops up inside common apps—e.g. spreadsheet formulas suggested by Gemini in Google Sheets or auto-generated incident summaries in ServiceNow. Attend, or suggest one if it’s missing.
   • Pitch an internal “AI Day” where teams must ship a small win using only the in-situ AI in their everyday tools (no external prompts allowed). The constraint sparks creativity and showcases practical value.

3. Share & Learn from Peers

   • Spin up a Slack or Teams channel for “AI-in-My-Tool” tips. People often discover keystroke-level shortcuts—like the /summarize command in Notion or the ✨ icon that appears when you highlight text in Confluence.
   • Found a killer Zapier automation that pipes Claude’s output back into Jira? Post the snippet. Collective experimentation is the fastest way to level everybody up.

4. Stay Curious & Open-Minded

   • Yesterday’s beta tag becomes today’s must-have feature. Keep an eye on release notes—your favorite app may quietly add an AI co-pilot overnight. Test, tweak, repeat.

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5. Practical Tips for Daily In-Situ Use

• Spot the Sparkles: Most tools denote AI actions with a ✨ icon or magic menu. Hover everywhere—you’ll find surprise helpers.
• Frame Clear Tasks: Whether it’s a Copilot side panel or a cell formula, give context (“summarize for exec”, “rewrite in friendly tone”) and set output length.
• Verify, Then Trust: In-app AI pulls live data, but hallucinations still happen. Sanity-check figures and links before you publish.
• Iterate in Place: Highlight → refine → regenerate. Because AI lives inside the doc, you can loop quickly without copy-pasting.
• Mind the Data: Embedded AI inherits your tool’s permissions. If a doc is confidential, stay in-situ—don’t paste content into a public chatbot.

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6. Making the Most of This Transition

1. Adopt a Growth Mindset

   • The technology will evolve, but so can you. Treat this transition as an opportunity to learn invaluable skills that apply in your current role and in future career moves.

2. Experiment Without Fear

   • Try small pilot projects. You don’t have to overhaul an entire department overnight. Sometimes it’s as simple as using ChatGPT to draft your next team memo or to analyze a short dataset.

3. Collaborate with AI, Don’t Just Automate

   • View AI as your colleague that does the repetitive, time-consuming work, so you can focus on strategy, creativity, and problem-solving.

4. Stay Aligned with Company Goals

   • Whether your focus is cost reduction, revenue growth, or brand building, ensure your AI explorations align with the organization’s broader mission and objectives. This is how you’ll gain buy-in and budget support for AI-driven projects.

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Conclusion: Building a Future-Proof Skillset

As multiple CEOs across tech and beyond are stating in plain language: AI is not optional. This doesn’t mean doom and gloom; it means empowerment—if you seize the opportunity to learn, adapt, and evolve.

• Start small: Incorporate AI into one part of your workflow.
• Stay curious: The pace of AI innovation is staggering. Keep an eye out for new tools and updates.
• Share successes: When you find something that saves you time or money, share it with your peers. This fosters a culture of continuous improvement.

Ultimately, the nature of work is shifting toward augmenting human potential with AI. Whether you’re in HR, finance, product, engineering, sales, or marketing, the mandate from leadership is clear: leverage AI or risk falling behind. Embrace this moment to become more efficient, creative, and impactful in your role. As Tobi Lütke summed it up: “We’re all in on this.”

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Get Connected, Share, and Other Socials

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Key Takeaways & Next Steps

• AI is now mainstream and every department can (and should) use it.
• Experimentation is vital—start with small tasks, then scale up.
• Continuous learning (lunch-and-learns, peer sharing) keeps everyone’s skills fresh.
• Performance & evaluations may soon hinge on AI fluency, so stay proactive.
• Aim to collaborate with AI rather than seeing it as a threat—doing so can supercharge your productivity and open new paths for career growth.

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Sources

[1] SAP SuccessFactors Accelerates AI Capabilities - https://news.sap.com/2024/06/sap-successfactors-accelerates-ai-capabilities/

[2] Workday Rising 2024: AI Illuminate, AI agents, Evisort acquisition - https://www.constellationr.com/blog-news/insights/workday-rising-2024-ai-illuminate-ai-agents-evisort-acquisition

[3] SAP Business AI: Release Highlights Q4 2024 - https://news.sap.com/2025/01/sap-business-ai-q4-2024-release-highlights/

[4] GitHub Copilot · Your AI pair programmer - https://github.com/features/copilot

[5] Atlassian Intelligence features in Confluence - https://support.atlassian.com/organization-administration/docs/atlassian-intelligence-features-in-confluence/

[6] Meet the new Notion AI - https://www.notion.com/product/ai

[7] What Is Einstein Copilot? - https://www.salesforce.com/agentforce/einstein-copilot/

[8] Meet Magic Studio | Canva’s AI Tools - https://www.canva.com/magic/

[9] Meet the new Notion AI - https://www.notion.com/product/ai

[10] ServiceNow injects more generative AI capabilities into its workflow platform - https://siliconangle.com/2024/03/20/servicenow-injects-generative-ai-capabilities-workflow-platform-washington-dc-release/

[11] Meet Breeze — HubSpot’s AI tools - https://www.hubspot.com/products/artificial-intelligence

[12] The future content creation and production with generative AI - https://blog.adobe.com/en/publish/2024/12/11/the-future-content-creation-production-with-generative-ai

[13] Peakforce – Atlassian Intelligence (AI) - Everything you need to know - https://www.peakforce.dev/blog/atlassian-intelligence-ai---everything-you-need-to-know

[14] Generative AI - ServiceNow - https://www.servicenow.com/now-platform/generative-ai.html

[15] SAP Business AI: Q2 2024 Release - https://news.sap.com/2024/07/sap-business-ai-q2-release/