As a tech writer, I've had the opportunity to sit down with executives and decision-makers at massive enterprises. We're talking sprawling international organizations with thousands of employees and wildly complex tech stacks that keep everything humming. And during those conversations, I've been able to learn a lot about how these behemoths use technology internally to optimize their operations.
Obviously, the topic of AI has come up a lot, especially in the past two to three years—and it's easy to see why.
Enterprise AI uses machine learning and natural language processing to unlock new efficiencies, empower your team members to focus on what matters most, create amazing products, and deliver the best possible results for your stakeholders.
But here's the thing many miss: you can't just sprinkle AI into your organization and expect miracles.
In this article, I'll pull back the curtain and explain what enterprise AI is, share some examples of how to use it, and, most importantly, outline how enterprises can integrate AI into their operations effectively.
Table of contents:
What is enterprise AI?
Enterprise AI refers to the use of advanced artificial intelligence (AI) tech within large-scale businesses. It's designed to tackle complex challenges uniquely felt by big enterprises. This includes things like analyzing massive datasets, automating intricate multi-departmental processes, and making data-driven decisions that impact the entire organization.
Here's a breakdown of some of its key applications:
Data insights: Enterprise AI can sift through mountains of information from various sources, uncovering hidden patterns, trends, and connections that would take humans months or even years to identify.
Process automation: AI excels at taking on repetitive, time-consuming tasks. This frees up your team to focus on higher-level work and reduces the chance of human error.
Predictive analytics: By analyzing past performance, customer behavior, and market data, enterprise AI can help you forecast likely outcomes and potential risks.
Personalization: Ever wished you could deliver tailored customer experiences at scale? Enterprise AI can help you understand individual users and provide highly relevant recommendations, offers, and support.
7 examples of enterprise AI
Let's take a look at how these AI technologies are being used in the real world to transform operations, boost efficiency, and drive better outcomes.
1. AI for operations
Anyone who manages IT in a large enterprise knows it's like playing a never-ending game of whack-a-mole. Problems pop up, systems slow down, and your teams scramble to keep everything running. Artificial intelligence for IT operations (AIOps) is like having a lightning-fast assistant who can spot those issues before they escalate—and often even fix them automatically.
Here's a breakdown of how it works at a high level:
Smarter data analysis: AIOps platforms pull together vast amounts of IT data that would overwhelm a human team. They use machine learning to spot patterns that indicate potential problems and even predict outages before they happen.
Automation: Repetitive tasks that drain your engineers' time? AIOps can automate them. This could range from routine maintenance to resolving common incidents.
Reducing the noise: Incident alerts flooding your system? AIOps can consolidate them, prioritize issues, and even suggest solutions, so your team focuses on the real problems, not the false alarms.
Instead of simply fighting fires, AIOps can help your IT team avoid them altogether, meaning less downtime and a smoother-running infrastructure. This translates to a more stable business environment for everyone who relies on your systems. Plus, it can free up your IT staff to focus on building cool stuff (like new Zapier workflows).
But AIOps isn't a magic bullet. It requires careful planning, starting with identifying your biggest IT pain points and choosing a solution that aligns with your specific needs. As AI continues to evolve, AIOps will become an essential tool for ensuring a robust and adaptable IT infrastructure.
2. AI for customer service
Customer service in a large enterprise can be a lot. It involves fielding endless calls, emails, chats, and other support inquiries, sometimes across multiple languages and time zones.
AI for customer service can reduce some of that hassle by answering common questions and issues without a human ever getting involved, empowering your agents to deliver exceptional experiences for more complex cases.
Here are just some of the things AI can offer for customer service:
24/7 availability: AI-powered tools like chatbots never need a break. They answer basic questions and solve simple problems even outside office hours. This boosts customer satisfaction and reduces the number of inquiries your staff needs to manage during peak hours.
Faster resolutions: AI can analyze customer data to automatically route tickets to the right team and even suggest resources to agents, saving them time on research. This means faster solutions for your customers and improved productivity for your support team.
Personalization at scale: Modern AI tools allow enterprises to go beyond basic FAQs. Using customer history and data insights, AI can provide tailored product recommendations, offer more relevant solutions, and create a personalized experience that increases your customer loyalty.
Data-driven insights: AI turns every customer interaction into valuable data. By analyzing things like sentiment and common inquiries, you can spot trends, proactively address recurring issues, and even gain valuable insights into your product development process.
AI is powerful, but if you've ever tried having a conversation with Siri, you're probably already aware that it's not quite ready to take over our jobs just yet. The best strategies combine AI's ability to handle routine with human empathy and problem-solving for those complex issues AI isn't yet equipped to tackle.
3. AI for marketing
In a crowded marketplace, marketing success means cutting through the noise to truly connect with your audience. AI is a powerful tool for marketers looking to achieve that level of personalization on an enterprise level.
What does that actually mean?
Smarter segmentation: AI excels at analyzing vast amounts of customer data to understand your audience on a deeper level. This goes beyond basic demographics, empowering you to target campaigns and messaging with laser precision.
Creative brainstorming: AI can generate headline ideas, suggest relevant visuals, and even help with short-form content. This boosts your team's efficiency and sparks new avenues for creative messaging.
SEO insights: AI SEO tools like seoClarity can analyze search trends and competitor strategies and can even predict potential market shifts, helping you optimize your content and boost your organic ranking.
Trend analysis: AI helps you move beyond gut feelings by analyzing past performance, market trends, and customer behavior. This gives you actionable insights to optimize budget allocation and focus creative efforts where they'll have the most impact.
Data quality is crucial for getting the most out of AI. Focus on clean, well-organized customer data, and always keep data privacy top of mind.
4. AI for market research
Traditional market research can feel like finding a needle in a haystack. You need those insights, but where do you even start digging? AI can digest data fast, rapidly uncovering insights that would take human teams weeks to find. And its usefulness doesn't stop with data. Generative AI like ChatGPT can even help with more abstract market research.
Here are some ways AI can turbo-charge your market research:
Validate ideas quickly: Need to gauge the market potential for a new product or campaign? AI can analyze market trends, competitor strategies, and audience sentiment, providing insights to help you make confident decisions faster.
Develop detailed buyer personas: Truly understanding your target customer is a game-changer. AI can generate in-depth buyer personas, outlining their demographics, pain points, and motivations. Want to take it a step further? Use Zapier to create a chatbot that interacts with potential customers, collecting data you can then use to refine these buyer personas and make your marketing efforts even more targeted.
Know your competition: AI can rapidly compile competitor data, giving you a clear understanding of their strengths, weaknesses, and market positioning. This empowers you to identify potential gaps to fill and opportunities for differentiation.
Find hidden opportunities: AI can surface market trends, audience segments, or untapped demand your team might not have considered, sparking innovation and fueling new growth strategies.
Harness the power of predictive analysis: AI tools can do more than analyze the past. Predictive analytic platforms can help you forecast future trends and potential customer behavior to make proactive, data-driven decisions.
Remember, AI can still be kind of…not intelligent. With generative AI specifically, the quality of your output depends on the quality of your input. You really need to hold its hand to get decent answers:
Ask specific questions: The more detailed your prompts, the more relevant insights you'll get.
Integrate with other data sources: AI still struggles to gather data on its own, but sharing your own research with the tool can help extract valuable insights you may have overlooked otherwise.
Always double-check its output: AI is powerful but not perfect. It's been known to share outdated info, hallucinate, and even create data that isn't based in reality. Make sure a human always has the final say.
AI streamlines the initial stages of market research, empowering your team to gain crucial insights quickly. This leads to faster decision-making, optimized campaigns, and a deeper understanding of your market, giving your enterprise a significant competitive edge.
5. AI for HR
HR departments in large enterprises face a unique challenge—they handle a massive volume of tasks while also needing to focus on the strategic, human side of building and managing a workforce. AI is rapidly becoming an essential tool to automate processes and empower HR teams to focus on what really matters: their people.
Here's how AI is transforming HR:
Eliminating the paper chase: AI can automate tasks like managing employee records, processing payroll, and even initial resume screening. This frees up your HR team to focus on developing innovative recruitment efforts and employee retention strategies.
Data-driven people management: AI can analyze everything from performance reviews to employee surveys, uncovering insights into engagement, productivity, and potential areas of improvement. This empowers HR to make proactive decisions based on data, not just hunches.
Personalized onboarding: Onboarding new employees can be time-consuming. AI can create personalized onboarding processes, ensuring new hires have the information and support they need for a smooth start.
Power up your employee management software: Many leading employee management platforms now incorporate AI features. These tools can streamline scheduling, improve internal communication, and even analyze employee sentiment.
Now, here's something that absolutely needs to be pointed out: AI can be a bit problematic. And in HR, you really need to stay ahead of that. Be aware of biases, understand how it's trained, and consider its limitations before making big decisions that have real, human implications.
6. AI engineering
In a large enterprise, every hour an engineer spends on mundane coding tasks is an hour lost for true innovation. AI transforms engineering workflows, empowering your team to build amazing things faster—without requiring everyone to become machine learning experts.
Here are a few ways AI can improve enterprise engineering:
Efficiency at scale: AI automates those repetitive tasks that bog down even the best engineers. Across a large team, this translates to saved time, faster iterations, and getting products to market sooner.
The AI knowledge boost: Need to expand your team's skillset quickly? AI tools bridge the gap, providing code examples, explanations, and solutions that speed up learning in key areas.
Clearer collaboration: For complex projects, understanding code written by others can be a bottleneck. AI helps break down logic, improving team-wide understanding and minimizing miscommunications.
Streamlined documentation: AI isn't just about code. It assists with technical writing, from catching typos to offering suggestions for clearer explanations. This makes knowledge transfer between teams and stakeholders smoother.
Empowering non-developers: AI gives non-developers the ability to write enough code to get simpler tasks done. It's another tool in the no-code belt.
While AI handles many logical tasks, the need for human creativity, problem-solving, and leadership in software engineering is as important as ever. Ensure your team understands how to use AI tools as collaborators, not replacements.
Enterprises that embrace AI tooling in engineering won't just build better products—they'll build a more adaptable, innovative workforce. It's about staying ahead in a rapidly changing tech landscape.
7. AI for logistics
AI is a logistical marvel. It offers powerful tools to streamline operations, cut costs, predict outcomes, and make data-driven decisions that impact your entire supply chain.
Here's how AI is completely changing enterprise logistics:
Route optimization for large fleets: AI goes far beyond consumer navigation apps. It analyzes vast amounts of real-time data on traffic, weather, and vehicle constraints to optimize routes for complex fleets. This saves fuel, reduces delivery time, and improves overall fleet utilization.
Demand forecasting at scale: Gone are the days of relying on past sales data alone. AI helps enterprises analyze market trends, competitor activity, and even external signals to accurately forecast demand. This aligns inventory levels with anticipated needs, preventing stock-outs and overstocking that tie up working capital.
Warehouse automation: AI optimizes warehouse operations with robotics and intelligent inventory management systems. This minimizes errors, frees up workers for more complex tasks, and increases throughput for faster order fulfillment.
Predictive maintenance for minimized downtime: Especially important for enterprises relying on specialized equipment, AI analyzes sensor data to predict potential failures. This allows for proactive maintenance, reducing unexpected downtime that causes supply chain disruptions and costly delays.
AI in logistics offers clear benefits for large-scale operations, including:
Significant cost reduction: Through optimized routing, smarter inventory management, and proactive maintenance, AI drives down logistical costs at every step.
Improved customer experience: Faster, more reliable deliveries become a competitive edge. For enterprises fulfilling their own eCommerce, this is a game-changer.
Building a resilient supply chain: AI tools empower enterprises with better forecasting and the ability to identify alternate routes or suppliers when disruptions occur. This minimizes costly delays and ensures goods keep flowing.
But remember that adopting AI for logistics has to align with your existing tech stack and broader business objectives. Investing in upskilling your workforce is also vital for a smooth transition.
How to implement AI across an enterprise
If there are only two things we should take away from Google’s AI Overview rollout, it's that you should not eat rocks even if Google tells you to. Also: AI won't fix all of your enterprise's problems.
Successful integration requires a strategic approach, clear communication, and a focus on aligning AI's power with your organization's specific needs. Think of it like creating a business plan, only you're pitching your dearest colleagues—some of whom have been pretty outspoken against AI.
To get the buy-in you need from the inevitable naysayer who refuses to embrace AI (looking at you, Dave), you want to create a structured proposal with a proof of concept and real-world examples while mapping AI solutions to tangible benefits for stakeholders.
1. Define your AI goals
Start by asking yourself, "why?" What are the biggest problems AI can help you solve? Maybe it's streamlining logistics to cut costs, providing hyper-personalized customer experiences, or making faster, data-driven decisions that impact your product roadmap.
Be specific, and focus on achievable objectives.
Next, think big-picture. How does this AI project support your enterprise's long-term vision? Connecting AI to those high-level goals will go a long way toward securing buy-in from key stakeholders.
Finally, manage expectations carefully. AI is powerful, but it can't fix everything. Set a realistic timeframe for results, and be prepared to iterate as you learn.
2. Consult internal teams
Those on the front lines of your enterprise operations have the best insight into where bottlenecks and inefficiencies occur. Actively involve departmental managers and employees in the conversation about where AI might offer solutions.
Don't forget to loop in your IT team early in the process. Ensuring that any AI solution aligns smoothly with existing infrastructure and security considerations is crucial.
3. Foster curiosity and experimentation
Beyond problem-solving, foster a culture of curiosity about AI across departments. Open discussions can alleviate worries about job displacement and generate excitement around innovation. Plus, it's your hands-on employees that will likely find the best uses for AI, and then you can build off that.
Zapier, for example, promotes a culture of experimentation. Employees are encouraged to build their own AI workflows, leading to innovative uses of AI that boost productivity and create a sense of ownership over new technology.
4. Log pain points or friction points AI can solve
It's time to get granular about the issues AI could address. Ask yourself:
How much time is your team losing to repetitive manual tasks?
Where is a lack of data-driven insights causing costly delays or missed opportunities?
Can you put a dollar figure on the impact of these issues?
Quantifying the problem makes the potential ROI of an AI solution crystal clear.
5. Find AI tools that solve outlined problems
There's no one-size-fits-all AI solution. Resist the urge to jump on the latest AI bandwagon and instead focus on tools that directly target the pain points you've identified.
When evaluating vendors, prioritize those with track records of success in your industry, and carefully consider how well potential solutions would integrate with your existing tech stack.
For highly specialized needs, be open to exploring custom AI solutions that can be tailored precisely to your workflows.
6. Create a realistic long-term budget
Think beyond that initial software purchase. Factor in the cost of implementation, training, preparing your data for AI use, ongoing maintenance, and potential future scaling.
Investing in AI education for your workforce is also a non-negotiable. This long-term investment will maximize the benefits you'll see over time.
7. Prove value to stakeholders
Data is your best friend here. Clearly define measurable KPIs to track before and after implementing AI—things like cost savings, efficiency gains, increased customer satisfaction, or faster time to market for new products. Real-world case studies are also incredibly powerful.
Research how similar enterprises in your industry have successfully used AI and share those results to build confidence. Finally, highlight the human element. AI should ultimately free up your team for more strategic, rewarding work.
8. Address stakeholder concerns with real-world examples
Listen, not everyone is eager to drink the AI Kool-Aid. Fears around job displacement are common, AI's accuracy is often called into question, and honestly, some people just don't like new things.
Emphasize that AI augments human capabilities and can lead to the creation of new, more skilled roles within an organization. Have a clear, transparent data governance plan to address privacy concerns, and consider AI solutions that provide clear explanations for their decisions. This builds trust and avoids the dreaded "black box" worries.
To give you an example of how it can benefit an organization, here's how Zapier uses AI.
9. Schedule rollout
You've defined your goals, addressed concerns, and everyone's excited (or at least cautiously optimistic) about AI's potential. Now it's time to make your AI plan a reality.
Here's how to approach your rollout:
Start small: Begin with pilot projects in well-defined areas with clear metrics to showcase AI's value and build confidence.
Consider change management: Involve stakeholders early and prioritize user training for a smooth transition.
Roll out AI in stages: Gradually expand successful pilots, celebrate wins, and address feedback as the plan gains traction.
Remember, a successful AI rollout prioritizes clear communication, focuses on solving real problems, and emphasizes the ongoing training and support your teams need.
What to look for in an enterprise AI platform
AI is changing the game, but just buying some fancy AI software won't instantly solve all your problems. You need to choose enterprise software that works with the processes you already have in place.
Here's what to look for.
Scalability: Can the solution scale as your enterprise and AI needs evolve? Look for platforms designed to handle increasingly complex operations and massive datasets.
Integrations: How easily does it integrate with your existing tech stack? Does it connect to Zapier? Prioritize tools that have APIs or pre-built connections to the software you already use to prevent data silos.
Security: Robust security and privacy features are non-negotiables. Make sure the platform aligns with all industry regulations and allows you to set clear data governance policies.
Ease of use: How will your teams actually interact with the software? Consider the various input methods to ensure ease of use, whether that's uploading data directly or connecting to existing tools and data warehouses.
Implementation: How quickly can you get it up and running and start seeing the benefits? Be realistic about implementation time and the learning curve involved.
Support: Vendor support is crucial, especially for enterprise-wide rollouts. Look for providers that offer personalized training, comprehensive documentation, and any implementation assistance needed for a smooth transition.
It's tempting to want all the bells and whistles, but start by identifying your most urgent pain points and seek AI solutions that target those specifically. You can always expand later, ensuring you get a positive ROI every step of the way.
Examples of enterprise AI software
It's unlikely you'll find a single tool that works across your organization. The good news is: a lot of the enterprise software you use now probably already has AI features built in, so you might already have access to AI without having to add another tool or shell out any more of your budget.
But if you're not sure where to start, here are some curated lists of enterprise AI software. Click on the links below to see Zapier's picks for the best software in each category.
General-purpose enterprise AI software
AI meeting assistants can record and transcribe meetings across your organization and then provide summaries, action items, and insights.
AI productivity tools, including AI chatbots, can help your employees experiment with AI and empower them to come up with new ways to streamline their work.
AI scheduling assistants help your team spend less time in their calendars and more time on the work that matters.
Sales and marketing-focused enterprise AI software
CRMs were some of the first apps to incorporate AI in some capacity—this includes everything from generative AI for cold outreach to AI lead scoring models.
Marketing automation software is also relying more and more on AI to optimize campaigns and streamline workflows.
AI sales assistants help your sales team spend less time on tasks that should be delegated to robots, so they can focus on the human side of closing the deal.
AI writing generators, AI grammar checkers, AI video generators, AI voice generators, and other generative AI tools can help employees across the enterprise brainstorm and create content together.
Pay-per-click tools are incorporating AI to help streamline advertising campaigns and optimize for ROI.
Form builders often include AI form building options, so your teams can create customer-facing forms for any purpose in minutes.
AI tools for social media management can suggest copy, create images, and schedule and optimize posts, all using AI.
SEO tools use AI to help brainstorm, create, and optimize content, so you can get more eyeballs on your website.
Engineering-focused enterprise AI software
Internal tool builders (and other AI app builders) allow your team to create tools to streamline their day-to-day work.
Data- and operations-focused enterprise AI software
ERP software is maybe the closest you'll get to an all-in-one software solution for your entire company. With most ERP solutions incorporating AI, it's a potential way to one-stop shop a lot of your AI enterprise needs.
Business intelligence software uses AI to analyze large amounts of data and give you insights into how your business is performing across the board.
Competitor analysis tools use AI to develop insights into what your competitors are doing right (and wrong), and how you might capitalize on that.
Predictive analytics software takes your enterprise's historical data (along with broader industry trends) and uses AI to help predict what things might look like down the road—and where you should focus your efforts.
HR-focused enterprise AI software
Applicant tracking systems (ATS) incorporate AI to help screen resumes, schedule interviews, and personalize onboarding.
Project management-focused enterprise AI software
Workspace apps (like Notion) give your team a central source of truth to keep everyone on the same page. Many of these apps now include generative AI and use AI to let you ask questions about your documentation.
Enterprise project management software uses AI to help you create tasks, summarize progress, develop reports, and anything else you need to do to keep cross-functional work on track. (And here are even more AI project management apps.
AI project management software is putting AI to work in the most useful ways. These aren't all enterprise apps, but they can all grow with you.
Customer support-focused enterprise AI software
Help desk software is leaning in hard to AI. Some tools, like Intercom, are even branding themselves as AI-first. It doesn't mean AI is replacing customer support teams—just that those teams can now move faster and help customers better with a little help from the robots.
Automating AI for large enterprises
Everyone could use more time to focus on what matters most, and Zapier is dedicated to making AI and automation accessible to everyone.
Zapier Central, an experimental AI workspace, combines the power of AI within an intuitive chat-like interface, empowering you to teach bots how to handle tasks across Zapier's massive app ecosystem. This unlocks new levels of efficiency and frees your team for more strategic work.
And with Zapier Enterprise, you'll be able to scale automation safely and effectively across your organization. With features tailored to IT oversight, dedicated support, and personalized onboarding, Zapier Enterprise bridges the gap between empowering your teams and safeguarding your enterprise with:
IT-approved automation for every team: Empower your people to solve their own problems without compromising on security or control.
Move fast without breaking things: Reduce IT bottlenecks, increase agility, and prevent costly manual workarounds.
Dedicated support: Ensure a smooth implementation and maximize the value your enterprise gets out of AI-powered automation.
Start your enterprise AI journey with confidence—learn how Zapier Enterprise can help you achieve your goals.
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