.jpg?table=block&id=230c53fc-5bce-8063-9a51-f86a5fcf2b7c&cache=v2)
AI in Enterprise: Practical Ways Large-Scale Businesses Are Adopting AI
AI in enterprise isn’t the future, it’s the present. Discover how intelligent tools are driving growth and informed decision-making across various industries.
Written by
Published on
Do not index
Do not index
Imagine you’re a sales manager looking to help your team improve their performance. You want to get them access to the best sales coaching tools available. But when you start to explore your options, you’re overwhelmed with the sheer volume of choices and realize that most of them have little to no information available on their websites. What you really want is a tool that can quickly assess your team’s individual strengths and weaknesses, and then use AI to personalize coaching for each rep to get them up to speed. This is precisely what artificial intelligence can do for sales coaching tools. In this article, we’ll explore AI in enterprise settings, looking specifically at how large-scale organizations are adopting AI and what this means for the future of business.
Aomni’s GTM automation tool is a valuable resource to help you achieve your goals, such as learning about AI in enterprise: practical ways large-scale businesses are adopting AI. This sales coaching tool uses AI to analyze your team’s performance and quickly uncover individual strengths and weaknesses. From there, it creates personalized coaching modules to help improve underperforming reps.
The Rise of AI in Enterprise Operations
Artificial intelligence is no longer a futuristic concept; it is a reality. It’s now a critical business tool for enterprises seeking to operate smarter, faster, and more competitively. Enterprise AI refers to the application of artificial intelligence technologies at scale within large organizations, such as:
- Machine learning
- Natural language processing
- Computer vision
It’s not just about isolated use cases or experimental pilots. It’s about embedding AI across functions to drive efficiency, automate complex processes, and deliver deeper insights from vast datasets.
Why Businesses Are Adopting AI at Record Rates
AI adoption is accelerating for a few clear reasons:
- The explosion of data from digital platforms, IoT, and enterprise systems has created both a challenge and an opportunity. AI offers the only scalable method for processing and extracting value from this data in real-time.
- Competitive pressure is intense; companies that don’t adapt risk falling behind as their peers utilize AI to:
- Reduce costs
- Personalize customer experiences
- Launch products more quickly
- Customer expectations have evolved. People now demand intelligent, seamless interactions, and AI helps businesses meet that standard at scale.
The State of AI in Business
According to McKinsey’s 2024 survey, 78% of organizations now use AI in at least one area of their operations, up from 55% in 2023. The message is clear: AI is becoming foundational, not optional.
Companies that embrace enterprise AI today are gaining a strategic edge, improving operational efficiency, enhancing productivity, and setting the pace for innovation in their industries. As AI technologies continue to mature, early adopters will be best positioned to lead in a fast-moving, high-expectation business environment.
Core Areas Where Enterprises Are Using AI
.jpg?table=block&id=230c53fc-5bce-8012-aef4-e1cc3fc54918&cache=v2)
Customer Experience: AI Delivers Seamless Interactions at Scale
Today’s consumers expect fast, seamless, and personalized interactions, and AI is how enterprises deliver at scale. Chatbots, voice AI, and intelligent routing systems allow companies to support customers 24/7, reducing wait times and operating costs. AI also powers personalized recommendations, which improve upsell rates and customer satisfaction.
Case in point: Hilton launched an AI chatbot called Xiao Xi to enhance customer service. Available 24/7, it offers travel tips, booking assistance, and personalized hotel recommendations.
The result? A 94% customer satisfaction rate, $1 million in annual cost savings, and a rise in direct digital bookings.
Operations and Supply Chain: AI Optimizes Logistics and Supply Chain Management
AI empowers enterprises to optimize logistics and manage supply chains with unparalleled precision. Effective demand forecasting, inventory optimization, and real-time route planning are essential for global companies navigating rising costs and evolving customer expectations.
Example: Walmart developed an AI system that slashed logistics costs by optimizing delivery routes, saving 30 million unnecessary miles and more than $900 million, while speeding up deliveries to stores across the U.S.
HR and Workforce Management: AI Improves Employee Experience and Business Outcomes
Enterprise HR teams are turning to AI for better decision-making and workforce planning. AI helps with candidate screening, employee sentiment analysis, and productivity insights, allowing HR teams to:
- Hire smarter
- Retain top talent
- Boost employee engagement
AI-powered tools can also identify burnout risks, suggest learning opportunities, and uncover hidden talent within the organization, all through real-time data analysis.
Finance and Risk Management: AI Moves Companies From Reactive to Predictive Operations
Finance departments are using AI to move from reactive to predictive operations. Key applications include fraud detection, credit scoring, automated reporting, and financial forecasting. AI can identify anomalies faster than human teams and help reduce costly errors.
Real-world example: Microsoft developed an internal AI tool called FINN, which increased revenue forecast accuracy to 99%. In Tokyo, it reduced a 60-person finance team down to just two, freeing up talent for more strategic, high-value work.
Sales and Marketing: AI Gives Enterprises a Competitive Edge
In the enterprise sales cycle, knowing who to reach and when is everything, and AI gives marketers that edge. It powers predictive analytics, lead scoring, and content personalization based on user behavior and historical trends.
Example: L’Oréal uses generative AI to power a personalized beauty assistant. Customers can try products virtually, receive customized recommendations, and chat with an AI advisor, all while boosting engagement, conversions, and loyalty.
AI-Powered GTM: Automating Sales Research and Outreach at Scale
Your competitors are closing deals while you're still searching for prospects on Google. Aomni's AI agents, which generate automatic responses, work 24/7 to:
- Research accounts
- Track buying signals
- Craft hyper-personalized outreach
Join enterprise sales teams at Oracle, AMD, and NVIDIA, who have already 10x'd their performance with AI-powered GTM automation. Stop being the bottleneck in your own sales process. Get started with Aomni and let AI handle the research while you close the deals. Close more deals with our GTM automation tool.
Related Reading
Benefits of AI for Enterprise-Level Businesses
Enterprise AI is no longer just about cutting-edge innovation—it’s become a core driver of operational success and competitive advantage. As companies scale, the complexity of their operations increases exponentially.
That’s where AI steps in across the organisation:
- Automating processes
- Uncovering insights
- Enabling more intelligent, faster decisions
The Strategic and Financial Impact of AI Across Industries
According to Gartner, 79% of corporate strategists agree that AI and analytics will be critical to their organisation’s success within the next two years. And in sectors like financial services, the savings are massive.
Marsh & McLennan estimates that AI could save the industry over $1 trillion by:
- Streamlining operations
- Reducing fraud
- Automating workflows
Here’s how AI delivers measurable value for enterprise-level businesses:
Efficiency and Automation: Streamlining Processes to Save Time and Money
AI excels at automating repetitive, rules-based tasks. Everything from data entry to invoice processing to customer support. This reduces operational costs, minimizes human error, and frees up talent to focus on strategic work.
Whether it’s routing service requests through AI-powered chatbots or using intelligent document processing to handle contracts, AI creates systems that work 24/7, at scale, without fatigue.
Scalability: Growing Your Business Without Proportionally Increasing Costs
As enterprises grow, so do their data and system demands. Unlike human teams, AI systems can scale effortlessly to handle increasing workloads. From processing millions of customer transactions to analyzing real-time supply chain data, AI allows businesses to expand without proportionally increasing headcount or infrastructure costs.
This scalability is essential for global enterprises managing large datasets across time zones and departments.
Data-Driven Decision Making: Replacing Guts with Data
AI transforms raw data into actionable insights. With the ability to ingest structured and unstructured data from dozens of sources, AI tools help leaders:
- Forecast trends
- Identify risks
- Personalize customer experiences
The shift is from gut-feel decisions to data-informed strategies, a game changer for everything from inventory planning to product development.
Competitive Advantage: Outpacing Rivals in Both Efficiency and Innovation
Enterprises that implement AI well aren’t just more efficient; they’re also more innovative. AI accelerates product development cycles, facilitates rapid testing, and enables businesses to adapt to changing market conditions more quickly than their competitors.
Whether it's using AI to predict customer churn, generate dynamic pricing models, or optimize media spend, companies that integrate AI deeply can outpace their rivals in both performance and market responsiveness.
Related Reading
- GTM AI
- Marketing AI Companies
Challenges Enterprises Face When Implementing AI
.jpg?table=block&id=230c53fc-5bce-80e4-b46a-e9dae3d6e26b&cache=v2)
Fear of the Unknown: Employees Prefer Familiar Workflows
Employees often prefer familiar workflows and may see new systems as a threat to their skills and job security. Addressing this challenge involves providing clear and relatable explanations that demonstrate how new tools can support both personal growth and the organization's success.
Lack of Training: New Tools Require Ongoing Support
Introducing a new system without sufficient support can leave employees feeling overwhelmed and more likely to revert to old methods. Offering personalized, ongoing training provides them with the skills and confidence they need to use the new tools successfully.
Strategic Misalignment: New Tools Must Align with Business Goals
When new tools or processes aren’t connected to broader organizational goals, they can seem like random changes instead of useful improvements. Teams need to see how these tools support larger business objectives to understand their true value.
Insufficient Leadership Support: Change Must Be a Priority
If leaders aren’t actively involved, team members may view new initiatives as unimportant. When top management stays engaged, it sends a message that the change is essential and worth the effort.
Complexity: Simplifying AI Implementation Is Critical
A complicated implementation process can make people reluctant to use new tools. Simplifying tools, improving their functionality, and ensuring they integrate seamlessly into current workflows can help teams adapt more easily.
Best Practices for Successful Enterprise AI Adoption
Map Out Your AI Strategy Before Getting Started
A well-defined AI strategy is the foundation of successful AI adoption. This strategy should align AI initiatives with the organization’s overall business objectives and governance frameworks.
Start by identifying key areas where AI can have the most significant impact, such as operational efficiency, customer experience, or innovation. Develop a roadmap that outlines the steps for implementing AI, from pilot projects to full-scale deployment.
Start Small With Pilot Projects
Rather than deploying AI across the entire organization from the outset, it’s advisable to start with pilot projects. These smaller, controlled projects enable organizations to test AI applications, assess their effectiveness, and gather valuable insights before scaling them up.
Successful pilots can then be expanded to other parts of the organization. This approach reduces risk and allows for adjustments based on real-world performance.
Invest in AI Skills and Training
The shortage of AI talent is a well-documented challenge, making it essential for organizations to invest in developing the skills needed for AI adoption. This can be achieved through a combination of hiring, training, and upskilling existing employees.
Organizations should foster a culture of continuous learning, where employees are encouraged to develop their AI and data science skills. By partnering with educational institutions and offering internships or training programs, organizations can help bridge the talent gap.
Implement Strong Data Governance
Effective AI adoption relies on high-quality data. Implementing strong data governance practices ensures that the data used by AI systems is accurate, secure, and compliant with regulations.
This involves establishing clear policies for data management, including how data is:
- Collected
- Stored
- Accessed
- Shared
Data governance frameworks should also address privacy concerns and ensure that AI systems are transparent and accountable.
Monitor Performance and Optimize AI Systems
AI adoption is not a one-time effort; it requires ongoing monitoring and optimization. Once AI systems are in place, organizations must regularly evaluate their performance to ensure they are delivering the desired outcomes.
This involves tracking:
- Key performance indicators (KPIs)
- Assessing the impact of AI on business goals
- Making necessary adjustments to improve effectiveness
Continuous monitoring also helps identify and address any ethical or compliance issues that may arise.
Close More Deals with Our GTM Automation Tool
Aomni's AI agents work 24/7 to:
- Research accounts
- Track buying signals
- Craft hyper-personalized outreach that generates automatic responses
Their research helps sales reps overcome being the bottleneck in their own sales process, enabling them to close more deals faster.
Aomni plays nicely with your existing sales tools to make GTM research more efficient. For example, if your team uses HubSpot, Aomni can help you identify and research the right prospects, create personalized outreach emails, and even draft responses to inbound leads, all within the HubSpot platform.
Related Reading
- AI Competitor Analysis
- AI-Driven Innovation
Written by
.jpg?table=block&id=b4eb7979-5182-4236-acb9-17a6a545395c&cache=v2)