Enterprise AI: What It Is, How To Implement, Examples
The journey of artificial intelligence (AI) has been nothing short of impressive. What began in the 1950s as a theoretical concept about machines capable of thinking, has now exploded into a technology capable of creating personalized exercise plans, writing entire books, and calculating how to get humans to Mars.
AI algorithms have become increasingly powerful and effective, transforming how we do business. The technology has become a pivotal force impacting industries and organizations around the world. While the recent evolution of AI has been driven by advancements in data storage, faster data processing, and sophisticated data analytics, the variety of ways people apply AI has also played a role. With the explosion of ChatGPT, AI moved out of the realm of advanced technical applications and into a convenient tool that could be applied across many use cases.
At a high level, AI encompasses the idea of using machine algorithms to autonomously “think” like humans. When software can make decisions that typically require human judgment and expertise, it can be considered AI. But not all AI is the same, and different ways of deploying these algorithms and tools shift AI from a checkers opponent to an essential tool for a wide variety of jobs and roles.
Consider enterprise AI, for example. Enterprise AI uses advanced algorithms and tools to enhance decision-making processes in large businesses. Let’s examine how enterprise AI is used and where it will go.
What is enterprise AI?
Enterprise AI integrates advanced AI technologies within large organizations to enhance and streamline business functions. Enterprise AI systems gather data from multiple sources, analyze it, and respond intelligently. It can be used for routine tasks like data collection and analysis, along with more complex operations like automation, customer service, and risk management.
These dynamic tools focus on finding ways to help businesses of all sizes streamline operations and improve efficiency. They are changing how companies do business.
By harnessing the power of enterprise AI, your business will see significant improvements in the speed and accuracy of your business decisions. This affects everything from marketing and sales to profitability and worker productivity. And because enterprise AI is growing so rapidly, every enterprise looking to stay competitive is carefully evaluating its use.
Enterprise AI vs regular AI
The biggest difference between enterprise AI and regular AI is the scope and scale of how it is applied. Enterprise AI is specifically designed to work for high-value tasks at a large scale and is tailored to the complex and varied needs of large organizations.
For example, enterprise organizations use a lot of applications and create a ton of data. Enterprise AI is uniquely positioned to handle and analyze this extensive data. It is designed to scale and integrate with existing AI systems, like content management, BI, operations, HR, or marketing tools. Often, enterprise AI tools easily work together with these tools to pull in data, combine it with other sources, and build machine learning algorithms based on comprehensive, cross-functional data. This ensures that enterprise AI tools can scale and adapt to business needs by accessing and utilizing data from every facet of the organization.
In contrast, regular AI is usually developed for more specific, narrowly defined tasks or objectives. For example, it might specialize in solving a particular problem like reading and extracting data from documents, providing customer service through a chatbot, or performing image recognition. While useful for a specific context, it is not designed to integrate with multiple enterprise systems or scale across an organization.
By integrating with the tools and data from every business function, enterprise AI can deliver more precise insights, optimize workflows, and enhance overall business efficiency, providing a significant competitive advantage.
Why is artificial intelligence important for the enterprise?
Because enterprise AI is becoming increasingly advanced, it is crucial for enterprise organizations to utilize it in order to advance their business goals. In the past, traditional AI development occurred in silos, where data science teams built machine learning models to solve a specific problem or serve a single department’s needs. However, using AI this way doesn’t work for enterprise organizations that require effective cross-departmental coordination to solve complex problems that drive business growth.
Enterprise AI solves this challenge by integrating with tools from across departments and combining data to answer questions that support all levels of the organization. From executives to frontline workers, everyone can use AI to help drive their business decisions. This is called the democratization of AI, which ensures that the benefits of AI and the data from your applications are readily available for your employees, enabling them to make timely decisions.
The time is approaching when AI will no longer be an optional tool. Enterprises need AI to help them make the most of the large volumes of data they collect daily. AI can also bridge the gaps in technical knowledge that naturally exist since not every employee is an expert at handling data. It can automate processes and complete tasks in minutes that would normally take humans hours.
Implementing enterprise AI
To successfully implement enterprise AI, it is essential to integrate AI technology throughout various departments and job functions. This requires careful and strategic planning to determine how AI can be effectively used across the organization and various job functions, as well as identifying the specific benefits your company wants to realize by deploying enterprise AI.
Once there is clear understanding of the methods and objectives, you can develop a comprehensive strategic plan for implementing enterprise AI. Below is a overview of some of the key steps involved in adopting and deploying AI within an organization:
- Define organizational goals and objectives:
The first step is to clearly outline what your organization hopes to achieve with AI. Whether the goal is to improve operational efficiency, enhance customer experience, drive innovation, or increase revenue, understanding these objectives will guide your AI strategy. By establishing specific goals and metrics to measure success, you can ensure that the implementation aligns with the overall business objectives. - Develop a data strategy:
Your AI algorithms are only as good as your data, so a solid data strategy is critical. Your AI systems will rely heavily on data for training and operational insights. Look at the current state of your organization’s data, including its availability, quality, and accessibility. Identify where you’re pulling data from and determine the data quality. Think about how you will connect, store, manage, and protect data. - Build a cross-functional team:
Implementing enterprise AI requires a blend of skills from across departments and job levels. Put together a cross-functional team that includes data scientists, AI experts, department specialists, and IT professionals. This collaborative approach ensures that different perspectives and expertise are combined to address the project and ensure enterprise AI is deployed in a way that benefits job functions. - Develop a comprehensive plan:
As your team gathers information from across the organization, develop a plan that encompasses the technical and business aspects of implementing enterprise AI. This involves selecting the right AI technologies and tools, defining the scope of AI projects, and allocating resources. The plan should be flexible enough to accommodate changes and scalable to support your company’s growth. It should also include steps for model development, testing, maintenance, and continuous improvement. - Maintain and monitor AI systems:
Once your team has gathered input from various job functions and created a plan for implementing AI, it’s time to deploy,maintain, and monitor your enterprise AI systems. Ongoing maintenance will be crucial to ensuring that your AI systems remain effective, secure, and aligned with your evolving business needs. Maintenance includes regular updates, performance monitoring, and making adjustments based on new data or changing requirements.
Your enterprise AI implementation will be a multifaceted process that continually needs to be reevaluated to ensure it meets your strategic goals and benefits the company.
Examples and use cases of enterprise AI
Like regular AI, enterprise AI has applications across a wide variety of industries, solving business problems that cover every aspect of your business. By using enterprise AI, companies can benefit by automating processes, gaining decision-ready insights from their data, and adapting quickly to changing markets. Here are some examples of how companies can use enterprise AI across different business sectors:
Healthcare
Healthcare companies use enterprise AI and data to improve patient care. By deploying enterprise AI to gather and combine data across all departments in a healthcare organization, teams can look at how they’re operating, understand how patients are responding to care, identify and resolve bottlenecks, and use all that information to develop and grow business plans built on the most efficient workflows.
By using enterprise AI to analyze real-time data and help teams adapt messaging for disease prevention programs or post-care data, healthcare organizations can stay ahead of potential problems and focus on the human side of healthcare. Additionally, AI-driven chatbots and virtual assistants provide patients with immediate access to medical information and support, improving overall healthcare accessibility and efficiency.
Financial services
For many verticals in the financial services industry, detecting fraud and managing risk while still delivering successful and personalized financial services are top priorities. Given the sensitivity of financial data and need for strict privacy controls, many financial services organizations struggle to get a holistic view of information across their operations. Utilizing enterprise AI addresses this by keeping data safe while surfacing insights that help identify fraud or provide personalized services for customers.
By analyzing large volumes of financial data, enterprise AI can identify fraudulent activities, assess credit risks, and detect market trends. This allows companies to better manage their risks and focus on providing top quality service for clients, who benefit from real-time insights on market movements, deep historical dives on trends, and predictive forecasting.
Manufacturing
Manufacturing companies can use enterprise AI across operations to optimize many aspects of their operations. This includes improving product schedules, setting more accurate targets, identifying trends, efficiently managing inventory, ensuring timely shipments, and evaluating new opportunities or markets. Plus, AI can be employed for predictive maintenance, quality control, and supply chain optimization. By integrating enterprise AI with IoT sensors that monitor equipment performance and detect anomalies, companies can proactively conduct maintenance to prevent costly downtime and streamline workflows.
Marketing and advertising
Marketing and advertising agencies can use enterprise AI to optimize marketing strategies, enhance customer targeting, and drive more revenue. As companies focus on delivering personalized insights, enterprise AI can help teams act on real-time data and respond immediately to shifting trends.
By quickly sifting through massive amounts of raw and unstructured data, enterprise AI can analyze intent, giving marketers insights that would normally take hours of manual work to understand. Companies can use this real-time analysis to personalize customer experiences and improve ROI. Predictive AI models can more accurately forecast customer behavior to identify high-value market segments, enabling marketers to optimize advertising spend and allocate resources more effectively.
Future of enterprise AI
Soon, enterprise AI won’t be an optional tool for your large organization—it will be a necessity. The complexity and scale of businesses’ internal and customer data will make it impossible to develop quality insights in a timely manner without the help of artificial intelligence. Smart enterprises are using AI now to propel them to success in the future.
Here are some ways the future of enterprise AI could unfold:
- Integration with emerging technologies
Enterprise AI will increasingly integrate with emerging technologies such as edge computing, blockchain, and the Internet of Things (IoT). This convergence of technologies will enable organizations to use real-time AI insights, improve decision-making, and enhance operational efficiency across a variety of use cases, many of which we likely cannot even envision yet. - Advanced automation and autonomous systems
The future of enterprise AI will see a proliferation of advanced automation and autonomous systems powered by AI algorithms. These systems will automate complex tasks, optimize processes, and adapt to dynamic environments with minimal human intervention. Whether it’s autonomous vehicles in transportation or smart factories in manufacturing, AI-driven automation will revolutionize industry landscapes. - Enhanced personalization and customer experience
Enterprise AI will drive enhanced personalization and customer experience across various industries. AI algorithms will analyze vast amounts of customer data to deliver hyper-targeted marketing messages, personalized product recommendations, and tailored services. This level of personalization will not only improve customer satisfaction but also drive customer loyalty and retention. - Ethical AI and responsible use
As AI technologies become more pervasive in enterprise settings, increased focus will be on ethical and responsible use of AI. Organizations will prioritize transparency, fairness, and accountability in AI-driven decision-making to mitigate biases and ensure ethical outcomes. This will involve implementing robust governance frameworks, ethical guidelines, and regulatory compliance measures to uphold trust and integrity in AI systems. - Collaboration between humans and machines
The future of enterprise AI will be characterized by collaboration between humans and machines, where AI augments human capabilities rather than replacing them. This symbiotic relationship between humans and machines will enable organizations to harness the strengths of both to achieve greater productivity, innovation, and competitive advantage. People working together with AI will empower employees to focus on more valuable tasks, while AI handles routine and repetitive functions. - Continuous innovation and adaptation
Enterprise AI will undergo continuous innovation and adaptation to meet evolving business needs and technological advancements. Organizations will invest in research and development to push the boundaries of AI capabilities, develop new algorithms, and explore novel applications across industries. This relentless pursuit of innovation will drive organizational agility, resilience, and competitiveness in an ever-changing business landscape.
As AI becomes more and more essential for enterprise operations, forward-looking companies embracing enterprise AI now will be ready to set themselves up for success in the future.