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Enterprise artificial intelligence is an approach that combines several different branches of AI, including machine learning (ML), natural language processing (NLP) and deep learning to improve efficiency and innovation at large enterprises. Unlike many dedicated enterprise tools which only solve one problem, the flexibility of the large language models (LLMs) which power AI means that these tools can assist with nearly any challenge an enterprise might face.
In this article, we’ll take a look at some of the key benefits of enterprise AI and help you come up with an adoption strategy which suits your organization’s needs.
Enterprise AI is the application of AI processes to highly complex business operations. Instead of an AI tool dedicated to a single user like ChatGPT, enterprise AI integrates with your whole organization, using techniques like deep learning to understand and assist your team members with many different use cases. As it learns about your organization’s products and structure, it can help automate repetitive tasks and can scale with you as your business grows.
For example, organizations can use enterprise AI to automate customer support processes, provide software developers with coding assistants and predict market changes. It can eliminate data siloing, helping each team collaborate better across the organization. You can also use enterprise AI to automate time-consuming, repetitive tasks, increasing focus on mission-critical operations and creative work.
Enterprise AI offers tremendous flexibility, learning from your data and integrating seamlessly into core business functions. Enterprise AI can also automatically optimize complex operations, testing possible solutions and analyzing historical data to help you plan for future changes.
Implementing enterprise AI can impact the business across a wide range of operations. Even seemingly small improvements, such as the number of hours each person on a team saves, or automated customer support, can add up significantly as you scale, giving you an advantage over any competitors who still rely on manual HR, support, sales and product development techniques.
Because it has such a diverse array of use cases, it’s helpful to break down some of the most popular and useful types of enterprise AI tools currently available:
Combining machine learning, statistical analysis and large datasets, predictive analytics is an enterprise AI tool that assesses the likelihood of a particular outcome. You can deploy this function throughout your organization, helping you to, for example, predict product inventory needs and give your infrastructure team early warnings about possible service incidents.
Enterprise AI tools are capable of understanding and responding effectively to different types of human communication, including written and verbal questions and commands. This makes AI enterprise tools useful to everyone, including non-technical users, since they can communicate with users in a way that feels natural and accessible. In addition, NLP allows AI tools to create large amounts of useful written or audio content on command, no matter the desired format, tone or subject matter.
Similar to NLP, computer vision allows AI tools to ingest and derive insight from images and other visual inputs. Computer vision tools can recognize what’s in an image and then make decisions based on that information. Organizations have used these tools for everything from content moderation to self-driving vehicles.
Depending on their products and structure, each organization will have slightly different uses for enterprise AI, but most can benefit from one, if not all, of the following:
Modern supply chains are vulnerable to delays caused by everything from weather patterns to international tax policy. You can use enterprise AI tools to track all of these factors, automatically flagging risks and identifying opportunities for improvement. Enterprise AI can also analyze historical data in order to predict future risks, such as fluctuating fuel costs or surges in demand.
AI-powered fraud detection can be both reactive and predictive, identifying suspicious behavior in real time and also analyzing data patterns to prevent fraudulent activity before it happens. Additionally, you can use AI tools to make internal assessments, uncovering poor credential management or other security risks within your organization.
By tracking workloads and measuring equipment performance over time, AI can identify when a piece of equipment is going to need repair or replacement. This allows you to plan ahead and proactively solve issues before they cause slowdowns or create safety risks for equipment operators.
Marketing and sales teams can use enterprise AI to build dynamic, reactive campaigns that adapt in real time, allocating resources to the most performant channels automatically. They can use AI’s NLP capabilities to query public and proprietary databases, requesting information about prospects or leads to create relevant marketing assets and sales pitches which address customer pain points.
Enterprise AI tools are transformative in the purest sense, offering efficiency and flexibility which can fundamentally change what your organization can achieve. Here are some of the key benefits:
AI can rapidly ingest data and uncover patterns in it, allowing you to make decisions with a deeper understanding of potential benefits and risks. NLP means that everyone can use these tools — even those in non-technical or data-focused roles.
Enterprise AI can track and analyze data in real-time, allowing you to identify problems before they happen, gain insight into operational bottlenecks and manage complicated supply chains, whether for physical products or software components.
Enterprise AI empowers every team and department to harness data-driven insights, allowing them to come up with new solutions to complex problems without having to rely on a data scientist or write queries themselves. Because it can automate even complex tasks, AI also gives your teams more time to propose, experiment with and implement innovative new tools.
Using dedicated support chatbots with 24/7 uptime and predictive analytics, AI can learn from your customers and recommend fixes automatically. These tools can also triage support requests, sending major issues to technical support teams before they escalate. You can also use AI to track product usage, offering educational resources to less-engaged users to help them get more out of the product.
As with any new and innovative technology, AI is not “plug and play,” and getting the most out of enterprise AI means being aware of potential challenges. Here are some of the most common ones:
Data siloing is not a new issue, but it can be a substantial hurdle for AI adoption and performance. Fragmented data storage can make it difficult to integrate AI successfully, as AI enterprise softwares may not have access to the critical data sources it needs to be effective. This can limit its potential usefulness and stifle opportunities for cross-team collaboration.
Like other transformative technologies, AI can be expensive to implement, as achieving ROI requires significant reorganization, training and maintenance. Enterprise AI is also a rapidly evolving space, and locking in to one vendor can put you at risk of missing out on new innovations from competitors.
AI has shown the potential to identify and act on data patterns which repeat human biases. This can be particularly concerning when the data is subject to government regulations like HIPAA, as mishandling sensitive data can create serious risks.
Each implementation of enterprise AI will depend on your budget and goals, but you can use the following steps to create a successful AI implementation strategy:
Harnessing the benefits of enterprise AI requires you to set clear and measurable goals before adopting it. Using these objectives, you can assess and calibrate your strategy, for example, by tracking customer satisfaction after implementing an AI-powered chatbot. Because benefits like collaboration and productivity can be difficult to measure, it can be more useful to use KPIs linked to specific business outcomes, such as the speed of product development cycles and the number of hours someone spends closing sales.
AI’s significant capabilities require advanced and energy-intensive computation and will work best when the tools can access accurate and relevant data. Before incorporating an enterprise AI solution, it’s critical that you assess that solution’s hardware and computational needs, as well as the availability of quality data. Additionally, the teams implementing these solutions need to understand your AI strategy and goals, which makes team training an important aspect of readiness.
Allowing one team to pilot AI tools or incorporating AI into a small set of use cases can make it easier for you to measure their impact, both in terms of the ROI and the costs of incorporating and maintaining enterprise AI tools. An iterative approach can also help your team buy into your strategy and provide you with useful feedback.
Taking everything you learned from the prior steps, you can identify and implement AI in more use cases, particularly those with a ready supply of quality data and the potential to directly impact your objectives. As you move forward, your enterprise AI tools will improve and scale, becoming key elements of your internal infrastructure while making modern changes and increasing efficiency.
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