Enterprise businesses are increasingly recognizing the need to invest in AI solutions to stay competitive. Global adoption has risen to 72% in 2024, up from around 50% in recent years.
Enterprise AI helps to automate tasks across multiple systems, support different domains and departments, and drive boosts in organizational efficiency and performance.
However, investing in just any AI platform isn't enough. Unlike basic or narrow AI tools, effective enterprise AI requires a holistic approach that supports clear business goal alignment, understands your unique organizational context, and facilitates seamless integration across your diverse technological ecosystems.
The right AI solution can significantly streamline operations, unlock new opportunities to improve efficiency, and can help to drive costs reductions. Let’s explore Enterprise AI, the differences between potential AI solutions, and strategies to excel in its implementation.
What is enterprise AI?
Enterprise AI refers to the strategic integration and deployment of AI technologies and AI platforms across numerous levels of an organization.
These AI applications use advanced machine learning, natural language processing (NLP), and other advanced AI techniques to surface data-driven insights, automate processes, and improve operational efficiencies among other business improvements.
Enterprises choose all kinds of AI solutions to address their diverse domains and challenges, from basic and narrow AI tools to advanced AI copilots. Some advanced enterprise AI tools are agentic, meaning they can reason and take independent actions across entire organizations (and across a wide range of connected systems). Let’s explore these solutions and their key differences.
How are enterprise AI platforms different from basic or narrow AI?
Basic AI handles simple, repetitive tasks like automating data or simple customer queries. Similarly, narrow AI is trained only for specific tasks or particular business functions. However, both of these fall short for managing for the complex and dynamic needs of large organizations.
That’s why enterprise AI is designed for adaptability and robustness, and built to handle complex, large-scale data environments and workflows across different departments and systems seamlessly.
Why basic and narrow AI tools fall short for large enterprises
Basic AI tools often lack the ability to handle tasks across departments and multiple systems (CRM, ERP, analytics), meaning employees may still need to switch between business systems and manually complete tasks that could be automated. In terms of insights, basic AI may provide irrelevant or outdated responses since it’s not fully integrated into all your business systems and applications and lacks understanding of your unique business context.
There may also be cost and security risks when using these tools in an enterprise context, without sufficient enterprise-level advanced AI security and permissions. When using basic or narrow AI, API costs also have the potential to become expensive as they can rely on third-party API providers for LLM access and support, which could result in increased operational expenses as usage scales.
With basic and narrow AI solutions, businesses may miss out on opportunities for AI-driven insights and automation, leading to a fragmented tech stack and reduced productivity.
What’s an AI copilot?
An AI copilot provides a chat (i.e. conversational) interface to support users in completing various tasks and decision-making processes within an enterprise environment.
This interface leverages the power of Large Language Models (LLMs) to understand user requests, generate relevant information, and assist with a wide range of tasks, from drafting emails and scheduling meetings to analyzing data and making predictions. In these ways, AI copilots are able to take enterprise AI much further than simple apps or chatbots that are designed to power specific use cases.
A successful enterprise AI strategy can include equipping your employees with a copilot, ensuring AI is able to be fully integrated and embedded across your entire organization. AI copilots help enable enterprise AI success by:
- Providing context-aware assistance, proactively responding to user needs based on contextual information (like which applications a user is accessing)
- Automating mundane tasks like employee support or payroll management, helping teams gain time and resources for strategic and creative work
- Analyzing data across tools and departments, such as marketing sales pipeline data
- Enabling seamless communication between employees, customers, and vendors
- Unifying disparate tools, systems, and processes that would otherwise be siloed
By integrating AI copilots across business operations, enterprises can reduce friction in routine but infrequent tasks, help manage workloads, automate repetitive tasks, and empower employees to make faster, more informed decisions, ultimately boosting productivity for every employee.
AI copilots have a variety of use cases across the enterprise org. For example, in IT, AI copilots can help resolve technical issues and automate ticketing systems, responding to issues faster and making sure team productivity isn’t stalled by a human dependency.
Artificial intelligence can help across enterprise departments
AI can help to boost productivity, improves the customer experience, supports decision-making, and resolves friction in operations. Rather than being able to run the business for you, effective AI empowers your employees by automating workflows and improving efficiency.
In HR, enterprise AI models can automate tasks like onboarding, policy inquiries, and payroll management, freeing up time for more strategic efforts. It also can consolidate the abilities from multiple HR systems into a single platform. A more holistic HR AI solution can cover the entire employee lifecycle, from recruitment, onboarding, and management to professional development, employee engagement, and compliance.
For IT departments, AI enhances service desks by offering self-service for employees. It can streamline user access, hardware management, security, troubleshooting, and ticket management. By automating these time-consuming tasks, in some cases AI was shown to improve employee performance and productivity by 40%.
Understanding the AI behind enterprise AI solutions
Enterprise AI offers a complete toolset, encompassing a diverse range of core AI technologies for complex enterprise needs. These AI technologies work together to enable businesses to optimize operations, improve decision-making, and gain a competitive advantage. Here's how:
- Machine learning: allows systems to learn from data and improve over time without explicit programming.
- Artificial neural networks: Inspired by the human brain, ANN allows the AI to manage and process complex information, recognize patterns, and make predictions.
- Natural Language Processing: NLP enables AI to understand, interpret, and generate human language, facilitating communication and interaction with users.
- Data mining: Involves extracting valuable insights and patterns from large datasets, providing the fuel for AI algorithms to learn and make informed decisions.
- Generative AI: Also referred to as genAI, creates new content like text and images to automate creative tasks and design.
- Agentic AI: Refers to AI systems that can operate autonomously, making decisions and completing tasks with minimal or no human intervention.
Building enterprise AI models
To build and deploy effective AI models, a structured process is followed. It starts with data collection and preparation, where raw data is cleaned and formatted for analysis to ensure accuracy and consistency. This crucial step lays the foundation for successful AI model training.
Next, machine learning algorithms analyze data to train models. This involves identifying patterns, relationships, and insights within the data to build predictive models.
Once trained, these models are integrated with existing business systems. Models are continuously improved with feedback loops, and are refined and updated using new data and improved algorithms. Throughout the process, NLP enables users to interact with these AI systems using human language, allowing for more intuitive and user-friendly interfaces.
Data quality forms the cornerstone of a successful enterprise AI tool. Models trained on low-quality, biased, or incomplete data may produce inaccurate or outdated results or exhibit biases. Ensuring data quality and integrity is paramount for building robust and reliable AI solutions.
Enterprise AI implementation challenges (And how to overcome them)
There are a wide variety of enterprise AI applications, and implementing AI organization-wide can bring transformative benefits — but also presents challenges.
AI systems handle sensitive data and can be vulnerable to risk, so companies should consider such implications and work with vendors that prioritize AI trustworthiness. And while AI is increasingly becoming more common across enterprises, it’s still relatively new for some day-to-day processes. Some employees may require training and change management to overcome hesitancy.
However, with the right enterprise AI strategy and approach, enterprises can overcome these challenges and effectively leverage AI’s capabilities across the organization.
Ensuring smooth integrations and organizational readiness
Integrating AI with existing systems can be complex and time-consuming, further complicating the implementation process. If there’s limited technical capacity or skilled employee bandwidth for such large-scale integrations, businesses can face delays or technical difficulties in getting AI systems up and running. Partner with enterprise AI vendors who have experience integrating their AI platform with your tech stack, offer out-of-the-box solutions, quick time to value, and can provide personalized support as you get AI off the ground.
Maintaining security across the enterprise
Ensuring security is key for a successful enterprise AI implementation. When business AI use cases include data management and key process automation, there’s a risk of data breaches and sensitive information being released. Adopt AI solutions that are built with security in mind and already comply with global industry regulations.
Ensure they’re SOC-2-compliant and meet security standards set by the National Institute of Standards and Technology’s AI Risk Management Framework, which assesses security and privacy measures for AI systems.
Organizations should also stay informed about emerging threats, establish strong AI governance, and consider a security-first protocol to safeguard AI systems and the data they process. This can help minimize vulnerabilities and maintain regulatory compliance.
Giving the right levels of access to the right people
Enterprises face challenges around user permissions and access that impact data security. Data needs to be stored security with proper access controls in place, with access granted to only those who require it.
By selecting AI solutions that provide granular control over user access, businesses can ensure that only the right people can access every piece of information. It’s essential that users only access the data they are authorized to see to prevent accidental or malicious attacks.
Compliance with data privacy requirements like GDPR and CCPA is also essential. These frameworks require businesses to uphold set standards for managing customer data. By taking proactive steps in these areas, businesses can better safeguard their AI systems and maintain compliance.
Getting everyone to adopt the technology
Adoption of enterprise AI can be challenging due to the time and effort required to learn new systems, as well as employee hesitancy to change during digital transformation. While some will be excited, other employees may be less open to adopt AI initiatives.
To support the change process, organizations can provide training to employees so they understand the benefits of enterprise automation and AI and feel confident using it. Choosing AI solutions that are intuitive and easy to learn can also help.
Having persona-specific marketing and outreach campaigns tailored to the needs of different roles can encourage broader buy-in. Training owner champions within the organization, who are interested in AI and can guide their teams through the transition, can help with smoother adoption.
A reliable partner is the key to a successful enterprise AI strategy
Enterprise AI is a rapidly changing sector, with new advancements and capabilities arising every day. A dependable partner ensures that your enterprise AI systems stay current, secure, and capable of handling the increasing complexity of tasks, enabling you to maintain a competitive edge and adapt to new challenges.
Moveworks is a popular choice for leading organizations looking for an advanced enterprise AI solution that provides enterprise search, automates business processes with ease, and enables you to maximize employee and agent productivity.
Moveworks’ Copilot helps employees to easily automate tasks, find information, get notifications across all your business applications. We give employees one place to go to find information and get support while enabling great efficiency for your business.
Discover why the world’s most innovative brands like Databricks, Broadcom, Hearst, and Palo Alto Networks trust Moveworks to eliminate repetitive support issues, deliver instant knowledge, and empower employees to work faster across applications.
Discover how Moveworks can help your employees to search, automate processes, and improve productivity — schedule your demo today.
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