How do AI and agentic platforms handle large volumes of data from various sources while maintaining performance?

AI and agentic platforms are designed to efficiently manage and process large amounts of data. To maintain performance even while working with high volumes of information, they rely on a number of capabilities: Seamless data integration: AI and agentic platforms use APIs to connect with existing enterprise systems like CRM and ERP. This makes it possible for them to aggregate data from diverse sources in real time, ensuring that the AI models have access to the most current and relevant information. Scalable architecture: Built on cloud-based infrastructures, these platforms can dynamically allocate resources based on data volume and processing needs. As data volume increases, the platform can scale horizontally or vertically to maintain optimal performance. Efficient data processing: By applying advanced algorithms, including machine learning and natural language processing, these platforms can quickly analyze both structured (like databases) and unstructured data (like text and images). And with stream processing capabilities, these platforms can provide immediate insights and responses. Adaptive machine learning models: The AI models employed are designed to continuously learn and improve from the data they process. As they encounter more data, they refine their algorithms, increasing accuracy and adapting to trends and patterns. Data quality and governance: These platforms typically incorporate data governance frameworks and quality management tools for data integrity and compliance with regulations. This helps prevent issues related to inaccurate or incomplete data, which can impede performance.

How can enterprises use AI while ensuring sensitive data remains private and secure?

Enterprises can use AI while keeping sensitive data private and secure by implementing several key strategies. Adopting these strategies in concert makes it possible for enterprises to harness the power of AI while safeguarding data, ensuring they remain compliant with regulations, and building customer trust.Strategies for maintaining data security and privacy of enterprise AI applications: Data anonymization: Before using data for AI applications, organizations can anonymize it by removing personally identifiable information (PII). This ensures that even if the data is analyzed, it cannot be traced back to individual users, helping to protect their privacy. Data masking: Data masking involves automatically redacting or obfuscating sensitive information during processing. This means that when AI applications handle data for business tasks and workflows, any sensitive details are hidden or altered, ensuring that only non-sensitive information is exposed. This helps protect privacy while still allowing AI to perform its functions effectively. Encryption: Encrypting data both at rest (when stored) and in transit (when being sent over networks) is fundamental. Encryption transforms data into a secure format that can only be accessed with the right decryption key, making it much harder for unauthorized users to access sensitive information. Access controls: Implementing strict access controls ensures that only authorized personnel can view or interact with sensitive data. Role-based access management allows organizations to set permissions based on employee roles, limiting exposure to only the information necessary for their job. Compliance: Enterprises should adhere to data protection regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act). These laws set strict guidelines for how personal data should be collected, stored, and processed, helping ensure that organizations manage sensitive information responsibly. Regular audits and monitoring: Conducting regular audits and monitoring data access can help detect any unauthorized attempts to access sensitive information. This proactive approach allows organizations to identify vulnerabilities and address them before they lead to data breaches. Privacy by design: Incorporating privacy considerations into the AI development process from the outset means building systems that prioritize user privacy and data protection so that security measures are integrated into the AI solution from day one. Training and awareness: Educating employees about data privacy and security best practices is also a key part of maintaining security and privacy. Training staff on how to handle sensitive information and recognize potential security threats can not only significantly reduce the risk of data breaches—in the context of AI applications, it can prevent data misuse and mishandling that could compromise security and privacy.

Can AI solutions be customized to align with an organization's specific needs and industry requirements?

Absolutely! AI solutions are versatile and can be highly customized to meet the unique needs of an organization and its specific industry requirements. Here’s how this customization works:Tailoring algorithms: AI solutions can be developed with algorithms that are specifically designed for an organization’s business processes. This means that the AI can learn from the specific data and scenarios relevant to the business, making it more effective and relevant. Integration with existing systems: AI solutions can be integrated with an organization’s existing software and systems, such as Customer Relationship Management (CRM) or Enterprise Resource Planning (ERP) tools. This integration allows AI to work seamlessly within current workflows. Domain-specific training: AI models can be trained on data that is specific to the organization’s industry. For example, a healthcare organization can train its AI on medical datasets, while a financial institution can use financial data. This specialized training helps the AI understand industry-specific terminology and nuances, improving its performance. Adjustable parameters: Many AI platforms allow users to adjust parameters and settings to fit their specific use cases. This flexibility means organizations can fine-tune how the AI operates, such as modifying how it analyzes data or how it interacts with customers. User-friendly interfaces: Advanced AI solutions often come with intuitive interfaces that enable non-technical users to customize the AI without needing extensive programming knowledge. This feature empowers teams to make adjustments based on their specific needs quickly and easily. Feedback loops: AI solutions can be designed to incorporate feedback from users. This means that as employees interact with the AI, their input can be used to refine and improve the system over time, ensuring it continues to meet the organization’s evolving needs. Compliance and security features: Organizations can customize AI solutions to include specific compliance and security measures that align with industry regulations. This ensures that the AI not only performs its intended tasks but also adheres to defined standards—legal, regulatory, and ethical.

What governance frameworks should be implemented to monitor and control AI deployments across an enterprise?

Effective governance frameworks are essential for monitoring and controlling AI deployments, helping AI systems operate ethically, transparently, and in compliance with regulations. Key components of a robust AI governance framework: Policy development: Establish clear policies that outline the organization’s approach to AI use. These policies should define acceptable use cases, data handling practices, and guidelines for ethical AI deployment. They should also address issues like bias, accountability, and transparency. Ethical guidelines: Create ethical guidelines that govern AI development and usage. These guidelines should focus on fairness, accountability, and transparency, ensuring that AI systems do not perpetuate biases or discriminate against certain groups. Organizations can adopt frameworks like the AI ethics guidelines from organizations such as the IEEE or the EU. Data governance: Implement data governance policies to manage how data is collected, stored, and used in AI systems. This includes ensuring data quality, privacy, and security. Organizations should also establish protocols for data access and sharing to make sure sensitive information is protected. Monitoring and auditing: Set up continuous monitoring and auditing processes to track AI performance and compliance with established policies. This includes regular reviews of AI decision-making processes, model accuracy, and adherence to ethical guidelines. Audits can help identify any potential issues and allow for timely intervention. Risk management: Develop a risk management framework specifically for AI deployments. This framework should assess potential risks associated with AI, including operational, legal, and reputational risks. Organizations should implement strategies to mitigate these risks and establish protocols for responding to incidents. Stakeholder engagement: Involve various stakeholders, including employees, customers, and regulatory bodies, in the governance process. This engagement can provide valuable perspective and help ensure that AI solutions meet the needs and concerns of all parties involved. Training and awareness: Provide training for employees on AI governance policies and ethical AI practices. Ensuring that staff understand the importance of responsible AI use and are aware of governance frameworks will help develop and support a culture of accountability within the organization. Feedback mechanisms: Establish feedback mechanisms that allow users to report issues or concerns related to AI systems. This can help organizations identify and address problems quickly, ensuring that AI deployments remain aligned with ethical and operational standards.

What's the best way to assess how well AI is performing in different parts of our business?

Determining the best way to assess AI performance in your business is not a one-size-fits-all approach; it largely depends on the specific context and objectives for the areas where AI is deployed. The right assessment methods will vary based on what you aim to achieve with AI systems. However, some effective strategies to evaluate AI performance are: Define clear objectives for each AI deployment. What do you want the AI to achieve? Whether it’s improving customer service, increasing efficiency, or enhancing decision-making, having clear objectives will help measure success. Identify and track relevant KPIs that align with your objectives to gauge performance quantitatively. For example, if AI is used in customer service, you might measure response time, customer satisfaction scores, or resolution rates. Or, for AI in marketing, you could look at conversion rates or return on investment (ROI). Continuously monitor AI performance. This could involve real-time analytics dashboards that display KPIs and other relevant data. Regularly reviewing performance data makes it possible to spot trends, identify issues, and make informed decisions. Collect feedback from employees and customers who interact with the AI systems. Their insights can provide valuable information about how well the AI is meeting their needs and where improvements can be made. Surveys, interviews, and focus groups are effective ways to gather this feedback. Benchmark performance of your AI systems against industry standards or competitors. Benchmarking can help you understand where your AI stands in relation to others in your field and identify areas for improvement. Use A/B testing to evaluate different AI models or approaches. By comparing the performance of two variations in real-world scenarios, you can determine which one delivers better results. This method is particularly useful for optimizing algorithms and refining AI strategies. Regularly audit AI systems to assess their accuracy, fairness, and compliance with ethical guidelines. This involves reviewing the data used, the algorithms employed, and the outcomes produced. Audits help ensure that AI is functioning as intended and not introducing biases or errors. Evaluate AI's impact on business outcomes. Look at how AI has affected productivity, cost savings, customer satisfaction, and revenue growth. Assessing the broader business impact helps you understand the value AI brings to your organization. Iterate and improve, using insights gained from assessments to make continuous improvements to AI systems. Whether it’s fine-tuning algorithms, adjusting data inputs, or changing workflows, being ready to iterate ensures that AI remains effective and aligned with business goals.

Do enterprises need to modernize their IT infrastructure before deploying AI solutions?

The short answer is, yes, enterprises often need to modernize their IT infrastructure before deploying AI solutions. And, while it’s not always mandatory to completely overhaul IT infrastructure before AI deployment, an updated infrastructure enhances compatibility, scalability, data management, performance, security, and collaboration—all of which are critical for successfully implementing and leveraging AI technologies. Reasons why it’s valuable—and sometimes necessary—to modernize IT infrastructure before deploying AI solutions: Compatibility: Many AI solutions require advanced computing power and storage capabilities that older IT infrastructures may not support. Modernizing infrastructure ensures compatibility with the latest AI tools and technologies, making it easier to integrate them into existing systems. Scalability: AI applications can generate large amounts of data and require significant processing power. Modern IT infrastructure is typically more scalable, allowing for easy expansion of capabilities as AI needs to grow. This flexibility is key for handling increased workloads and data volumes. Data management: Effective AI solutions depend on high-quality, well-organized data. Modernizing IT infrastructure often involves implementing better data management practices, such as data warehousing and cloud storage. This improvement helps ensure that the data used for AI training and analysis is accessible, secure, and reliable. Performance: Legacy IT systems may struggle to provide the performance levels needed for AI applications. Modern infrastructure, including faster servers and upgraded networks, can significantly boost the speed and efficiency of AI processes. Security: AI solutions handle sensitive data, so having a robust security framework is non-negotiable. Modern IT infrastructure typically includes advanced security measures, such as encryption, access controls, and real-time monitoring, which help protect against data breaches and cyber threats. Collaboration tools: AI often requires collaboration across different teams and departments. A modern IT infrastructure can support better communication and collaboration tools, making it easier for teams to work together on AI projects. Cloud integration: Many AI solutions are cloud-based, offering flexibility and cost-effectiveness. Modernizing IT infrastructure often involves adopting cloud technologies, which can facilitate the deployment of AI solutions and allow for easier access to powerful AI resources. Ongoing maintenance and support: An updated infrastructure is generally easier to maintain and support. This means that IT teams can focus more on optimizing AI solutions instead of spending time troubleshooting outdated systems.

What is Enterprise AI? Benefits, Best Practices, and Examples

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What is enterprise AI?
What is enterprise AI?
Journey to the autonomous enterprise
Enterprise AI applications
Why enterprise AI
Challenges of implementation
Best practices
Strategy and architecture
Future trends
Measuring success
How we powers the autonomous enterprise
FAQ

What is enterprise AI?

Enterprise AI brings people, processes, and technology together to enable the journey to an autonomous enterprise. It’s the strategic foundation for how organizations evolve toward autonomy, connecting human workers, agentic AI, and automated decision-making in a single, collaborative ecosystem that completes end-to-end processes. When orchestrated effectively, this combination increases operational efficiency, drives innovation, and improves decision-making in an increasingly fast-paced, complex business environment.

The explosion of agentic AI and autonomous agents, projected to be in 40% of enterprise applications by 2026, vastly increases the importance and urgency of enterprise AI. To stay competitive, large organizations need the ability to move faster, scale operations, and spark innovation. Enterprise AI provides the key, freeing human workers to focus on strategic, cognitive work by automating routine tasks and harnessing data to drive faster, more informed decisions.

This article will define enterprise AI, explore how it works, examine its real-world applications, explain how to measure success, and discuss Automation Anywhere’s role in accelerating the journey to becoming an autonomous enterprise.

From enterprise AI to the autonomous enterprise

An autonomous enterprise uses agentic process automation (APA) to orchestrate robotic process automation (RPA), AI agents, and human workers and automate up to 80% of processes — even complex, end-to-end processes. Autonomous enterprises enjoy vastly increased efficiency, scalability, and innovation to deliver better, faster, and more personalized customer experiences while reducing risks and costs.

Enterprise AI makes an autonomous enterprise possible, beginning with simple, task-level automation and eventually reaching enterprise-wide autonomy. At the earliest stages of this journey, human workers are assisted by simple automations. Then, intelligent AI agents are added to collaborate with human workers on more complex processes. Finally, APA and AI agents take on decision-driven work to execute processes autonomously, relying on human workers for oversight.

Assisted automation uses task-level automations and agentic AI to help human workers, support decision-making, and accelerate processes, while maintaining human oversight.
- Enterprises at this stage have guardrails in place, a stable data foundation, and are adept at using automation and basic AI tools to pilot solutions for tasks like data analysis and decision support.
Intelligent automation adds APA to orchestrate RPA, AI agents, and human workers across complex, end-to-end processes, completing many tasks autonomously while keeping humans in the loop for oversight and critical decisions.
- Enterprises at this stage have an established governance framework and platform to ensure safety and responsible AI usage as automation scales across the enterprise. Human workers and AI collaborate on complex processes, and AI provides predictive and real-time analytics.
Autonomous enterprises extend APA to create independent, self-learning APA and agentic AI systems that manage, execute, and govern processes with minimal human involvement.
- Enterprises at this stage use traditional, rules-based automations plus more adaptive, intelligent components, with human workers remaining in the loop for oversight or critical decision-making.

Most organizations have embarked on this journey, whether just beginning to use pinpoint-assisted automation pilots or already working to scale agentic AI and APA across the far corners of the enterprise. Enterprise AI makes it all possible.

Enterprise AI applications

Enterprise AI is transforming enterprises by automating complex workflows and highlighting opportunities for new ways to operate and optimize. Every industry benefits from enterprise AI, demonstrating its versatility in delivering real value across diverse business needs, structures, and sizes. Applications range from fraud detection and risk assessment in financial services firms to diagnostics, treatment recommendations, and critical systems monitoring in healthcare.

Cross-industry applications of enterprise AI include:

Telecom: SoftBank uses enterprise AI to create capacity equivalent to 4,500 full-time workers

Challenge: This telecom firm wanted to transition from RPA to more advanced stages of enterprise AI, aiming to automate higher-value activities and move beyond routine task automation.
Solution: Using generative AI and Automation Anywhere’s Agentic Process Automation System, SoftBank equipped teams to identify and implement enterprise AI automations that streamlined processes and reduced reliance on human intervention.
Impact: SoftBank reengineered and automated the equivalent of 4,500 full-time workers, saving 700 hours on AI-enabled call volume predictions and cutting recruitment hours by 85%. The company is now pushing to become an autonomous enterprise, incorporating APA and agentic AI for analysis and strategic decision-making that enhances operational efficiency.

Agriculture: Cargill saves up $15 million with enterprise AI, cutting order processing to less than 1 minute

Challenge: This agricultural and industrial products producer wanted to enhance the efficiency of its order management process, which handles orders from thousands of businesses, from enterprises to small farmers, in myriad formats.
Solution: Using agentic AI and Automation Anywhere’s Agentic Process Automation System, Cargill automated 70% of its order management process with zero business disruption and immediate improvements to the customer experience.
Impact: Cargill now saves up to $15 million annually in just this single workflow, with orders processed in under one minute. With human workers freed from tedious manual order entry, they have more time to focus on building stronger customer relationships.

Services: KPMG realizes $90 million impact from enterprise AI, plus $150 million in future automation opportunities

Challenge: This global professional services firm was increasing operational efficiency by reducing workloads. It began with task automations in its candidate recruiting process, where early success gave it the confidence to explore enterprise AI in more complex processes.
Solution: Using APA and agentic AI along with Automation Anywhere’s Document Automation, KPMG automated knowledge gathering and creation of new learning experiences. Those processes were orchestrated with Automation Anywhere’s Agentic Process Automation System, which it uses to create self-learning AI agents that predict and prevent future issues.
Impact:KPMG reduced back orders by $50 million and saved $30 million by accelerating days sales outstanding. Looking ahead, the firm has already identified $150 million in future automation opportunities, enabled by enterprise AI.

Why enterprise AI (done right) matters now

In the above use cases, and in countless other organizations worldwide, enterprise AI provides measurable business improvements. Task-level automations, combined with cognitive AI agents orchestrated by APA, eliminate bottlenecks, increase business agility, break down information silos, and give human workers the freedom to focus on what matters most.

But that’s not all. Here are other areas where enterprise AI shows real benefits:

Operational speed and productivity: Processes that took weeks of manual effort can be accomplished in seconds with enterprise AI. Alight uses Automation Anywhere to process claims six times faster than manual efforts, slashing call volumes in half. Sumitomo Rubber Industries uses Automation Anywhere to shorten logistics processes from 20 days to four hours. APA enables operations to run faster, giving workers more time to focus and think, engage with customers and stakeholders, and make decisions faster and more confidently.
Scalability: Enterprise AI helps businesses scale global operations by facilitating faster processes that require fewer resources to complete. It also scales knowledge sharing, where AI gleans insights from massive datasets in seconds and makes valuable information instantly accessible to employees throughout the organization. For example, IQVIA uses Automation Anywhere to boost analytics efficiency by 80% while reducing data entry costs by 65%.
Innovation: Enterprise AI catalyzes innovation, agility, and responsiveness to market dynamics. It automates work, optimizes resource use, and provides time to harness unused human potential that can be applied to identify problems and find new opportunities — all while APA and AI agents complete vital processes in the background. With overall R&D productivity declining in many industries, enterprise AI gives workers time to develop more ideas and the insights to accelerate that innovation.
Governance and trust: Enterprise AI improves governance by infusing automations with consistent rules and guardrails for how information is managed across the organization. AI agents enforce data quality standards, monitor compliance with regulatory requirements, and protect sensitive information. Leading enterprises know the value of governance, even in their AI initiatives: Accenture found that companies driving enterprise-wide value from AI are nearly three times as likely to have AI governance programs in place.
Cost efficiency: Enterprise AI drives significant cost reductions by optimizing workflows and reducing resource use to achieve greater efficiency, lowering operational expenses while maintaining or improving quality and output. Synergy uses Automation Anywhere to manage billing exceptions — 179,000 annually — increasing efficiency for a savings of $2.3 million per year.

Challenges of implementing enterprise AI

Enterprise AI offers significant efficiency gains and cost-cutting possibilities, but implementing AI is not without its challenges. Recognizing the challenges of implementing AI at enterprise scale is essential for crafting strategies that ensure successful adoption and maximize ROI. Watch out for these potential pitfalls:

Struggling with data gathering and integration

Enterprise AI relies on gathering and integrating high-quality data from diverse sources that are representative of the domain in question. Disparate data systems, data silos, and inconsistent formats are common roadblocks impacting the accuracy and effectiveness of AI models.

Lacking AI expertise

A lack of skilled AI professionals who can design, develop, and manage AI systems effectively can delay AI project implementations and limit the benefits of AI technologies. Solutions include upskilling existing employees, partnering with AI service providers, and using no-code AI platforms such as Automation Anywhere’s AI Agent Studio, which require minimal data science expertise to create and deploy AI-driven solutions.

Justifying the initial investment

Implementing enterprise AI can involve substantial upfront investments in technologies, infrastructure, and training. However, many AI implementations deliver value right away. In the case of Petrobras, implementing a generative AI automation solution returned $120 million in savings in just three weeks. Of course, effective AI implementations will continue to add value over the long term, with a return on investment from ongoing operational efficiencies, cost savings, and revenue growth.

Lacking stakeholder buy-in

Securing buy-in from all organizational stakeholders is at the core of successful AI adoption. Resistance to change, lack of clarity on the benefits of AI, and concerns about job displacement can impede enterprise AI initiatives. Effective change management strategies, clear value propositions, and showcasing the diversity of AI use cases and successful AI implementations can help align stakeholders and generate excitement.

Best practices for implementing enterprise AI

Because AI adoption is accelerating — 90% of enterprises expect 2026 AI budgets to increase — there is no shortage of best practices to learn from successes and failures across industries. These insights help leaders navigate the complexities of enterprise AI implementations to ensure initiatives deliver meaningful and sustainable value.

What follows is an actionable framework for operationalizing enterprise AI at scale.

Define enterprise goals and success metrics. Identify specific use cases where AI can add value to help select the right AI tools and set measurable targets. This ensures AI projects deliver impactful outcomes and contribute to goal attainment.

Assess data and infrastructure readiness. Look for or develop high-quality data pipelines and implement data governance frameworks to ensure consistent, secure data management. These practices enhance AI reliability, trust, and security, and support regulatory compliance.

Start with focused, high-impact pilots. Pilot projects allow organizations to test APA and agentic AI and demonstrate tangible benefits before scaling up. For example, Merck piloted automations for compliance-related document processing, saving 150,000 hours and sparking enterprise AI initiatives in operations, product development, supply chain, and other areas.

Establish responsible AI and automation governance. Put guardrails and infrastructures in place to guide enterprise AI and enable responsible AI and automation. For example, creating an enterprise AI ethics committee to review projects and uphold standards of fairness and transparency builds trust while ensuring compliance with regulatory requirements.

Use APA to orchestrate agentic AI thinking with RPA execution and human oversight. APA leverages traditional, rules-based automations plus more adaptive, intelligent components, allowing business processes to flow autonomously while human workers remain in the loop. Put an APA platform, such as Automation Anywhere’s Agentic Process Automation System, in place early to ensure all components of enterprise AI integrate seamlessly.

Scale and continuously optimize. Bring together different departments and stakeholders to create a holistic enterprise AI strategy that plans for scale and drives collective success. Launch a center of excellence (CoE), and use tools like Automation Anywhere’s CoE Manager to govern, scale, and optimize automation initiatives effectively. Taking an adaptive approach ensures that AI and automation initiatives remain adaptable, effective, and relevant, consistently delivering value over the long term.

Enterprise AI strategy and architecture

Data and infrastructure foundation

Enterprise AI requires access to large volumes of enterprise-grade data. Efficient data management, scalability across departments, and the ability to process diverse data types are essential features that ensure the platform can support expanding business needs without compromising performance. Platforms should use pre-built packages, APIs, and integration platform-as-a-service (iPaaS) to mesh seamlessly with existing infrastructures and bring real-time data into enterprise AI.

APA as the automation fabric connecting AI insights to execution

APA platforms coordinate across AI agents, traditional automation, APIs, documents, and human workers to create goal-driven automations that plan, execute, learn, and self-heal autonomously. Platforms must operate safely and responsibly with any application, team, environment, and data to eliminate barriers between people, apps, and processes.

Governance and compliance layer

Enterprise AI platforms must include secure guardrails and governance for responsible AI, with built-in security to protect sensitive data and ensure privacy and regulatory compliance. Platforms should offer observability tools to audit and monitor AI agent behaviors, tool usage, and performance, plus evaluation capabilities to benchmark accuracy and consistency.

Future trends in enterprise AI

Enterprise AI is moving fast. Here are a few areas where enterprises can start planning and investing today to stay ahead of the curve over the next few years.

ROI: With AI spending expected to grow by more than 30% annually through 2029, leaders must show real, and really big, results from these investments, which requires data on AI deployments, performance, and results.
Interoperability: Communication protocols like A2A and MCP enable AI agents to collaborate across systems, underscoring the importance of an agnostic APA platform approach that centralizes governance, security, integrations, and more.
AI-first processes:To increase enterprise AI’s scalability, speed, accuracy, and productivity, enterprises will redesign processes to enable AI to deliver the desired outcome without human intervention.
Training: Enterprises must prioritize worker training and upskilling to improve AI competency across the organization, enabling teams to implement enterprise AI in more areas and for more impact.

Automation Anywhere is shaping the future of enterprise AI and helping organizations move quickly toward becoming autonomous enterprises. The Agentic Process Automation System provides a unified orchestration platform that delivers real business results through enterprise-wide automation.

Measuring enterprise AI success

Connecting AI initiatives to tangible business outcomes ensures that enterprises realize value and achieve strategic objectives. Organizations that follow enterprise AI best practices — such as high-quality data, strong security and governance, and AI applied to clear business objectives — will realize a greater return on AI investments.

Key metrics enterprises can track to measure agentic AI success include:

Automation rate to measure the share of tasks and processes automated within a team, department, or system.
ROI that covers hard impacts like speed and cost savings, plus soft benefits like accuracy and experience improvements.
Adoption to show the percentage of workers using automation tools and the percentage of processes automated.
Governance metrics to quantify AI accuracy, data privacy violations, downtime, explainability, and governance program maturity level.
Decision speed to show how AI-enabled automations compress cycle times, accelerate information dissemination, and provide more accurate forecasts and projections.

Track and report on these and similar enterprise AI performance metrics at least monthly, or use an APA platform that provides real-time dashboards. Communicate progress and shortfalls frequently, and conduct quarterly reviews to head off potential issues and share insights on AI deployments.

How Automation Anywhere powers the autonomous enterprise

While 88% of organizations already use AI, 62% are still just experimenting or piloting limited use cases. Scaling AI across the enterprise, like any traditional transformation or change management effort, can be challenging, especially as the most significant enterprise AI opportunities are complex processes that touch many systems and teams.

Becoming an autonomous enterprise requires reimagining the very fabric of operations and setting a new standard for what organizations can achieve. It also requires a unified automation platform that seamlessly automates complex, long-running processes across departments, vendors, and applications to weave AI into every aspect of enterprise operations.

Unified APA platform: Automation Anywhere unifies RPA, agentic AI, orchestration, and more, combining traditional automation technology with AI agents to automate up to 80% of processes while always remaining agnostic across platforms, tools, and vendors.
Autonomous operations at scale: Automation Anywhere empowers enterprises to create goal-based AI agents that execute, adapt, and make decisions automatically across business functions.
Governance and trust: Automation Anywhere enables enterprise-grade governance, compliance, and visibility to streamline workflows, enhance operations, and scale with confidence.
Business impact: Automation Anywhere helps unlock the full power of agentic AI, from process discovery to ROI tracking, to scale automations that deliver faster decision cycles, reduced operational cost, shorter time to value, and more.

Automation Anywhere provides the infrastructure supporting a faster path to an autonomous enterprise. Customers move quickly from automation pilots and experimentation to using with agentic AI to measurable ROI from faster execution, lower operational costs, and seamless scalability. Agentic solutions for accounts payable, customer onboarding, and other ubiquitious functions help further accelerate pilot-to-production cycles with purpose-built, governed AI agents that deliver real results on high-impact business processes.

Frequently asked questions.

How can enterprise AI support sustainability and ESG goals?

ESG and sustainability disclosure requirements create enormous administrative and reporting burdens for organizations. And businesses are spending to keep up: only 7% are cutting back on ESG budgets. Enterprise AI automates slow, manual data-gathering, reporting, and disclosure processes to cut costs, accelerate decision-making, and provide insights that drive ESG and sustainability impact.

What are the biggest misconceptions about implementing enterprise AI?

The biggest misconception of enterprise AI implementation is the oft-cited notion that nearly all AI pilots fail. In fact, thousands of enterprises are seeing real and measurable impact from AI implementations of all sizes. Another popular misconception of implementing enterprise AI is that it will eliminate jobs. In fact, recent research shows that job growth has increased for roles explicitly targeted by enterprise AI.

What’s the difference between enterprise AI and an autonomous enterprise?

Enterprise AI makes an autonomous enterprise possible by managing and directing RPA for task-level execution, and being orchestrated by APA alongside human workers. The autonomous enterprise relies on enterprise AI as the thinking, acting, and scaling component of autonomy.

How does enterprise AI affect workforce roles and skill requirements?

Enterprise AI elevates human workers out of task-level tedium and into more cognitive, creative, and strategic roles. However, AI and automation require workers in nearly every role to gain new skills, such as effective prompting for generative AI, greater familiarity with data, security, and privacy, and, for those deploying AI, coding, design, and software development.

What ROI benchmarks should enterprises expect from mature AI programs?

An AI capability maturity model provides a framework and roadmap for becoming an autonomous enterprise. In five stages, the model shows how enterprises can move from basic, human-led automations to fully autonomous operations. More mature AI programs have agentic AI executing complex processes with little human oversight, where AI manages and governs processes while human workers make critical decisions.

How will emerging regulations (like the EU AI Act) impact enterprise AI adoption?

The European Union’s Artificial Intelligence Act (EU AI Act) covers the development and use of AI in the EU. It implements governance, risk management, and transparency requirements, and puts limits on or bans more risky AI applications. While it may seem as if these types of AI regulations will slow AI adoption, complying with transparency and governance rules will spur enterprises to create more holistic, centralized enterprise AI efforts that may actually accelerate adoption.