Introduction: The AI Enterprise Revolution
As we navigate through 2026, artificial intelligence has transcended from being a competitive advantage to becoming an operational necessity for enterprises worldwide. The enterprise software landscape has fundamentally transformed, with AI capabilities no longer serving as optional add-ons but as core functionalities deeply embedded within business-critical systems. Organizations that were once experimenting with AI pilots are now deploying autonomous agents, leveraging generative AI for complex workflows, and reimagining entire business processes through intelligent automation.
This comprehensive guide examines the top ten AI-enabled enterprise software platforms that are reshaping how businesses operate in 2026. These platforms represent the vanguard of enterprise AI, combining cutting-edge technology with proven enterprise reliability, security, and scalability. Whether you are evaluating AI solutions for customer relationship management, enterprise resource planning, human capital management, or cross-functional automation, this guide provides the detailed insights needed to make informed decisions for your organization’s digital transformation journey.
1. Salesforce Einstein AI and Agentforce: Transforming Customer Relationship Management
Salesforce has established itself as the undisputed leader in AI-powered customer relationship management, serving more than 150,000 companies globally and processing billions of customer interactions daily through its Einstein AI platform. In 2025, Salesforce significantly expanded its AI capabilities with the introduction of Agentforce, marking a strategic evolution from passive AI assistance to proactive, autonomous agent-based systems that can independently execute complex business tasks.
Core Capabilities and Platform Architecture
Salesforce Einstein represents a comprehensive AI layer integrated throughout the Customer 360 platform, combining predictive analytics, natural language processing, and generative AI capabilities. The platform’s architecture is built on three foundational pillars. First, Salesforce leverages its proprietary AI models developed specifically for customer relationship management use cases, ensuring optimal performance for sales, service, and marketing workflows. Second, through its partnership with OpenAI, Salesforce provides enterprise-grade access to ChatGPT technology, enabling sophisticated generative AI capabilities directly within the CRM environment. Third, the platform offers a bring-your-own-model capability, allowing organizations to integrate their preferred external AI models while maintaining the security and governance provided by Salesforce’s Einstein Trust Layer.
The Einstein Trust Layer represents a critical differentiator in the enterprise AI market. This security framework prevents large language models from retaining sensitive customer data, ensuring that organizations can leverage powerful generative AI capabilities while maintaining strict compliance with data privacy regulations including GDPR and CCPA. The trust layer operates at query time, passing only the minimum necessary data to AI models and immediately discarding it after processing, rather than allowing models to train on proprietary customer information.
Agentforce: The Next Generation of Enterprise AI
Salesforce Agentforce represents the company’s most ambitious AI initiative, introducing autonomous agents that can reason, plan, and execute multi-step business processes without constant human intervention. These AI agents go far beyond traditional chatbots, functioning as digital employees capable of handling complex scenarios across sales, service, marketing, and commerce.
In the sales domain, the Sales Qualification Agent autonomously researches and engages with leads, helping sales teams identify prospects with genuine purchase intent. The agent moves leads closer to full qualification by analyzing behavioral patterns, engagement history, and buying signals, then proactively reaching out through personalized communications. This capability has demonstrated significant impact on pipeline generation, with early adopters reporting increases in qualified opportunities ranging from twenty to thirty-five percent.
For customer service, Agentforce provides intelligent case resolution that can automatically understand customer issues, search knowledge bases, identify relevant solutions, and even take remediation actions when appropriate. Service agents powered by Einstein AI can draft contextual responses to customer inquiries across chat and email channels, summarize case resolutions to build institutional knowledge, and route complex issues to human agents with full context and recommended resolution paths.
Strategic Acquisitions Enhancing AI Capabilities
Salesforce has pursued an aggressive acquisition strategy throughout 2025 to accelerate its AI capabilities. The company acquired Regrello, a specialist in AI-driven workflow automation for supply chain and manufacturing operations, positioning Salesforce to extend Einstein’s capabilities into these traditionally complex domains. The acquisition of Informatica for eight billion dollars earlier in the year dramatically enhanced Salesforce’s data integration and management capabilities, providing the foundation for more sophisticated AI applications that require clean, unified data across diverse enterprise systems.
The company also acquired Convergence.ai to enhance its Data Cloud with better integration capabilities, and Waii to support agentic AI integration. These strategic moves demonstrate Salesforce’s commitment to building what CEO Marc Benioff describes as the “ultimate AI-data platform” that combines powerful AI models with comprehensive data management in a trusted, scalable environment.
Pricing and Enterprise Considerations
Salesforce Einstein follows a tiered pricing model that reflects the fragmented nature of enterprise CRM needs. Base Salesforce Enterprise licensing starts at one hundred sixty-five dollars per user per month, with AI capabilities requiring additional investments. Einstein Conversation Insights adds fifty dollars per user monthly, while Revenue Intelligence requires two hundred twenty dollars per user monthly, and Agentforce for Sales costs one hundred twenty-five dollars per user monthly. For a typical enterprise sales team of fifty users, annual licensing costs can exceed three hundred thirty-six thousand dollars before implementation expenses.
However, organizations implementing Einstein report significant returns on this investment through improved sales productivity, faster deal cycles, and enhanced customer satisfaction. The platform’s deep integration with the broader Salesforce ecosystem means that AI capabilities benefit from years of customer data, behavioral patterns, and process optimization already embedded in the CRM system.
2. SAP Joule and Business AI: Reimagining Enterprise Resource Planning
SAP has emerged as a formidable force in enterprise AI with Joule, its comprehensive AI copilot that is fundamentally reshaping how businesses interact with enterprise resource planning systems. Joule represents SAP’s vision for the future of enterprise software, where natural language interfaces replace traditional menu-driven navigation, and intelligent agents proactively assist users across every business function.
The Joule Architecture and Ecosystem
Joule is built on SAP’s Business Technology Platform and integrates deeply with the entire SAP Business Suite, including SAP S/4HANA for ERP, SAP SuccessFactors for human capital management, SAP Ariba for procurement, and SAP Analytics Cloud for business intelligence. This comprehensive integration provides Joule with unparalleled context about business operations, enabling it to deliver insights and automation that understand the complex interrelationships between different business processes.
The platform leverages purpose-built large language models specifically tailored for SAP workloads, including specialized models for ABAP development that can provide code predictions and explanations with high accuracy. These SAP-specific models are augmented by partnerships with leading AI providers, ensuring that Joule benefits from both domain expertise and cutting-edge generative AI capabilities.
Joule operates through multiple interaction modalities. The SAP Action Bar provides an always-on, omnipresent interface that travels with users across all SAP and third-party applications, delivering proactive recommendations based on the user’s current context and anticipated needs. This represents a significant advancement from reactive chatbot interfaces to truly anticipatory AI that surfaces relevant information and suggested actions before users explicitly request them.
Joule Agents: Autonomous Business Process Automation
SAP has introduced a comprehensive network of Joule Agents—specialized AI systems that autonomously execute multi-step workflows across different business functions. These agents collaborate with each other to deliver connected, enterprise-wide business outcomes that would previously require manual coordination across multiple departments and systems.
In finance, Joule Agents handle complex tasks such as account reconciliation, identifying discrepancies between voucher amounts, ledger entries, and subledger records. Rather than waiting for month-end close processes to surface issues, the reconciliation agent continuously reviews transactions, identifies exceptions, and suggests appropriate resolution actions. Finance teams report that this continuous monitoring approach has reduced their financial close timelines by twenty to thirty percent while improving accuracy.
For supply chain management, Joule Agents proactively monitor external factors including weather patterns, supplier financial health, geopolitical risks, and logistics disruptions. When potential issues are detected, the agents automatically assess impact across materials, inventory, distribution networks, and customer commitments, then draft communications to alert affected partners and suggest mitigation strategies. This proactive approach has proven particularly valuable in the volatile global supply chain environment, enabling organizations to respond to disruptions hours or days faster than traditional monitoring approaches would allow.
In procurement, Joule enhances daily tasks such as retrieving supplier bids, displaying past purchases and approval histories, and providing ready access to supplier information. The agents can automatically generate purchase orders based on inventory levels and consumption patterns, route approvals to appropriate stakeholders, and even negotiate terms with suppliers based on predefined business rules and constraints.
Joule Studio: Customization and Extension Platform
Recognizing that every enterprise has unique processes and requirements, SAP introduced Joule Studio as part of its Build platform, enabling organizations to create custom AI agents and skills tailored to their specific business needs. The skill builder capability became generally available in July 2025, with the agent builder capability scheduled for general availability in December 2025.
Joule Studio provides a low-code environment where developers can design, build, and deploy enterprise-ready custom agents using familiar drag-and-drop interfaces. These custom agents benefit from the same powerful SAP technologies that power standard Joule capabilities, including SAP Knowledge Graph for deep business context, SAP Business Data Cloud for comprehensive data access across SAP and non-SAP sources, and SAP’s central identity and authorization services to ensure responsible agent behavior.
Organizations are using Joule Studio to address industry-specific challenges that off-the-shelf agents cannot fully address. Healthcare providers are building agents that navigate complex regulatory requirements for medical billing and insurance claims processing. Manufacturing companies are creating agents that coordinate production scheduling with maintenance windows and supplier delivery commitments. Financial services firms are developing agents that monitor compliance requirements across multiple jurisdictions and automatically flag potential issues before they become violations.
Integration and Interoperability
SAP has made significant investments in ensuring Joule operates seamlessly across diverse technology environments. The bidirectional integration with Microsoft 365 Copilot, scheduled for general availability in the second quarter of 2025, enables users to leverage Joule capabilities from within Microsoft applications, creating a unified AI experience across SAP and Microsoft systems. Users can ask Joule questions about SAP data directly from Microsoft Teams, Outlook, or other Office applications, and Joule can trigger actions in SAP systems based on activities in Microsoft environments.
The partnership with Google Cloud to support the Agent2Agent interoperability protocol establishes a foundation for AI agents from different vendors to securely collaborate across platforms. This open standard ensures that Joule agents can interact with agents from other enterprise systems, sharing context and working together to complete cross-platform workflows.
Additionally, SAP’s collaboration with Perplexity AI enables Joule to address questions that require both internal business data and external information sources. By integrating Perplexity’s ability to search and synthesize information from the broader internet with Joule’s access to SAP Business Suite data, enterprises can get instant solutions that combine proprietary business context with current market intelligence, competitive information, and industry best practices.
Measurable Business Impact
Organizations implementing SAP Business AI with Joule are reporting dramatic improvements in operational efficiency. SAP data indicates that users can complete everyday tasks up to ninety percent faster and obtain business insights up to eighty percent faster compared to traditional ERP interfaces. The platform currently supports over three hundred ten AI-powered scenarios, with SAP committed to expanding this to four hundred scenarios by the end of 2025.
Research commissioned by SAP in partnership with Oxford Economics, surveying sixteen hundred executives across eight countries, found that organizations are experiencing strong returns from AI investments. Specifically, ninety-four percent of business leaders report that AI is improving innovation within their organizations, while the average return on AI investments has reached sixteen percent and is projected to nearly double within the next two years.
3. Microsoft Dynamics 365 with Copilot: Integrated Business Applications Intelligence
Microsoft has leveraged its dominant position in enterprise productivity software to create one of the most comprehensive AI-enabled business application suites available today. Microsoft Dynamics 365, integrated with Microsoft 365 Copilot and powered by the latest OpenAI models including GPT-5.2, represents a unified approach to embedding intelligence across customer relationship management, enterprise resource planning, and business process automation.
The Copilot Advantage: Unified AI Across the Enterprise
Microsoft’s strategic advantage lies in the seamless integration between Dynamics 365 business applications, Microsoft 365 productivity tools, Power Platform for custom development, and Azure cloud infrastructure. This integration creates what Microsoft calls the “Frontier Firm” model—organizations that are human-led and agent-operated, where every employee works alongside AI assistants and business processes are reimagined through intelligent automation.
The Copilot experience in Dynamics 365 operates consistently across all applications while adapting to specific business contexts. In Dynamics 365 Sales, Copilot helps sellers dramatically reduce time spent on clerical tasks by automatically drafting email responses enriched with customer data from the CRM, generating concise summaries of sales calls, and using predictive AI to guide sellers toward deals most likely to close. The Sales Qualification Agent, enhanced in the 2025 release wave, autonomously researches and engages with leads, moving them closer to full qualification and boosting opportunity pipelines with greater precision.
For customer service, Dynamics 365 Contact Center and Customer Service applications leverage Copilot to enhance agentic capabilities for case management, knowledge creation, and AI-driven routing. Service representatives receive contextual answers to customer queries drafted in real-time, powered by semantic search across knowledge bases and historical case data. The system can automatically summarize case resolutions to build institutional knowledge, empowering new agents and scaling service operations efficiently.
In finance, Dynamics 365 Finance introduces AI agents that accelerate financial close processes and provide intelligent reconciliation capabilities. The Account Reconciliation Agent now supports not only voucher amount mismatches but also ledger-to-subledger discrepancies. Rather than relying on manual exception handling and static reports, the agent continuously reviews all transactions, identifies exceptions, and presents recommended resolution actions that finance teams can accept or modify based on their judgment.
Copilot Studio: Democratizing AI Agent Creation
Microsoft Copilot Studio represents a paradigm shift in how enterprises create and deploy AI capabilities. The platform enables both technical and non-technical users to build custom AI agents without extensive programming knowledge, dramatically accelerating AI adoption across organizations.
The studio provides intuitive interfaces for designing agent behaviors, defining knowledge sources, and orchestrating multi-agent collaboration. Organizations can create role-based AI assistants tailored to specific job functions—for example, a procurement assistant that understands supplier contracts, negotiation history, and purchasing policies, or a compliance assistant that monitors regulatory requirements and flags potential violations across different business units.
Microsoft introduced Copilot Tuning in 2025, a low-code capability that enables organizations to fine-tune AI models using their own company data, workflows, and processes without requiring teams of data scientists or weeks of development work. This capability ensures that AI agents understand company-specific terminology, policies, and processes, delivering more accurate and contextually appropriate responses than generic AI models could provide.
The platform also supports multi-agent orchestration, enabling different AI agents to collaborate on complex tasks while maintaining human oversight. For example, a product launch workflow might involve a marketing agent that creates campaign content, a sales agent that identifies target accounts and drafts outreach messages, a finance agent that develops pricing and revenue models, and a project management agent that coordinates timelines and resource allocation across teams. These agents work together, with humans providing strategic direction and approving key decisions while the agents handle execution details.
Work IQ: Contextual Intelligence Layer
Microsoft introduced Work IQ at Ignite 2025 as the intelligence layer that enables Microsoft 365 Copilot and Dynamics agents to understand individual users, their roles, and their organizational context. Work IQ provides agents with access to organizational memory and lifecycle context, enabling more personalized and adaptive automation that reduces repetitive tasks.
This contextual awareness means that agents can deliver tailored responses without requiring users to repeatedly provide the same background information. A sales agent interacting with a territory sales manager will automatically understand that manager’s accounts, team members, quota targets, and current pipeline status. When that same manager asks about deal strategies, the agent can provide recommendations grounded in the specific characteristics of deals in that territory, competitive landscape, and historical win patterns.
Power Platform Integration: Custom Business Solutions
The integration between Dynamics 365 and Microsoft Power Platform enables organizations to extend standard applications with custom logic, workflows, and user interfaces tailored to their unique requirements. Power Apps now incorporates AI capabilities that assist with app creation, enabling users to describe what they need in natural language or share screenshots of desired functionality, with AI generating initial app structures automatically.
Power Automate has evolved with smarter automation tools including generative AI actions for content creation, intelligent document processing that can extract structured data from unstructured sources, and enhanced human-in-the-loop experiences that ensure appropriate oversight for critical decisions. The platform’s integration with Copilot Studio means that automated workflows can invoke AI agents as needed, creating sophisticated orchestrations that blend traditional business process automation with modern agentic AI capabilities.
Agent 365: Governance and Control
Recognizing that enterprises require robust governance as AI adoption scales, Microsoft introduced Agent 365 as a unified control plane for managing AI agents across the Microsoft ecosystem. This framework provides IT teams with visibility into all agents deployed across Microsoft 365 and Power Platform, with capabilities to manage agent lifecycles, monitor agent activities, enforce security policies, and ensure compliance with organizational standards.
Agent 365 addresses a critical enterprise concern—how to enable business units to create and deploy AI agents rapidly while maintaining appropriate oversight and control. The platform enforces role-based access controls, maintains audit logs of agent actions, and provides analytics on agent performance and business impact.
Pricing and Enterprise Adoption
Microsoft Dynamics 365 follows a modular pricing approach where organizations pay for specific applications and add-on capabilities. Base licenses for applications such as Dynamics 365 Sales or Customer Service start at several hundred dollars per user annually, with AI capabilities included in higher-tier licenses or available as add-ons.
The company has seen remarkable enterprise adoption, with over ninety percent of Fortune 500 companies now using Microsoft 365 Copilot. The tight integration with existing Microsoft investments provides a compelling value proposition, as organizations can extend AI capabilities across their entire digital workplace without introducing completely new platforms that require separate training, governance, and support structures.
4. Oracle Cloud Infrastructure and AI Enterprise: Infrastructure and Intelligence Combined
Oracle has emerged as a critical player in enterprise AI through its unique positioning as both an infrastructure provider and enterprise application vendor. Oracle Cloud Infrastructure has become the platform of choice for some of the world’s most demanding AI workloads, while Oracle’s enterprise applications increasingly leverage AI to transform business processes.
OCI: Purpose-Built for AI at Scale
Oracle Cloud Infrastructure has distinguished itself through performance characteristics specifically optimized for artificial intelligence and machine learning workloads. The platform offers GPU-enabled instances powered by NVIDIA’s latest architectures including Hopper H100/H200 and next-generation Blackwell GB200 systems. OCI Superclusters can scale to configurations with up to one hundred thirty-one thousand seventy-two GPUs, providing the massive compute resources required for training large language models and running complex inference workloads.
Oracle’s infrastructure advantage stems from its network architecture. The company has invested heavily in ultra-low-latency networking that connects GPUs with minimal overhead, a critical requirement for distributed AI training where communication between compute nodes can become a bottleneck. Early benchmarks show that OCI’s Blackwell GPU deployments achieve twelve point six times improvement over previous-generation A100 GPUs in MLPerf Inference benchmarks, demonstrating significant performance gains that translate directly to faster model training and lower operational costs.
The platform’s distributed cloud capability enables organizations to deploy AI workloads across OCI’s public regions, government clouds, sovereign clouds, OCI Dedicated Region, Oracle Alloy for partner deployments, OCI Compute Cloud@Customer for on-premises scenarios, and even OCI Roving Edge Devices for remote locations. This flexibility addresses diverse enterprise requirements around data sovereignty, regulatory compliance, and latency sensitivity.
NVIDIA AI Enterprise on OCI
Oracle’s partnership with NVIDIA extends beyond hardware provisioning to include comprehensive software integration. NVIDIA AI Enterprise—an end-to-end, cloud-native software platform that accelerates data science and streamlines AI deployment—is available natively through the OCI Console. This integration provides access to more than one hundred sixty AI tools and frameworks optimized for NVIDIA hardware, reducing the complexity and time required to deploy generative AI models in production.
Unlike marketplace offerings from other cloud providers where NVIDIA AI Enterprise operates as a separate service, Oracle’s native integration enables direct billing, unified customer support, and streamlined deployment workflows. Organizations can purchase NVIDIA AI Enterprise using their existing Oracle Universal Credits, simplifying procurement and budget management.
Oracle AI Data Platform: Unified Foundation for Enterprise AI
Announced at Oracle AI World 2025, the Oracle AI Data Platform provides an enterprise-ready foundation that unifies data management and AI capabilities in a single, integrated environment. The platform addresses one of the most significant challenges enterprises face when implementing AI—ensuring that AI models have access to clean, comprehensive, and current data from across the organization.
The platform supports open data formats including Delta Lake and Apache Iceberg, eliminating data duplication and enabling organizations to build data lakehouses using industry-standard technologies. Zero-ETL and Zero-Copy capabilities enable seamless connections to critical business application data from Oracle Fusion Cloud applications including finance, human resources, supply chain, marketing, sales, and service applications, as well as connections to existing enterprise databases and external data sources.
Oracle AI Data Platform catalog provides unified governance across all data and AI assets, helping organizations maintain compliance with regulatory requirements and internal policies. The catalog supports open standards including Agent2Agent and Model Context Protocol, enabling sophisticated multi-agent systems that can orchestrate workflows across different applications and data sources.
Oracle Fusion Cloud: AI-Native Business Applications
Oracle has integrated AI capabilities across its Fusion Cloud application suite, with over four hundred AI features now embedded in applications spanning ERP, HCM, supply chain management, and customer experience. These AI capabilities leverage the rich business context and data available in Oracle applications to deliver intelligent automation and insights that understand industry-specific requirements and best practices.
In Oracle Fusion Cloud ERP, AI capabilities include intelligent document processing for invoice capture and processing, predictive analytics for cash flow forecasting, anomaly detection for fraud prevention, and automated reconciliation that can identify and resolve discrepancies between accounting systems. The platform can automatically suggest journal entries based on historical patterns and current business context, dramatically reducing manual data entry requirements.
Oracle Fusion Cloud HCM incorporates AI for talent acquisition, including intelligent candidate matching that considers not only explicit qualifications but also cultural fit indicators derived from analyzing successful employee profiles. The system can predict employee attrition risk by analyzing engagement patterns, performance trends, and external factors, enabling proactive retention interventions.
AI Agent Studio and Agentic Capabilities
Oracle AI Agent Studio, introduced at no additional cost to Fusion Cloud customers, enables organizations to create custom AI agents tailored to their specific business processes. The studio provides tools for building conversational AI experiences, integrating with Oracle and third-party systems, and deploying agents that can execute multi-step workflows autonomously.
Oracle has emphasized agent-to-agent collaboration capabilities, where multiple specialized agents work together to accomplish complex objectives. The platform includes governance frameworks that provide visibility into how agents make decisions, identify bottlenecks and compliance issues, and enable organizations to optimize agent behaviors for maximum efficiency and impact.
Strategic Partnerships and Ecosystem
Oracle’s partnership strategy extends beyond NVIDIA to include collaborations with leading AI innovators. The company has secured major AI infrastructure contracts, including a reported three hundred billion dollar agreement with OpenAI linked to Project Stargate, positioning Oracle as a critical infrastructure provider for next-generation AI systems.
Oracle’s Remaining Performance Obligations—a measure of future contracted revenue—surged to five hundred twenty-three billion dollars, significantly driven by new commitments from major AI players including NVIDIA and Meta Platforms. This massive backlog demonstrates the scale at which enterprises are committing to Oracle’s infrastructure for AI workloads.
Enterprise Considerations
Oracle’s pricing model for AI capabilities varies significantly based on workload characteristics and deployment models. OCI provides usage-based pricing for compute, storage, and networking resources, with discounts available through Oracle Universal Credits programs. Organizations running large-scale AI workloads often negotiate custom pricing arrangements that reflect their specific requirements and commitment levels.
The platform’s enterprise focus means that Oracle emphasizes features critical for large organizations, including comprehensive security frameworks with zero-trust architecture, advanced encryption, extensive compliance certifications, and global infrastructure supporting data residency requirements across multiple jurisdictions.
5. Workday Illuminate and Agentic AI: Human Capital and Financial Intelligence
Workday has established itself as the definitive platform for human capital management and financial management in large enterprises, serving over eleven thousand organizations globally including more than sixty percent of the Fortune 500. The company’s strategic focus on AI, culminating in the Workday Illuminate platform and comprehensive suite of agentic AI capabilities, positions Workday as the system of intelligence for managing people, money, and increasingly, AI agents.
Workday Illuminate: Purpose-Built AI for Enterprise Operations
Workday Illuminate represents the company’s comprehensive AI engine, powering products and AI agents across the entire Workday platform. Unlike generic AI tools, Illuminate leverages Workday’s deep understanding of business processes, extensive business data from millions of users, and specialized models trained specifically for human resources and financial management use cases.
The platform operates on a unified data foundation that integrates information about full-time employees, contingent workers, financial transactions, operational metrics, and business outcomes. This comprehensive data model enables Illuminate to understand complex relationships—for example, how workforce composition affects operational costs, how employee satisfaction correlates with customer satisfaction, or how skills gaps impact project delivery timelines.
Illuminate capabilities span multiple domains. In talent management, AI powers intelligent job recommendations that match employees to internal opportunities based on skills, aspirations, and career trajectory analysis. The system can identify hidden talent within organizations—employees whose capabilities may not be fully utilized in current roles but who possess skills needed elsewhere in the business. Onboarding processes benefit from AI-powered personalization that adapts new employee experiences based on role requirements, team dynamics, and individual learning preferences.
Agentic AI: Autonomous Workforce and Financial Operations
Workday has introduced a comprehensive suite of AI agents that autonomously execute complex, multi-step workflows across recruiting, payroll, financial operations, and workforce management. These agents represent a fundamental shift from AI as an assistive technology to AI as an active participant in business processes.
The Recruiting Agent, enhanced by Workday’s acquisition of Paradox in 2025, uses conversational AI to simplify every step of the job application journey. Candidates can interact with the agent through natural language across multiple channels—company career sites, text messages, or messaging applications—to ask questions about positions, submit applications, schedule interviews, and receive updates on application status. The agent operates twenty-four hours a day across time zones, ensuring that candidate experiences remain positive and consistent regardless of when prospects engage with the company. Organizations using the Recruiting Agent report significant improvements in time-to-fill metrics and candidate satisfaction scores.
The Payroll Agent monitors payroll processing workflows to identify potential errors, compliance issues, and anomalies before paychecks are issued. The agent can automatically flag discrepancies such as unusual overtime patterns, missing deductions, or calculations that don’t align with employment contracts and applicable regulations. By catching issues proactively, organizations reduce costly payroll corrections and improve employee satisfaction by ensuring accurate, timely compensation.
The Contingent Sourcing Agent addresses the growing importance of contingent workers in enterprise workforce strategies. With data unified from Workday HCM and VNDLY—Workday’s platform for managing contract workers acquired in recent years—the agent provides a complete view of the entire workforce. The agent can identify skills gaps that might be filled through contingent staffing, suggest appropriate contractors from historical performance data, manage the sourcing and negotiation process, and ensure that contingent worker relationships comply with classification regulations and contractual terms.
Workday Spring 2025 Release: Three Hundred Fifty Plus Enhancements
Workday’s Spring 2025 release, shaped directly by customer feedback, delivered over three hundred fifty product features and updates that demonstrate the company’s commitment to embedding AI throughout the platform while also addressing core functional requirements.
New AI Gateway capabilities enable developers to use Workday AI APIs to build intelligent applications with Workday Extend Professional. The Data Query capability leverages AI to simplify data retrieval through natural language queries, enabling users to extract insights without understanding complex data models or writing queries in technical languages. Document Intelligence empowers users to extract important information from unstructured documents including supplier agreements, customer contracts, pay slips, and employee records, automatically populating structured fields in Workday applications.
For frontline workers—a demographic increasingly critical to business operations yet historically underserved by traditional enterprise software—Workday introduced Time Kiosk capabilities that allow workers to clock in without internet connectivity and easily track time across different positions or shifts. Time-Tracking Intelligent Prompts leverage AI to accelerate time entry by suggesting durations based on typical patterns for individual workers, making time tracking faster and reducing errors from manual entry.
Integration and Ecosystem Strategy
Workday has pursued strategic integrations that extend the platform’s reach beyond traditional HCM and financial management boundaries. The company announced plans to acquire Pipedream, an integration platform offering over three thousand built-in application connectors and more than ten thousand tools, triggers, and actions for building workflows and powering AI agents. This acquisition will enable Workday customers to connect information and workflows from across their technology ecosystems—including tools such as Asana, HubSpot, Jira, Salesforce, and Slack—directly into Workday and take actions in external applications without leaving the Workday interface.
The platform’s partner ecosystem extends Workday capabilities to global payroll through Workday Global Payroll Network and Global Payroll Connect, providing seamless integration with third-party payroll providers in over one hundred eighty countries. This global reach enables multinational organizations to manage worldwide workforce operations through a single system of record while accommodating local payroll requirements, tax regulations, and compliance mandates.
Workday Wellness partners leverage real-time data exchange capabilities, allowing organizations to gain insights on benefits usage and employee sentiment that inform data-driven decisions about wellness programs and benefit offerings. This integration between HR systems and wellness platforms creates a more holistic view of employee wellbeing and enables more targeted interventions to support workforce health.
Workday Extend: Customization and Application Development
Workday Extend provides a low-code development platform that shares Workday’s standard data model and security framework, enabling organizations and partners to build custom applications that address unique business needs without introducing data silos or compromising security. AI capabilities in Extend can recommend optimal approaches to building applications, analyze log data to identify areas for improvement, and even generate initial application structures based on natural language descriptions of requirements.
The Extend marketplace features applications built by Workday partners that address industry-specific requirements—for example, healthcare-specific credential management, retail workforce scheduling optimization, or financial services compliance monitoring. These applications benefit from Workday’s continuous update model, meaning they remain compatible with new platform releases without requiring extensive testing and remediation efforts.
Recognition and Market Position
Workday was named a Leader in the 2025 Gartner Magic Quadrant for Cloud HCM Suites for 1,000+ Employee Enterprises for the tenth consecutive year, positioned highest for Ability to Execute. The company also received Leader designations in Gartner Magic Quadrants for Cloud ERP for Service-Centric Enterprises and Financial Planning Software, demonstrating strength across multiple enterprise software categories.
With subscription revenue of seven point seven billion dollars and year-over-year growth of nearly seventeen percent, Workday maintains strong financial performance while investing heavily in AI capabilities. The company serves more than sixty-two hundred HR customers globally, including prominent organizations across diverse industries from healthcare systems to automotive manufacturers to outdoor apparel brands.
6. ServiceNow: Intelligent Workflow Automation Across the Enterprise
ServiceNow has evolved from its origins as an IT service management platform into a comprehensive workflow automation solution that spans IT operations, customer service, human resources, and enterprise service management. The company’s AI capabilities, powered by its Now Platform and enhanced through strategic acquisitions and partnerships, enable organizations to automate complex business processes and deliver intelligent services at scale.
AI-Powered IT Service Management
ServiceNow’s core strength lies in IT service management, where AI capabilities dramatically improve incident resolution, change management, and service delivery. Natural language processing enables employees to report IT issues conversationally through chat interfaces, with AI automatically categorizing, prioritizing, and routing tickets to appropriate support teams. Virtual agents can resolve common issues autonomously—password resets, access requests, software installations—without human intervention, significantly reducing resolution times and support costs.
Predictive analytics identify patterns in incident data that suggest underlying infrastructure problems before they cause widespread outages. The platform can automatically trigger preventive maintenance workflows when degradation patterns are detected, shifting organizations from reactive firefighting toward proactive operations management. Machine learning models trained on historical resolution data recommend solutions to support agents based on issue characteristics, dramatically reducing the time required to resolve complex technical problems.
Employee Workflows and HR Service Delivery
ServiceNow extends beyond IT to provide intelligent automation for human resources processes including employee onboarding, benefits administration, case management, and lifecycle transitions. AI-powered chatbots handle routine HR inquiries about policies, benefits eligibility, time-off balances, and organizational information, freeing HR professionals to focus on strategic initiatives and complex employee situations requiring human judgment.
The platform can automatically route HR cases to appropriate specialists based on issue type, employee location, and urgency. Knowledge management capabilities powered by AI ensure that self-service resources remain current and helpful, with the system identifying knowledge gaps when employees repeatedly ask questions not adequately addressed by existing documentation.
Customer Service Management
Organizations leverage ServiceNow for customer service operations, with AI capabilities enabling omnichannel support across web, mobile, chat, email, and voice channels. Virtual agents provide initial customer assistance, gathering information, troubleshooting issues, and escalating to human agents only when necessary. The platform maintains complete context as interactions move between channels or from virtual to human agents, ensuring customers don’t need to repeat information.
Sentiment analysis monitors customer communications to identify frustrated or at-risk customers, automatically escalating these cases to experienced agents who can prevent customer churn. Post-interaction surveys and feedback analysis inform continuous improvement in both AI agent behaviors and human agent training.
Industry-Specific Applications
ServiceNow has developed industry-specific applications that incorporate AI capabilities tailored to sector requirements. Healthcare organizations use ServiceNow for patient appointment scheduling, care coordination, and medical device management. Financial services firms leverage the platform for regulatory compliance tracking, risk assessment workflows, and customer onboarding that satisfies know-your-customer requirements. Telecommunications companies employ ServiceNow for network operations management, field service dispatch, and subscriber lifecycle management.
Integration and Ecosystem
ServiceNow’s extensive integration capabilities enable the platform to serve as an intelligent orchestration layer connecting diverse enterprise systems. Pre-built integrations with leading ERP, CRM, ITSM, and communication platforms ensure that ServiceNow can access and act upon data across the technology landscape. The platform’s API-first architecture enables custom integrations when pre-built connectors don’t exist.
Enterprise Adoption Considerations
ServiceNow pricing reflects its positioning as an enterprise-grade workflow automation platform, with costs scaling based on the number of users, volume of service requests, and complexity of workflows implemented. Large enterprises often negotiate custom pricing arrangements that reflect their specific requirements and commitment levels.
Organizations implementing ServiceNow report significant returns on investment through reduced resolution times, improved employee satisfaction with internal services, and the ability to scale service delivery without proportional increases in support staff. The platform’s maturity and extensive ecosystem make it a relatively low-risk choice for enterprises seeking to modernize service delivery across IT, HR, and customer-facing functions.
7. Google Vertex AI: Comprehensive Machine Learning Platform
Google Vertex AI represents Google Cloud’s unified platform for building, deploying, and scaling machine learning models. Designed for organizations with established data science teams requiring full control over the ML lifecycle, Vertex AI combines comprehensive tooling with Google’s deep AI expertise and infrastructure capabilities.
Unified ML Platform Architecture
Vertex AI consolidates previously fragmented Google Cloud AI services into a cohesive platform that supports the entire machine learning lifecycle. Data scientists can prepare datasets, select from pre-built or custom algorithms, train models at scale, evaluate performance, deploy to production endpoints, and monitor ongoing model behavior—all within a unified environment.
The platform offers both AutoML capabilities for users seeking rapid development without extensive ML expertise and custom model training options for data scientists who want fine-grained control over model architectures, hyperparameters, and training procedures. This flexibility accommodates diverse skill levels and project requirements within a single platform.
Pre-Built AI Capabilities
Vertex AI provides access to pre-trained models for common tasks including image recognition, natural language processing, video analysis, and structured data prediction. Organizations can use these models directly with minimal customization or fine-tune them on proprietary data to improve accuracy for specific use cases.
The platform’s natural language capabilities enable sentiment analysis, entity extraction, content classification, and language translation across more than one hundred languages. Vision capabilities support object detection, image classification, optical character recognition, and content moderation. These pre-built capabilities significantly accelerate development timelines for common AI use cases.
Model Garden and Generative AI
Google has integrated generative AI capabilities extensively throughout Vertex AI. Model Garden provides access to Google’s proprietary foundation models including PaLM for language tasks and Imagen for image generation, as well as selected third-party models. Organizations can experiment with different models to identify optimal options for their requirements without managing separate platforms or API integrations.
Generative AI Studio enables developers to prototype applications using large language models through intuitive interfaces that don’t require extensive coding. Prompt design tools help optimize model inputs for desired outputs, while grounding capabilities ensure that generated content remains factually accurate by connecting models to authoritative data sources.
Integration with Google Cloud Ecosystem
Vertex AI integrates deeply with other Google Cloud services, leveraging BigQuery for data warehousing, Cloud Storage for artifact management, Dataflow for data preprocessing, and Cloud Run for serverless deployment. This tight integration enables organizations to build end-to-end ML workflows that span data ingestion, transformation, training, deployment, and monitoring using Google’s comprehensive cloud platform.
Google Workspace integration enables AI capabilities to be surfaced directly in productivity applications. Organizations can deploy custom models that analyze documents in Google Drive, extract insights from Sheets data, or enhance Slides presentations with generated content.
Responsible AI and Governance
Google emphasizes responsible AI practices throughout Vertex AI, providing tools for detecting and mitigating bias in training data and model predictions, explaining model decisions through interpretability features, and ensuring models behave reliably across diverse population segments. Model monitoring capabilities track prediction accuracy, data drift, and feature importance over time, alerting teams when models degrade and require retraining.
The platform’s enterprise-grade security includes identity and access management integration, encryption of data at rest and in transit, VPC service controls for network isolation, and comprehensive audit logging. Organizations operating in regulated industries can demonstrate compliance with data protection and AI governance requirements through Vertex AI’s built-in capabilities.
Enterprise Use Cases and Adoption
Financial services firms leverage Vertex AI for fraud detection models that analyze transaction patterns in real-time, credit risk assessment that incorporates alternative data sources, and algorithmic trading strategies. Healthcare organizations use the platform for medical image analysis, patient outcome prediction, and drug discovery applications. Retail companies deploy Vertex AI for demand forecasting, dynamic pricing optimization, and personalized product recommendations.
Vertex AI pricing follows Google Cloud’s consumption-based model, charging for compute resources during training and prediction, storage for datasets and models, and API calls for managed services. Organizations can optimize costs by selecting appropriate machine types, using preemptible instances for fault-tolerant workloads, and carefully managing resource utilization.
8. Microsoft Azure AI: Enterprise-Grade Intelligence Platform
Microsoft Azure AI provides a comprehensive suite of artificial intelligence services that integrate seamlessly with the broader Microsoft ecosystem, enabling organizations to build intelligent applications, automate processes, and generate insights from data. Azure AI’s strength lies in its enterprise focus, providing tools that balance cutting-edge capabilities with the security, compliance, and scalability requirements of large organizations.
Azure OpenAI Service: Generative AI for Enterprise
Azure OpenAI Service provides access to OpenAI’s most powerful models including GPT-4, GPT-4 Turbo, and GPT-5 through Microsoft’s trusted cloud infrastructure. This service enables organizations to leverage cutting-edge generative AI capabilities while benefiting from Azure’s enterprise-grade security, regional availability, and compliance certifications.
The service includes content filtering capabilities that automatically detect and block potentially harmful content, ensuring that applications remain appropriate for business contexts. Rate limiting and quota management features provide cost control and prevent unexpected expenses. Organizations can deploy models in specific Azure regions to satisfy data residency requirements, a critical consideration for industries with strict regulatory oversight.
Azure OpenAI Service integrates with Azure Cognitive Search to enable Retrieval-Augmented Generation patterns, where large language models are grounded in proprietary documents and data sources. This approach significantly reduces hallucination risks while enabling models to provide accurate, source-attributed answers based on enterprise knowledge.
Azure Machine Learning: Comprehensive ML Platform
Azure Machine Learning provides end-to-end capabilities for building, training, and deploying machine learning models at scale. The platform supports both code-first experiences for data scientists comfortable with Python or R, and low-code interfaces for citizen data scientists developing models through visual workflows.
AutoML capabilities automate feature engineering, model selection, and hyperparameter tuning, enabling rapid development of accurate models even for users without deep machine learning expertise. The platform can automatically try dozens or hundreds of model types and configurations, identifying optimal approaches for specific datasets and prediction tasks.
MLOps capabilities streamline the transition from experimentation to production, with version control for data and models, automated testing pipelines, deployment templates, and monitoring dashboards. Models can be deployed to various endpoints including real-time inference APIs, batch scoring services, or edge devices, with Azure managing infrastructure provisioning and scaling automatically.
Cognitive Services: Pre-Built AI Capabilities
Azure Cognitive Services provides pre-trained AI models for common tasks, accessible through REST APIs that require minimal machine learning expertise. Vision services support image analysis, face detection, optical character recognition, and custom vision model training. Language services enable sentiment analysis, key phrase extraction, named entity recognition, and language translation. Speech services provide transcription, text-to-speech synthesis, speaker recognition, and real-time translation.
These pre-built services significantly accelerate application development for standard AI use cases. Organizations can integrate sophisticated AI capabilities into applications with just a few API calls, avoiding the complexity and cost of training custom models from scratch.
Azure AI Studio: Unified Development Environment
Azure AI Studio, launched in 2024 and continuously enhanced throughout 2025, provides a unified development environment for building generative AI applications. The studio brings together capabilities from Azure OpenAI Service, Azure Machine Learning, Azure Cognitive Search, and other Azure AI services in a cohesive interface designed for enterprise AI development.
Prompt flow capabilities enable developers to visually design, test, and refine complex prompt sequences that orchestrate multiple AI models and data sources. Evaluation tools assess model performance across diverse test cases, identifying weaknesses and comparing different approaches. Deployment templates simplify the process of moving applications from development to production with appropriate monitoring and governance.
Integration with Microsoft Ecosystem
Azure AI’s deepest value proposition for many organizations lies in its integration with other Microsoft products. Power Platform enables citizen developers to incorporate Azure AI capabilities into custom applications and workflows without coding. Dynamics 365 leverages Azure AI for intelligent business processes. Microsoft 365 Copilot uses Azure AI infrastructure to deliver intelligent assistance across Office applications.
This integration means that organizations already committed to Microsoft technology stacks can extend AI capabilities across their entire digital environment with consistent security models, unified identity management, and streamlined vendor relationships.
Industry-Specific Solutions
Microsoft has developed industry-specific AI solutions built on Azure infrastructure. Microsoft Cloud for Healthcare includes AI capabilities for clinical documentation, patient engagement, and care coordination. Microsoft Cloud for Financial Services provides AI for fraud detection, regulatory compliance, and personalized banking experiences. Microsoft Cloud for Retail enables demand forecasting, personalized recommendations, and intelligent supply chain management.
These industry solutions accelerate time-to-value by providing reference architectures, compliance frameworks, and pre-built models tailored to sector requirements, reducing the customization work required to address industry-specific challenges.
Enterprise Considerations
Azure AI pricing varies significantly based on specific services used, resource consumption patterns, and commitment levels. Organizations can often reduce costs through Azure Reserved Instances that provide discounts in exchange for one-year or three-year commitments, or through Azure Hybrid Benefit that allows use of existing software licenses.
For organizations already invested in Microsoft technologies, Azure AI often represents the path of least resistance for AI adoption, leveraging existing relationships, contracts, and technical expertise while avoiding the complexity of multi-vendor AI strategies.
9. DataRobot: Automated Machine Learning for Business Users
DataRobot has established itself as the leading AutoML platform, enabling organizations to develop, deploy, and manage machine learning models without requiring deep technical expertise. The platform democratizes AI by making sophisticated modeling techniques accessible to business analysts, domain experts, and citizen data scientists while providing the rigor and governance required for enterprise deployments.
Automated Model Development
DataRobot’s core value proposition lies in automating the most time-consuming and technically complex aspects of machine learning. Users upload datasets and specify prediction targets, then DataRobot automatically performs feature engineering, tests hundreds of algorithms, optimizes hyperparameters, and generates ensemble models that often outperform manually developed approaches.
The platform provides transparent explanations of what features drive predictions, how different models compare in terms of accuracy and interpretability, and what tradeoffs exist between model complexity and performance. This transparency enables business users to make informed decisions about which models to deploy without requiring deep statistical knowledge.
Pre-Built Industry Templates
DataRobot offers industry-specific templates and pre-configured models for common business problems. Financial services templates address credit risk assessment, fraud detection, and customer lifetime value prediction. Healthcare templates support patient readmission risk, treatment recommendation, and claims processing optimization. Retail templates enable demand forecasting, price optimization, and customer segmentation.
These templates incorporate best practices and domain knowledge accumulated from thousands of customer deployments, significantly reducing the time and expertise required to develop effective models for standard use cases.
MLOps and Governance
DataRobot provides comprehensive MLOps capabilities that address the operational challenges of maintaining hundreds or thousands of models in production. Automated monitoring detects data drift, concept drift, and accuracy degradation, alerting teams when models require retraining. The platform can automatically retrain and redeploy models on scheduled intervals or when performance thresholds are breached.
Governance features include model documentation that satisfies regulatory requirements, audit trails showing who developed and approved models, bias detection that identifies potential fairness issues, and challenger analysis that compares new models against existing baselines. These capabilities are particularly valuable in regulated industries including banking, insurance, and healthcare where model governance is subject to regulatory scrutiny.
Integration and Deployment Flexibility
DataRobot supports diverse deployment options including REST APIs for real-time predictions, batch scoring for large datasets, edge deployment for offline scenarios, and export to other platforms. Models can be deployed to AWS, Azure, Google Cloud, or on-premises infrastructure based on organizational requirements.
The platform integrates with common data sources including SQL databases, data warehouses such as Snowflake, and cloud storage services. ETL tool integrations enable DataRobot to operate within existing data pipelines without requiring extensive re-architecture.
Business Impact and Use Cases
Organizations across industries leverage DataRobot for mission-critical applications. Insurance companies use the platform for underwriting automation, claims triage, and loss prediction. Manufacturers deploy DataRobot for predictive maintenance, quality control, and supply chain optimization. Healthcare providers apply the platform to sepsis prediction, patient flow optimization, and provider network adequacy analysis.
A key differentiator is DataRobot’s focus on time-to-value. Organizations often deploy their first production models within weeks rather than months, with business users able to iterate and improve models based on feedback without waiting for data science teams to implement changes.
Enterprise Considerations
DataRobot pricing typically involves annual subscriptions based on the number of users and scale of deployments. The platform’s value proposition is particularly compelling for organizations facing data science talent shortages, as it enables existing business analysts and domain experts to develop sophisticated models that previously would have required specialized data science expertise.
10. UiPath: Intelligent Process Automation and Agentic RPA
UiPath has evolved from a leader in robotic process automation into a comprehensive intelligent automation platform that combines traditional RPA with AI capabilities, document understanding, process mining, and orchestration of both human and digital workers. The company’s platform enables organizations to automate complex business processes that span multiple systems and require sophisticated decision-making.
AI-Powered Document Understanding
UiPath’s document understanding capabilities leverage computer vision and natural language processing to extract structured information from unstructured documents. The platform can process invoices, purchase orders, contracts, forms, and correspondence across diverse formats including PDFs, images, and email bodies.
Pre-trained models understand common document types without requiring organization-specific training data, while custom models can be developed for industry-specific documents or company-specific formats. Validation workflows enable human review of extracted data with confidence scores highlighting fields requiring verification, balancing automation efficiency with accuracy requirements.
Process Mining and Optimization
UiPath Process Mining analyzes event logs from enterprise systems to create detailed visualizations of how business processes actually execute, identifying bottlenecks, deviations from standard procedures, and opportunities for improvement. The technology reveals hidden inefficiencies that process owners often don’t recognize, such as unnecessary handoffs between departments, excessive rework loops, or compliance gaps.
Task mining capabilities observe how knowledge workers interact with applications, identifying repetitive tasks suitable for automation and capturing implicit process knowledge that exists in workers’ daily activities but isn’t documented in official procedures. This insight enables organizations to prioritize automation investments toward activities that will deliver maximum impact.
Automation Fabric and Orchestration
UiPath’s automation fabric provides centralized management for thousands of automation workflows across an enterprise. The platform orchestrates interactions between attended bots that assist human workers, unattended bots that run autonomously on scheduled intervals or triggered by events, and AI models that provide intelligent decision-making within automated processes.
Queue management capabilities enable asynchronous processing of work items, with intelligent prioritization based on business rules and deadlines. The platform can dynamically allocate work across available bot resources, scaling automation capacity to meet demand fluctuations without manual intervention.
Integration with Enterprise AI Platforms
UiPath has established partnerships and integrations with leading AI providers including Azure AI, AWS AI services, and Google Cloud AI. These integrations enable automation workflows to leverage capabilities such as sentiment analysis, image recognition, and natural language understanding from specialized AI services while UiPath manages the orchestration and business logic.
The platform’s cognitive skills marketplace provides pre-built AI components that automation developers can incorporate into workflows—for example, invoice data extraction, resume parsing, or chatbot integration—without developing these capabilities from scratch.
Center of Excellence and Governance
UiPath emphasizes the importance of Centers of Excellence for scaling automation enterprise-wide. The platform provides tools for discovery and prioritization of automation opportunities, standardized development methodologies, reusable component libraries, and governance frameworks that ensure automations maintain appropriate controls and comply with security policies.
Analytics dashboards provide visibility into automation performance, ROI measurement, and identification of underperforming bots that require optimization. This operational intelligence enables continuous improvement of automation portfolios based on empirical business impact data.
Enterprise Deployment Patterns
Organizations typically begin UiPath deployments with tactical automations addressing specific pain points—for example, automating invoice processing in accounts payable or application testing in IT. As teams develop expertise and demonstrate value, deployments expand to strategic process automation that fundamentally reimagines workflows across functions.
Successful enterprise deployments often involve hybrid approaches where UiPath automates repetitive tasks while humans focus on exceptions, creative problem-solving, and relationship management. This human-bot collaboration model delivers better outcomes than attempting fully autonomous automation for complex processes requiring judgment and empathy.
Conclusion: The Future of AI-Enabled Enterprise Software
The enterprise software landscape in 2026 represents a fundamental inflection point where artificial intelligence has transitioned from experimental technology to operational necessity. The platforms examined in this guide—from Salesforce Einstein and SAP Joule to Microsoft Dynamics 365, Oracle AI, Workday Illuminate, and others—demonstrate that enterprise AI has achieved production readiness, delivering measurable business value across diverse industries and use cases.
As AI capabilities continue advancing at extraordinary pace, the gap between organizations effectively leveraging these technologies and those that lag behind will widen dramatically. The time for enterprise AI adoption is now—not as a future-looking experiment, but as a present-day imperative for organizations committed to thriving in an increasingly intelligent, automated, and data-driven business environment.
