TL;DR: Agentic AI refers to next-generation AI systems (often called autonomous agents) that set and pursue goals on their own, unlike traditional AI which requires step-by-step instructions. Agentic AI systems combine autonomy, adaptability, and real-time decision-making to plan, execute, and learn from multi-step tasks. In practice, an agentic retail AI might monitor shelf stock and automatically trigger replenishment orders or adjust pricing without human prompts. The enterprise impact is profound: global companies gain higher efficiency and productivity as agents automate workflows end-to-end, working 24/7 and freeing staff for strategic work. Agentic AI extends traditional RPA by handling dynamic, unstructured processes across the organization. To adopt it successfully, firms should assess readiness (strategy, data, governance), pilot agents on key processes, and engage specialized AI consulting services. For example, leading firms now offer agentic AI consulting services – see our Top Agentic AI Consultants page – to guide enterprises through strategy, integration, and risk management.
Definition and Explanation: Agentic AI vs Traditional AI
Agentic AI marks a paradigm shift in artificial intelligence. In contrast to traditional “narrow” AI (or rule-based automation), agentic AI systems have agency: they autonomously perceive their environment, plan actions, and pursue goals with minimal human intervention. As one AI lab explains, agentic AI combines learning with real-time decision-making to plan and execute goals on its own, based on context and data, rather than only reacting to direct instructions. In other words, agentic systems are goal-driven instead of trigger-driven. For example, a user might set a high-level goal (such as “optimize our supply chain performance”), and the agentic AI will break this into tasks, evaluate options, and act to achieve the goal without step-by-step guidance.
Traditional AI systems – including classical machine-learning models, expert systems, and today’s chatbots – typically work within preset boundaries. They require detailed human instructions or labeled training data for each task, and they generally respond only when prompted. For instance, a rule-based inventory system may notify managers of a stockout (a “push” of information), but it won’t reorder items on its own. By contrast, an agentic AI proactively acts on insights. A Deloitte analyst notes that whereas a conventional AI chatbot simply responds to user queries, an agentic AI “break[s] a job down into discrete steps” and carries it out with virtually no human supervision.
Figure: Conceptual illustration of agentic AI autonomy
Agentic AI systems can set and pursue goals autonomously. Unlike a traditional AI assistant that waits for commands, an agentic system actively plans and executes tasks to meet objectives. This self-directed behavior – the defining “agency” of agentic AI – allows it to operate more like a skilled employee than a mere software tool.
In summary, traditional AI (and RPA) is typically reactive and narrow: it follows hard-coded rules or responds to prompts, often on one specific task. Agentic AI (autonomous agents), by contrast, is proactive and general: it adapts, learns, and coordinates multiple steps end-to-end to achieve a goal. As Gartner predicts, this distinction is driving real-world change: roughly one-third of enterprise software will incorporate agentic AI capabilities by 2028.
Comparison Table: Agentic AI vs Traditional AI
| Aspect | Traditional AI | Agentic AI (Autonomous Agents) |
|---|---|---|
| Autonomy | Minimal – follows explicit human instructions. Responds only when triggered. | High – plans and executes tasks without step-by-step directions. Acts on its own to achieve goals. |
| Goal Orientation | Task-based – built for narrow tasks or single objectives. | Goal-driven – humans set high-level objectives, and agents determine the steps to achieve them. |
| Adaptability | Limited – usually fixed after training or programming. Struggles with new scenarios. | Continuous learning – uses reinforcement and feedback loops to adapt to changes over time. |
| Decision-Making | Static models – often static outputs based on input patterns. Decisions are usually predefined. | Dynamic reasoning – evaluates context in real time, weighs options, and decides best actions. Builds models on-the-fly. |
| Interaction | Tools/Models – typically single-purpose (e.g., classification model, chatbot). Limited to one system. | Multi-modal – can use LLMs, vision, IoT, APIs and other tools together. Agents can even collaborate with other agents. |
| Scope of Tasks | Narrow – solves one problem at a time (e.g., image recognition, data lookup). | Broad – handles complex, multi-step workflows (e.g., end-to-end supply chain management). |
| Human Oversight | Often required – humans must monitor output and correct errors. | Minimal – designed to work with limited supervision. Human experts set goals and guardrails, but agents handle execution. |
| Examples | Traditional RPA bots, recommendation systems (Netflix suggestions), rule-based systems (chatbots like early Siri) | Autonomous IT or business agents, e.g. an AI that independently tests and fixes code, or a retail agent that manages inventory and pricing without being prompted. |
This comparison table highlights the key differences: traditional AI excels at specific, well-defined tasks, while agentic AI is built to act autonomously across broad objectives. As one industry analysis notes, agentic AI puts the “agency” into AI by giving it the power to choose actions to reach its goals.
Core Features of Agentic AI: Autonomy, Goal-Driven, Real-Time
Agentic AI systems share several defining characteristics that distinguish them from earlier AI:
- Autonomy: An agentic AI can operate independently once given a high-level objective. It does not need step-by-step human guidance. After receiving a goal, an autonomous agent determines when and how to act on its own. For example, Aerospike explains that an autonomous agent “decides its course of action and carries it out,” choosing the actions it needs to meet its goal. This self-direction is the core of “agency” – the system has the power to act and make decisions by itself.
- Goal-Orientation: Rather than passively processing inputs, agentic AI focuses on goals. Humans define what success looks like (e.g. maximize profit, resolve customer query, avoid stockouts), and the agent organizes complex objectives into subtasks. It then plans sequences of actions and carries them out to fulfill the overarching goal. As UiPath describes, agentic systems can “break down goals into sub-tasks and pursue them independently” and will “make decisions based on context and changing conditions”. In practice, this means the agent continuously evaluates its progress and adapts its plan to achieve the goal efficiently.
- Adaptability and Learning: Agentic AI is inherently adaptive. It learns from interactions and feedback, improving performance over time. Unlike static models, agentic systems use techniques like reinforcement learning and continuous training so they can adjust strategies on the fly. For instance, if an unexpected obstacle or new data appears, the agent revises its plan and persists. This flexibility ensures the AI remains effective in dynamic, real-world environments. Over time, an agent refines its behavior, reducing errors and increasing output quality.
- Context Awareness and Memory: Advanced agents have memory modules and rich context awareness. They can retain information across sessions and use knowledge from earlier steps to inform later decisions. For example, a coding agent might remember a developer’s style preferences or a retail agent might recall seasonal trends. This context lets agents handle longer workflows: they “perceive” their environment, draw on past experiences, and integrate data from multiple sources (APIs, databases, IoT sensors).
- Coordination and Multi-Agent Collaboration: Agentic systems can collaborate with other agents. In sophisticated implementations, multiple AI agents work together on complex tasks, each specializing in a part of the problem. They communicate or pass tasks among themselves and even report to a higher-level “manager” agent. For instance, a customer service system might have one agent dedicated to understanding intent, another handling data lookup, and a third managing follow-up actions, all coordinated seamlessly. This distributed agent architecture amplifies capabilities well beyond a single model.
These core features empower agentic AI to handle tasks far beyond the reach of traditional AI tools. In short, an agentic system combines the thinking of generative AI with action-taking: it senses an environment, reasons a plan, executes tasks, and learns from results to achieve goals with minimal human oversight.
Agentic AI in Action: Examples in Global Retail
Agentic AI’s autonomy and decision-making are particularly transformative in retail – an industry with complex, fast-moving workflows. Leading retailers are exploring autonomous agents to manage inventory, pricing, promotions, and more. Here are key examples:
- Autonomous Inventory and Replenishment: Imagine a grocery chain where AI agents continuously monitor shelves via cameras or sales data. When an agent detects a product running low (or a planned promotion affecting stock), it automatically triggers restocking orders without waiting for a report. SymphonyAI describes exactly this scenario: “Agents act independently, handling tasks like inventory restocking or promotion adjustments without human prompts”. In practice, an agentic retail system might use image recognition to detect out-of-stock items and then generate purchase orders or alert the supply chain team to send more goods. One example from industry is a system that “monitors shelf availability across stores and automatically triggers restock orders – no chatbots required”.
- Dynamic Pricing and Promotion Optimization: Agentic AI can also automate pricing and promotions. Instead of setting prices manually, an AI agent continuously analyzes sales performance, competitor prices, and inventory levels. It can then adjust discounts, markdowns, or promotions in real time to maximize revenue and margin. For instance, agents may simulate pricing scenarios (e.g. “what if we reduce Product A’s price by 5%?”) and autonomously implement the best promotions. SymphonyAI explains that agents analyze promotion effectiveness on the fly and “autonomously adjust promotion frequency, reallocate budgets to high-performing campaigns, and generate reports” to optimize outcomes. This removes guesswork and ensures pricing adapts to demand and competition instantly.
- Supply Chain Resilience: Beyond stores, agentic AI enhances the entire retail supply chain. Autonomous agents can predict demand shifts (like a spike in umbrellas when rain is forecast), reroute shipments around disruptions, or automatically shift inventory among warehouses. For example, if an overseas supplier is delayed, an AI agent might reconfigure the supply plan in real time. As one analyst notes, in logistics agentic AI “can analyze vast amounts of data in real-time, optimizing routes, predicting potential bottlenecks, and even adjusting inventory levels based on demand fluctuations”. In retail, this means fewer stockouts and lower waste.
- Customer Experience and Store Operations: Agentic AI can support customer-facing and in-store tasks. Virtual assistants powered by agents might proactively engage customers with personalized offers as they shop online or in-app, rather than simply answering queries. In stores, mobile robots or kiosks with agentic intelligence could guide shoppers to items or complete multi-step assistance tasks (e.g., find an out-of-stock item’s closest alternative across stores, then notify staff). While still emerging, these use cases illustrate the agentic trend: AI systems that do work for you, managing processes end-to-end without constant human oversight.
These examples underscore how agentic AI translates to real-world value in global retail. By automating critical workflows – from inventory management to dynamic pricing – across an international chain, retailers can scale operations efficiently and respond to market changes instantly. In all cases, agentic AI acts on data-driven insights, turning analytics into action. As one industry report highlights: instead of passive BI dashboards, agentic systems act on insights. For example, an agent might not only detect a stockout, but immediately resolve it by ordering more stock under business rules. The result is a self-correcting, proactive retail operation.
Benefits of Agentic AI for Global Enterprises
For global businesses – especially those with complex, cross-border operations – agentic AI offers sweeping benefits:
- Extended Automation and Efficiency: Agentic AI vastly broadens what can be automated. Tasks once beyond automation (like coordinating multi-step processes across departments) become feasible. Autonomous agents can take on decision-intensive work previously reserved for knowledge workers. This drives efficiency: companies free employees from routine work to focus on strategy and innovation. In effect, agentic AI adds “traditional time savings” to business processes by operating without constant supervision. Early implementations show agents offloading a significant workload – for instance, automating portions of software development or customer service, allowing human experts to handle exceptions.
- Productivity and Cost Savings: Since agents can run 24/7 without fatigue and scale across systems, firms see measurable productivity gains. Work that once required many people can be partly or fully handled by AI agents. Charter Global explains that agentic systems can manage entire processes independently, without proportional increases in headcount or infrastructure. This leads to lower operational costs. For example, agents can automate invoice processing, fraud checks, or compliance monitoring around the clock, reducing manual errors and delays. Over time, cost savings compound: as agents learn, they improve accuracy and throughput, further lowering overhead.
- Enhanced Decision-Making and Agility: Agentic AI serves as a connective tissue across data silos. It synthesizes information from CRM, ERP, IoT, and external sources to generate insights in real time. Agents can run simulations and scenario planning on live data (e.g. “what-if” analyses for new markets or products) and recommend or enact optimal next steps. This speed and scope of decision support helps multinational enterprises respond to global trends or disruptions faster. For instance, an agent might spot a rising demand for a product in one region and immediately trigger production adjustments or marketing campaigns. By turning data into action, agentic AI accelerates strategy execution worldwide.
- Scalability and Collaboration: Enterprises with operations in dozens of countries benefit from agentic AI’s ability to operate uniformly across geographies. Agents follow global business rules and compliance requirements while adapting to local contexts. Multiple agents in different units can collaborate: for example, agents in regional branches could coordinate to balance inventory or share insights. As FullStack notes, companies can deploy several AI agents that “coordinate” and work together, even speaking to a unified agent that reports on overall performance. This distributed intelligence scales enterprise functions intelligently as the company grows, without a linear increase in management complexity.
- Innovation and Competitive Edge: The autonomy and learning built into agentic AI drive continuous improvement. As agents operate, they identify bottlenecks and opportunities for process optimization. Enterprises that harness this see strategic advantages: faster time-to-market for new products, highly personalized customer experiences, and the ability to reengineer business models. Surveys and analyst reports suggest that firms already investing in agentic AI aim to use it for growth and productivity. For instance, Gartner predicts widespread adoption of agentic features in enterprise applications, highlighting the growing edge this technology gives early adopters.
In summary, agentic AI brings autonomy, adaptability, and intelligence to enterprise workflows. Businesses gain higher throughput and agility as AI agents handle decision loops. C-level leaders can leverage this by refocusing their workforce on creativity and strategy while trusting agents to run routine processes. As one industry analysis observes, agentic AI promises “improved scalability, reduced costs, and increased margins” for organizations that harness its capabilities.
AI Workflow Automation in Retail and Beyond
Agentic AI is not confined to a single tool; it augments AI workflow automation across retail and other industries. Traditional RPA (Robotic Process Automation) excels at repetitive, rule-based tasks, but it falters on dynamic, unstructured processes. Agentic AI picks up where RPA leaves off. It optimizes entire workflows by interlinking systems and making intelligent decisions along the way.
In retail, this means turning data flows into live action. For example, a pure RPA bot could record when inventory falls below a threshold. An agentic AI workflow, however, might automatically go through these steps: sense the stockout via IoT sensor or sale point data, reason whether to reorder or substitute products, execute the optimal choice (place orders or notify other stores), and verify outcomes – all in one loop. SymphonyAI puts it well: “imagine BI tools that don’t just flag a stockout but automatically resolve it”. This shift transforms insights into immediate action.
More broadly, agentic AI enables dynamic, end-to-end automation. In finance, an agentic workflow could autonomously reconcile complex transactions: it gathers data from banking APIs, identifies inconsistencies, and even initiates correction entries – without a human in the loop. In customer service, agents can triage requests, retrieve context from CRM systems, respond with personalized solutions, and learn from feedback (escalating only when needed). All these are examples of advanced AI workflow automation: agents orchestrate multiple steps and systems.
Crucially, agentic AI fits into existing automation ecosystems. Enterprises typically build on RPA, workflow engines, and AI co-pilots. As UiPath explains, agentic AI and “agentic automation” extend these capabilities: they “optimize complex, unstructured processes that traditional rules-based automation can’t address by itself”. In practice, companies integrate agents via API orchestration platforms or AI development frameworks. The agent can invoke RPA bots, cloud services, or microservices as sub-tools when needed, effectively evolving legacy workflows.
For global retailers, the phrase “AI workflow automation in retail” captures this transition. It means automating the entire retail workflow — from demand sensing to shelf stocking to customer interaction — through intelligent agents. Instead of static weekly reporting, agents continuously loop through data: they sense demand signals, update plans, purchase stock, adjust pricing and promotions, and even coordinate with logistics in real time. The result is a seamless end-to-end automation layer. As one strategist notes, agentic AI turns reactive systems into “proactive, profit-driving” processes by autonomously acting on insights.
In summary, agentic AI redefines workflow automation. Whether in retail, finance, manufacturing, or IT, autonomous agents can be built into business process flows. They consume real-time data, break tasks into sub-steps, and trigger actions without human prompts. This blurs the line between AI analysis and AI action, giving enterprises an automated value chain that continuously optimizes itself.
Evaluating and Adopting Agentic AI: The Role of AI Consulting Services
Introducing agentic AI into a business requires careful planning. Companies should follow a structured approach – often with the help of AI consulting specialists – to ensure success:
- Assess Readiness and Strategy: First, evaluate if agentic AI aligns with your strategic goals. Define clear use cases where autonomy adds value (e.g., global inventory, dynamic pricing). Assess data quality, infrastructure, and talent readiness. Many organizations underestimate this step: one study found 80% of AI project failures stem from non-technical gaps (misaligned strategy, poor data, or lack of stakeholder buy-in). As Sia Partners advises, moving to agentic AI “requires more than technology: it requires strategic alignment, organizational readiness, and human commitment”. AI consulting services can guide this phase – conducting an AI readiness assessment, defining governance, and mapping out a vision.
- Prepare Data and Infrastructure: Autonomous agents thrive on integrated data. Build a unified data foundation by breaking silos across ERP, CRM, inventory, and external sources. Tools like data lakes or connected retail platforms can feed agents a 360° operational view. Ensure real-time data pipelines so agents can react instantly. Consulting firms often help design this architecture: as RSM notes, their advisory services include “understanding your data landscape” to identify the right AI opportunities.
- Pilot High-Impact Use Cases: Start small but strategic. For retail, this might be automating one store’s restock process or a single pricing category. Define metrics (e.g., stockout rate, sales lift) and run agentic pilots alongside human teams. Iterate on agent behavior and guardrails until performance is reliable. Then expand to more locations or categories. Consultants can accelerate pilots by providing tested agent frameworks and rapid prototyping, ensuring early wins that demonstrate ROI.
- Integrate with Existing Systems: When scaling, integrate agents into enterprise workflows and interfaces. This may involve connecting the agent to backend systems (inventory management, POS, pricing engines) via APIs or RPA scripts. Include safety checks: for example, an agentic pricing agent might have built-in guardrails to prevent extreme discounts. Work with AI development teams or consultants to embed the agents into your software stack and ensure security, compliance, and change management processes are in place.
- Monitor, Govern, and Iterate: Autonomous agents introduce new risks (errors, unintended actions). Establish monitoring dashboards and audit trails so humans can review agent decisions. Regularly retrain and update agents with new data. Key stakeholders (including legal and compliance) should review agent behavior. AI governance frameworks – a common consulting service – ensure that the agentic systems remain aligned with company policies and ethics as they learn.
Throughout this process, AI consulting services can be invaluable. Firms specializing in AI and automation bring cross-industry experience and tools. For example, global consulting company RSM highlights its “agentic AI consulting services” designed to help enterprises at every stage. Such services often include strategic advisory, solution implementation, and even ongoing managed support for agents. They help companies avoid pitfalls by providing best practices (e.g. how to integrate agents with legacy systems, or how to build a Center of Excellence).
Ready to harness agentic AI? Explore our Top Agentic AI Consultants page to connect with vetted AI consulting experts. They can help evaluate your readiness, define a roadmap, and deploy autonomous agents tailored to your business.
By following these steps – from readiness assessment to pilot to governance – enterprises can adopt agentic AI in a controlled, strategic way. The result is a powerful new layer of automation and intelligence across the organization. C-level leaders and boards should view this as a multiyear transformation, similar to how cloud or mobile strategies were adopted. With expert guidance and careful execution, agentic AI can deliver long-term productivity gains and a lasting competitive edge.
Frequently Asked Questions (FAQs)
What is agentic AI?
Agentic AI refers to autonomous AI systems (also called AI agents) that perceive their environment, plan actions, and execute tasks to achieve specified goals independently. Unlike traditional AI which reacts to inputs, agentic AI “reasons” about its goals and makes decisions on its own. In effect, agentic AI has “agency”: it can act and choose actions without being micromanaged. These systems typically combine advanced machine learning (often foundation models like LLMs) with planning, memory, and tool-use components.
How is agentic AI different from traditional AI?
Traditional AI (including rule-based systems, narrow ML models, and RPA) is usually limited to specific, pre-defined tasks and needs explicit human instructions. For example, a recommendation engine or a scripted chatbot will only do what it was specifically programmed or trained to do. Agentic AI goes beyond this by being goal-oriented and adaptive. It does not require step-by-step prompts; given a high-level objective, it autonomously breaks it down, decides what to do, and learns along the way. In short, traditional AI responds to commands, whereas agentic AI acts on goals.
What are autonomous agents?
An autonomous agent is simply an AI system built using agentic AI principles. It is a software “agent” that operates autonomously. For example, an autonomous inventory agent could be programmed to maintain stock levels: it checks data, decides when to reorder, and carries out purchase orders on its own. Autonomous agents can be simple or complex – some carry out focused tasks (like a scheduling assistant), while others can manage entire processes end-to-end. They collaborate with humans and other systems but do not need constant oversight. The essence of an autonomous agent is its independence in decision-making.
Can agentic AI improve retail operations?
Absolutely. In retail, agentic AI can revolutionize inventory management and pricing. For instance, an AI agent can continuously monitor sales and shelf levels across stores, and when it detects an impending stockout, it automatically orders replenishment from warehouses. This prevents lost sales without needing staff to place orders. Similarly, agents can dynamically optimize pricing and promotions: they analyze sales trends and competitor actions, then autonomously adjust discounts or promotional campaigns to boost revenue. Beyond stores, agents can streamline supply chains (auto-adjusting orders to changing demand) and enhance customer support (handling queries end-to-end). These autonomous workflows are what we call “AI workflow automation in retail,” where the AI makes decisions and executes them in real time to improve efficiency and responsiveness.
What are the benefits of agentic AI for enterprises?
Agentic AI drives productivity and competitive advantage. Key benefits include: (1) Higher efficiency: Agents automate complex processes 24/7, reducing manual work and errors. (2) Better scalability: Systems scale horizontally (you can deploy more agents globally without a linear increase in costs). (3) Improved decision speed: Agents analyze real-time data across systems to make proactive decisions, accelerating strategy execution. (4) Cost reduction: By offloading routine tasks, companies save on labor and operational expenses over time. (5) Innovation: Agents continuously learn and suggest optimizations, enabling new business models. In short, global companies using agentic AI can focus human talent on high-value problems while AI handles scalable, data-driven workflows.
How do I start using agentic AI in my business?
Begin by identifying specific use cases where autonomy adds value (e.g., supply chain, customer support, finance). Conduct an AI readiness check: ensure your data is integrated and your leadership has a clear goal for the AI. Then run a small pilot: work with an AI consulting team to develop a proof-of-concept autonomous agent for one process. Measure its performance and refine it. If successful, gradually expand the scope and integrate with more systems. Throughout, maintain governance (clear policies, audit trails, and human oversight for critical steps). Engaging experienced AI consultants is advisable: they can help design the agentic AI architecture, set up the data pipelines, and manage the change process.
What are AI consulting services for agentic AI?
Many consulting firms now offer specialized AI strategy and implementation services, including for agentic AI. These AI consulting services help companies evaluate readiness, pick the right tools, and build solutions. For example, RSM offers dedicated “agentic AI consulting services,” guiding organizations on developing and governing AI agents. Our Top Agentic AI Consultants page lists vetted providers who understand both AI and your industry. These consultants can help with everything from data strategy and model integration to upskilling your team, ensuring that agentic AI delivers tangible business impact.
What is on the Top Agentic AI Consultants page?
We curate a list of leading AI consulting companies and experts specializing in autonomous agents and agentic AI. The page provides links and overviews so you can find partners experienced in AI strategy, software development, and enterprise automation. Refer to that page to connect with advisors who can help your organization plan, implement, and scale agentic AI solutions tailored to global business needs.
Can agentic AI work alongside humans?
Yes. Agentic AI is intended to augment human teams, not replace them. Autonomous agents handle routine, data-heavy, or multi-step tasks, while humans focus on creative and strategic work. Most agentic systems include a human-in-the-loop for critical decisions or oversight. For example, an agent might recommend a pricing change, which a manager reviews before approving. Over time, as trust grows, humans can delegate more to the agents. The goal is a symbiotic workflow where AI amplifies human capabilities, driving higher quality and speed.
What are the risks of agentic AI?
Autonomous agents can make mistakes or pursue unintended actions if not carefully managed. Risks include erroneous decisions, bias propagation, and compliance issues. Because agents operate with some independence, it’s crucial to have governance: clear objectives, constraints, monitoring, and fail-safes. Consulting experts emphasize building transparent audit trails and ethical frameworks from the start. By proactively addressing these risks with proper oversight, firms can harness agentic AI securely while mitigating potential downsides.
How does agentic AI relate to generative AI (GenAI)?
Both technologies are interrelated. Generative AI (like ChatGPT) specializes in creating content (text, images, code) based on prompts. Agentic AI, on the other hand, is built to act. As UiPath explains, generative AI “stops at creation,” while agentic AI “continues – implementing actions, triggering workflows, and adapting to new circumstances”. In practice, a business might use GenAI to draft an email campaign, then have agentic AI launch and optimize that campaign automatically based on real-time feedback. The two can complement each other: generative models can serve as sub-components of agentic agents, providing language or analysis, while the agent orchestrates overall tasks.
Where can I learn more?
For an in-depth look at use cases, implementation advice, and up-to-date trends, check out resources from industry experts and consulting firms. You can also explore our own Top Agentic AI Consultants page for actionable guidance and partner recommendations. Embracing agentic AI is a journey – start with education, strategy, and pilot projects to ensure your organization thrives in this new era of autonomous intelligence.
