Schedule a Demo

Content Type: Educational

Deep-dive explanations and frameworks

  • The Evolution of Enterprise AI: From Prediction to Action

    The Evolution of Enterprise AI: From Prediction to Action

    Three years ago, ChatGPT launched and changed everything. Or did it?

    The reality is more nuanced. According to McKinsey’s 2025 State of AI report, 88% of enterprises now report regular AI use in their organizations. That’s remarkable progress. But here’s the sobering counterpoint: over 80% of those same respondents reported no meaningful impact on enterprise-wide EBIT. AI has gone from experimental to operational, but for most organizations, it hasn’t yet become transformational.

    Understanding why requires understanding how enterprise AI has evolved—and where it’s heading next. What started as specialized machine learning models for prediction has evolved into autonomous agents capable of taking action on behalf of the organization. Each era has built on the last, and each has demanded different capabilities from the organizations deploying it.

    The Four Eras of Enterprise AI

    Era 1: Traditional AI (2020-2022)

    This was AI as most enterprises first knew it—sophisticated machine learning models trained on historical data to make predictions. A fraud detection model could flag suspicious transactions. A demand forecasting system could predict inventory needs. But the key limitation was fundamental: these systems provided scores and classifications. They couldn’t take action.

    These traditional AI systems excelled at passive prediction—providing scores or classifications that required human interpretation. Each model was single-purpose, built for a specific task, and demanded substantial data requirements for training. They had limited adaptability to new situations and couldn’t learn from conversational feedback. Think fraud detection scoring, demand forecasting, customer churn prediction, image classification, and recommendation engines.

    These systems were powerful but required significant data science expertise and infrastructure investment. Value came from better predictions, but humans still made all decisions and took all actions. The barrier to entry was high—you needed specialized talent and years of data to train effective models.

    Era 2: Chat AI (2023)

    ChatGPT’s November 2022 launch marked a turning point. Suddenly, any employee could interact with AI using natural language—no data science degree required. Within months, generative AI went from curiosity to corporate priority. According to the Stanford HAI 2025 AI Index Report, U.S. private AI investment grew to $109.1 billion in 2024—nearly 12 times China’s investment and 24 times the U.K.’s.

    Chat AI delivered an interactive Q&A interface with natural language understanding and generation, broad general knowledge, and remarkable accessibility. But it had no ability to take action and maintained only stateless conversations. ChatGPT for research and drafting, customer service chatbots, content creation tools, and code explanation and debugging became commonplace.

    ChatGPT made AI accessible to everyone. But these systems could only provide information—they couldn’t take action in business systems. The knowledge was impressive; the capability to act on it was absent.

    Era 3: Copilots (2024)

    Copilots represented the first real integration of generative AI into daily work. Code became AI’s first true “killer use case”—50% of developers now use AI coding tools daily, according to Menlo Ventures research, rising to 65% in top-quartile organizations. Menlo Ventures reports that departmental AI spending on coding alone reached $4 billion in 2025—55% of all departmental AI spend.

    Copilots brought context-aware suggestions while keeping humans in control of every decision. They provided real-time assistance during work and integrated into existing tools like IDEs, productivity apps, and CRMs. But they required constant human oversight—the AI suggested, the human decided. GitHub Copilot for code completion, Microsoft 365 Copilot for productivity, Salesforce Einstein GPT for sales, and Google Duet AI for workspace defined this era.

    Copilots showed AI could accelerate individual productivity. A developer with Copilot could write code faster; a sales rep could draft emails more quickly. But humans still made every decision and approved every action. The AI suggested; the human decided.

    Era 4: Agentic AI (2025-2026)

    This is where we are now—and where the transformation gets real. For a deeper understanding of what distinguishes agents from earlier AI systems, see our guide on what agentic AI actually means. According to Gartner, 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. That’s an 8x increase in a single year.

    McKinsey’s research shows 62% of organizations are already experimenting with AI agents, with 23% actively scaling agentic AI systems. The projected ROI is striking: organizations expect an average return of 171% from agentic AI deployments, with U.S. enterprises forecasting 192% returns.

    Agentic AI introduces goal-oriented autonomy—systems that can plan multi-step processes and execute them independently. They use tools and APIs, adapt through learning from feedback, and maintain contextual memory across sessions. Automated incident response, end-to-end invoice processing, supply chain optimization, multi-step sales workflows, and customer onboarding automation are emerging applications.

    Agents can complete entire workflows autonomously. They don’t just suggest the next email—they draft it, send it, track responses, and follow up. The human role shifts from execution to oversight. This is where AI finally starts delivering on the promise of true business transformation.

    What Changes with Each Era

    Dimension Traditional AI Chat AI Copilots Agents
    Human role Interpret & act Ask & evaluate Approve & edit Supervise & escalate
    Autonomy None None Limited High
    Integration Backend systems Chat interface Within apps Across systems
    Expertise needed Data scientists Anyone Anyone Anyone (with governance)
    Risk profile Low (no action) Low (no action) Medium (human approval) Higher (autonomous action)

    The Governance Imperative

    As AI gains more autonomy, governance becomes more critical. But here’s a warning from Gartner that every enterprise leader should heed: over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls.

    The enterprises that succeed will be the ones that treat governance as an enabler, not an afterthought.

    Traditional AI and Chat AI carried a low governance burden—they provided information but took no action. Main concerns centered on accuracy and appropriate use. Copilots require moderate governance—AI suggests actions but humans approve. Concerns include data handling, appropriate suggestions, and over-reliance on AI-generated outputs.

    Agentic AI demands high governance. AI takes action autonomously, which means you need visibility into what agents do, controls to prevent inappropriate actions, and audit trails for compliance. Without these, agents become liabilities rather than assets. Knowing how to measure AI ROI becomes essential when autonomous systems are making decisions on your behalf.

    What This Means for Enterprise Leaders

    The Opportunity

    Each era has delivered more value than the last. The numbers tell the story: companies spent $37 billion on generative AI in 2025, up from $11.5 billion in 2024—a 3.2x year-over-year increase. That investment is flowing toward real productivity gains, not just experimentation.

    The Challenge

    More autonomy means more risk. An agent that can take action can take wrong action. And the failure modes are real: 42% of companies abandoned most AI initiatives in 2025, up sharply from 17% in 2024, according to research from MIT and RAND Corporation. The gap between AI adoption and AI value remains stubbornly wide.

    The Path Forward

    The enterprises that will win are those who embrace agentic AI for the right use cases—starting with low-risk, high-volume workflows where automation delivers clear value and mistakes are recoverable. They’ll build governance from day one, treating visibility, controls, and measurement as core requirements rather than afterthoughts. They’ll measure outcomes relentlessly, proving ROI and identifying problems before they become crises. And they’ll prepare their organization, helping employees understand how their roles will evolve from execution to oversight as agents take on more autonomous work.

    What’s Next

    The evolution isn’t over. By 2028, Gartner predicts at least 15% of day-to-day work decisions will be made autonomously through agentic AI—up from 0% in 2024. Additionally, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024.

    Several emerging trends deserve attention. Multi-agent systems—agents that coordinate with each other to complete complex tasks—are moving from research to production. Continuous learning enables agents that improve from feedback without manual retraining. Deeper integration gives agents access to more enterprise systems and data. And industry-specific agents provide pre-built solutions for common workflows in specific industries.

    For a deeper exploration of the economics driving agent adoption, the Future of Agentic guide to agent economics covers TCO analysis and ROI calculations.

    The enterprises that understand this evolution—and prepare for what’s coming—will be best positioned to capture value from AI. The ones that don’t will find themselves in that uncomfortable 80%: using AI everywhere, but struggling to show the ROI.

    Ready to navigate the evolution of enterprise AI? Schedule a demo to see how Olakai helps organizations measure and govern AI across all four eras.

  • Shadow AI: The Hidden Risk in Your Enterprise

    Shadow AI: The Hidden Risk in Your Enterprise

    Your employees are using AI tools you don’t know about. Right now. They’re pasting customer data into ChatGPT to draft emails. They’re uploading financial documents to AI summarizers. They’re using unapproved coding assistants that send your source code to third-party servers.

    And the numbers are staggering. According to a Gartner survey of cybersecurity leaders conducted in 2025, 69% of organizations suspect or have evidence that employees are using prohibited public GenAI tools. Microsoft’s research found that 71% of UK employees admitted to using unapproved AI tools at work—with 51% doing so at least once a week. This isn’t occasional experimentation; it’s a systematic shadow operation running parallel to your official technology stack.

    This is shadow AI—the enterprise AI equivalent of shadow IT—and it represents one of the most significant and underestimated risks facing organizations today.

    What is Shadow AI?

    Shadow AI refers to AI tools and services that exist outside your organization’s visibility and governance. They’re not approved by IT, security, or compliance teams. They’re not visible in your technology inventory. They’re not governed by your data protection policies. And they’re not monitored for security, compliance, or cost implications.

    Just as shadow IT emerged when employees started using Dropbox, Slack, and other cloud tools without IT approval, shadow AI is spreading as employees discover that AI makes their jobs easier—regardless of whether it’s sanctioned. According to the 2025 State of Shadow AI Report, the average enterprise hosts 1,200 unauthorized applications, and 86% of organizations are blind to AI data flows. Nearly half (47%) of people using generative AI platforms do so through personal accounts that companies aren’t overseeing.

    Why Shadow AI is Different from Shadow IT

    Shadow AI carries risks that go beyond traditional shadow IT in fundamental ways.

    Data goes out, not just in. When an employee uses unauthorized Dropbox, they might store company files externally—a risk, but a bounded one. When they use unauthorized AI, they actively send sensitive data to third-party models. That customer complaint they pasted into ChatGPT? It might be used to train the model, potentially surfacing in responses to competitors. According to Cisco’s 2025 study, 46% of organizations reported internal data leaks through generative AI—data that flowed out through employee prompts rather than traditional exfiltration.

    Prompts reveal more than files. The questions employees ask AI reveal context that raw data doesn’t. “Summarize this contract and identify terms unfavorable to us” tells the AI (and its operator) not just the contract contents, but your negotiating strategy and concerns. The prompt itself is intelligence.

    Answers drive decisions. When AI provides analysis or recommendations, employees act on them. An unauthorized AI tool giving bad financial advice, incorrect legal interpretation, or flawed technical guidance can lead to costly mistakes with no audit trail. And there’s no recourse when things go wrong.

    The attack surface is enormous. Prompt injection, jailbreaking, and other AI-specific attacks create new vectors. An employee who pastes customer data into a compromised AI tool might unknowingly expose that data to attackers who’ve manipulated the model.

    The Scope of the Problem

    If you think shadow AI isn’t happening in your organization, the statistics suggest otherwise. Gartner predicts that by 2030, more than 40% of enterprises will experience security or compliance incidents linked to unauthorized shadow AI. That’s not a distant future risk—it’s the trajectory we’re already on.

    The financial impact is real and immediate. According to IBM’s 2025 Cost of Data Breach Report, shadow AI incidents now account for 20% of all breaches and carry a cost premium: $4.63 million versus $3.96 million for standard breaches. AI-associated cases caused organizations more than $650,000 extra per breach. The gap between AI adoption and AI governance is where shadow AI thrives—and where the costs accumulate.

    Perhaps most concerning: 83% of organizations operate without basic controls to prevent data exposure to AI tools. The average company experiences 223 incidents per month of users sending sensitive data to AI applications—double the rate from a year ago. And 27% of organizations report that over 30% of their AI-processed data contains private information, including customer records, financial data, and trade secrets.

    Common Shadow AI Scenarios

    These aren’t hypothetical risks. They’re happening in organizations like yours, every day.

    The helpful marketer uses an AI writing tool to draft blog posts. She pastes competitor analysis, product roadmaps, and customer testimonials as context. The tool’s terms of service allow training on user inputs. Your competitive intelligence is now potentially in someone else’s model—or in their training data, waiting to surface in responses to your competitors.

    The efficient developer uses an unapproved coding assistant to speed up development. He pastes internal API documentation and proprietary algorithms for context. The code generated might include those patterns in ways that constitute IP leakage, and the original code may be used for model training.

    The overwhelmed HR manager uses an AI tool to help screen resumes and draft interview questions. She pastes candidate information, salary data, and performance review excerpts. She’s now exposed PII to an unapproved processor, potentially violating GDPR and internal policies—with no documentation of consent or processing basis.

    The pressured analyst uses an AI tool to summarize earnings calls and model scenarios. He pastes material non-public information into prompts. If that information surfaces elsewhere—or even if someone later discovers it was processed through an unauthorized channel—it could trigger SEC scrutiny.

    Why Traditional Controls Don’t Work

    The approaches that worked for shadow IT often fail for shadow AI.

    Blocking doesn’t scale. You can’t block every AI tool—new ones appear daily. Employees use personal devices. VPNs and proxies circumvent network controls. Small businesses face the highest risk, with 27% of employees in companies with 11-50 workers using unsanctioned tools. These organizations average 269 shadow AI tools per 1,000 employees while lacking the security resources to monitor them.

    Policies aren’t enough. Acceptable use policies help, but they rely on employees reading, understanding, and following them. When AI makes someone dramatically more productive, policy compliance becomes an afterthought. According to research, 90% of security leaders themselves report using unapproved AI tools at work—with 69% of CISOs incorporating them into daily workflows. If the people writing the policies aren’t following them, you have a systemic problem.

    Training has limits. Security awareness training can highlight risks, but it can’t prevent every incident. Employees under deadline pressure make expedient choices.

    A Better Approach: Discovery, Governance, and Alternatives

    Effective shadow AI management requires a multi-pronged approach that acknowledges human nature while protecting organizational interests.

    Discovery: See what’s happening. You can’t govern what you can’t see. Modern shadow AI discovery involves monitoring network traffic for AI tool usage patterns, analyzing browser extensions and desktop applications, surveying employees about tools they’re using, and reviewing expense reports and credit card statements for AI subscriptions. The goal isn’t surveillance—it’s visibility. You need to know what’s being used so you can make informed governance decisions.

    Risk assessment: Prioritize what matters. Not all shadow AI carries equal risk. Assess each discovered tool against data sensitivity (what data types are being processed?), regulatory exposure (does usage implicate GDPR, CCPA, HIPAA, or SOX?), vendor risk (what are the tool’s data handling practices?), and business impact (how critical is this tool to the workflow?). For a framework on matching governance to risk levels, see our AI risk heatmap approach.

    Provide sanctioned alternatives. Heavy-handed blocking drives shadow AI underground. Instead, provide approved alternatives that meet employee needs: deploy enterprise AI tools with proper data protection, negotiate data processing agreements with AI vendors, configure guardrails like PII detection and content filtering, and communicate what’s available and how to access it. When approved tools are easy to use and meet employee needs, shadow AI becomes less attractive.

    Continuous monitoring. Shadow AI isn’t a one-time problem to solve—it’s an ongoing challenge to manage. Establish regular discovery scans to identify new tools, usage monitoring for sanctioned tools, incident response procedures for policy violations, and feedback loops to understand why employees seek alternatives. According to Delinea’s 2025 report, 44% of organizations with AI usage struggle with business units deploying AI solutions without involving IT and security teams. That gap needs ongoing attention.

    The Role of Governance

    Ultimately, shadow AI is a symptom of governance gaps. Organizations that struggle with shadow AI often lack visibility (no central inventory of AI tools and usage), policy (no clear guidelines on acceptable AI use), process (no fast-track approval for low-risk AI tools), alternatives (no sanctioned tools that meet employee needs), and culture (no psychological safety to ask “Can I use this?”).

    Building AI governance isn’t about creating barriers—it’s about creating clarity. Employees want to do the right thing. They just need to know what the right thing is. Our CISO governance checklist provides a comprehensive framework for building these foundations.

    Getting Started

    If you’re concerned about shadow AI in your organization—and if you’re paying attention to the statistics, you should be—start with a discovery exercise. Survey employees, analyze network traffic, review expense reports. Understand your current exposure before trying to solve it.

    Assess risks by prioritizing discovered tools by data sensitivity and regulatory exposure. Focus governance efforts on highest-risk usage first—you can’t solve everything at once.

    Provide alternatives by deploying sanctioned AI tools that meet legitimate employee needs. Make approved tools easier to use than shadow alternatives. If the official path is harder than the unofficial one, you’ll keep losing.

    Build governance foundations through policies, processes, and monitoring. But start lightweight—you can add sophistication over time. Gartner also predicts that by 2030, 50% of enterprises will face delayed AI upgrades and rising maintenance costs due to unmanaged GenAI technical debt. Building governance now prevents that debt from accumulating.

    Communicate clearly. Tell employees what’s approved, what’s not, and why. Create a safe channel to ask questions. The Future of Agentic use case library can help illustrate what good AI governance looks like in practice.

    The Bottom Line

    Shadow AI is already in your organization. The question isn’t whether it exists, but how you’ll respond. A heavy-handed approach drives usage underground. A permissive approach exposes you to risk. The right approach—discovery, governance, and alternatives—lets you get the benefits of AI while managing the downsides.

    Want to understand your shadow AI exposure? Schedule a demo to see how Olakai helps enterprises discover, assess, and govern AI usage across the organization.

  • What is Agentic AI? A Guide for Enterprise Leaders

    What is Agentic AI? A Guide for Enterprise Leaders

    If you’re an enterprise leader trying to make sense of AI, you’ve likely noticed a shift in the conversation. ChatGPT and copilots were impressive—but now there’s talk of agentic AI: systems that don’t just answer questions, but take action to achieve goals. What does this mean for your organization?

    The numbers suggest this isn’t hype. According to Gartner, 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. That’s an 8x increase in a single year. McKinsey’s 2025 State of AI report found that 62% of organizations are already experimenting with AI agents, and 79% say they’ve adopted agents to some extent.

    This guide cuts through the hype to explain what makes AI “agentic,” how it differs from the chatbots and copilots you’re already using, and what enterprise leaders need to know as autonomous agents become a reality.

    The Evolution of Enterprise AI

    To understand agentic AI, it helps to see where we’ve been.

    Traditional AI (2020-2022) consisted of machine learning models that predict outcomes based on patterns. Think fraud detection scoring, demand forecasting, or customer churn prediction. These systems were powerful but passive—they required humans to interpret results and take action on the insights they provided.

    Chat AI (2023) brought large language models that respond to prompts with natural language. ChatGPT made AI accessible to everyone, enabling research assistance, content drafting, and customer service chatbots. But these systems had no ability to take action—they could only provide information and leave the execution to humans.

    Copilots (2024) represented AI assistants that augment human work with suggestions and completions. GitHub Copilot, Microsoft 365 Copilot, and Salesforce Einstein GPT define this generation. They’re context-aware and integrated into workflows, but humans remain in control of every decision. The AI suggests; the human decides and executes.

    Agentic AI (2025-2026) introduces autonomous systems that take action to achieve goals with minimal human intervention. These agents don’t wait for prompts—they plan multi-step workflows, use tools and APIs, and execute end-to-end processes. For a deeper exploration of how this evolution is unfolding, see our analysis of enterprise AI’s evolution from prediction to action.

    Six Core Characteristics of Agentic AI

    What makes an AI system truly “agentic”? According to Gartner, autonomous agents are combined systems that achieve defined goals without repeated human intervention, using a variety of AI techniques to make decisions and generate outputs. They have the potential to learn from their environment and improve over time. Look for these six characteristics.

    Autonomy means the system takes action without constant human input. It operates independently within defined boundaries and escalates only when necessary. Think of it like a trusted personal assistant who knows to book your recurring monthly flight without asking each time, but will check with you if prices exceed your usual budget. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention.

    Planning enables the system to break down complex tasks into actionable steps. It creates execution plans and adjusts based on outcomes and changing conditions. Like a seasoned chef preparing Thanksgiving dinner—they know to start the turkey first, prep sides while it cooks, and adjust timing if guests arrive late. The planning capability is what transforms a responsive system into a proactive one.

    Tool Use allows the system to integrate with other systems via APIs, databases, and applications. It orchestrates multiple tools to complete end-to-end workflows. Think of a general contractor who doesn’t just plan your kitchen remodel—they actually pick up the phone to coordinate electricians, plumbers, and inspectors to get the job done. Agentic AI doesn’t just recommend calling the API; it calls it.

    Memory maintains context across interactions and sessions. The system remembers past decisions, user preferences, and workflow state. Like your family doctor who remembers your medication allergies from three years ago, your preferred pharmacy, and that you respond better to evening appointments. Memory transforms one-off interactions into ongoing relationships.

    Reasoning enables decisions based on goals, constraints, and context. The system evaluates trade-offs and selects optimal actions given the information available. Like a financial advisor who weighs your retirement goals against current cash needs and recommends whether to max out your 401(k) or pay down your mortgage. The reasoning is transparent and auditable.

    Learning allows the system to adapt from feedback, successes, and failures. It improves performance over time through experience and reinforcement. Like a barista who remembers you liked your latte extra hot last time, tries it that way again today, and asks for feedback to get your order perfect every visit. Learning agents get better the more they’re used.

    For a comprehensive exploration of these characteristics with interactive examples, the Future of Agentic guide to agent characteristics provides detailed analysis.

    Chat AI vs. Copilots vs. Agents: Key Differences

    Understanding the spectrum helps you set appropriate expectations.

    Dimension Chat AI Copilots Agentic AI
    Autonomy Level None—responds only when prompted Limited—suggests but doesn’t execute High—executes multi-step workflows
    Human Oversight 100% (every interaction) 80-90% (review before action) 10-30% (key decision points only)
    Task Complexity Single-turn Q&A Assisted completion Multi-step workflows
    Response Time Seconds Milliseconds to seconds Minutes to hours
    Cost per Interaction $0.001-0.01 $0.01-0.10 $0.10-1.00+
    Risk Level Low (information only) Medium (human reviews) High (requires governance)

    While generative AI focuses on creating content such as text, images, or code, agentic AI focuses on action. Adding task specialization capabilities evolves AI assistants into AI agents with the capacity to operate and perform complex, end-to-end tasks.

    Real-World Examples

    What does agentic AI look like in practice?

    Agentic Example: Invoice Processing. When an invoice exceeds $50K or has mismatched PO numbers, an agentic system automatically flags it, updates the status to “Review Required,” adds a comment explaining the anomaly, and sends a Slack message to the appropriate approver based on department and amount thresholds. No human initiated these steps—the agent made decisions and executed actions autonomously based on policy and context.

    Agentic Example: Travel Booking. An employee submits a trip request: “Book me a flight to San Francisco next Monday, staying until Thursday.” The agent searches flights, books the cheapest option under $500 per company policy, reserves a hotel near the office, creates an expense report pre-filled with trip details, updates the employee’s calendar, and sends a confirmation email with the complete itinerary—all without human intervention.

    Not Agentic: Code Completion. A developer uses an AI-powered code editor that predicts what they’ll type next. The AI suggests function completions, but the developer must explicitly accept each suggestion. This is a copilot pattern—sophisticated assistance, but no autonomous execution. The human remains in the loop for every action.

    Why This Matters for Enterprise Leaders

    The shift to agentic AI has significant implications that go beyond technology decisions.

    Higher stakes. When agents take action autonomously, mistakes have real consequences. A chatbot that gives wrong information is annoying; an agent that executes wrong actions can cost money, damage relationships, or create compliance issues. Deloitte’s 2025 study found that while 30% of organizations are exploring agentic options and 38% are piloting solutions, only 14% have solutions ready to deploy and just 11% are actively using agents in production. The gap reflects how seriously enterprises are taking the governance requirements.

    New governance requirements. You need visibility into what agents are doing, controls to prevent unauthorized actions, and the ability to audit decisions after the fact. Traditional IT governance wasn’t designed for autonomous systems. Gartner predicts that guardian agents—specialized agents focused on governance and oversight—will capture 10-15% of the agentic AI market by 2030. For a comprehensive framework, see our AI governance checklist for CISOs.

    Different ROI model. Agents cost more per interaction but can deliver dramatically higher value by completing end-to-end workflows. The economics shift from “cost per query” to “value per outcome.” In a best-case scenario, Gartner projects agentic AI could generate nearly 30% of enterprise application software revenue by 2035—surpassing $450 billion. For a framework on measuring this value, see our AI ROI measurement guide.

    Workforce implications. Agents won’t replace humans wholesale, but they will change what humans do. Many roles will shift from execution to oversight and exception handling. By 2028, Gartner predicts 33% of enterprise software applications will include agentic AI, enabling 15% of day-to-day work decisions to be made autonomously. Organizations need to prepare their workforce for this shift.

    The Multi-Agent Future

    Just as monolithic applications gave way to distributed service architectures, single all-purpose agents are being replaced by orchestrated teams of specialized agents. Gartner reported a staggering 1,445% surge in multi-agent system inquiries from Q1 2024 to Q2 2025. By 2028, Gartner predicts 70% of AI applications will use multi-agent systems.

    This evolution means enterprise AI will increasingly involve ecosystems of specialized agents working together—finance agents, HR agents, security agents, customer service agents—coordinating to complete complex workflows that span organizational boundaries.

    Getting Started with Agentic AI

    If you’re considering agentic AI for your enterprise, start with low-risk, high-volume use cases. Lead qualification, invoice processing, and IT ticket routing are common starting points where autonomous action delivers clear value with manageable risk. 50% of enterprises using generative AI are expected to deploy autonomous AI agents by 2027, doubling from 25% in 2025.

    Build governance from day one. Don’t wait until you have a dozen agents to think about visibility, controls, and measurement. Establishing governance foundations early prevents painful retrofitting later. Our AI risk heatmap framework helps you match governance intensity to risk level.

    Measure what matters. Track not just agent activity but business outcomes: time saved, error rates, cost per transaction, and ROI. Without measurement, you can’t prove value or identify problems before they become crises.

    Plan for scale. Pilot projects often succeed; scaling is where most enterprises struggle. Consider how your infrastructure, governance, and change management will handle 10x the agents before you need to find out.

    The Bottom Line

    Agentic AI represents a fundamental shift from AI that informs to AI that acts. For enterprise leaders, this means new opportunities for automation and efficiency—but also new requirements for governance, measurement, and oversight.

    The enterprises that thrive will be those who embrace agentic AI while building the guardrails to use it responsibly. That means investing not just in the agents themselves, but in the infrastructure to measure their impact, govern their behavior, and ensure they’re delivering real business value.

    Ready to implement agentic AI with confidence? Schedule a demo to see how Olakai helps enterprises measure ROI, govern risk, and scale AI agents responsibly.