Tag: Agentic AI

We’re in the middle of a major shift in how AI shows up at work. It’s no longer just about automation or predictions. What’s emerging now is AI that acts with intent, systems that observe their surroundings, make sense of what’s happening, plan intelligently, and then execute, often without constant human direction.

This new class of intelligent systems is called Agentic AI. And it doesn’t just improve productivity – it fundamentally reimagines it.

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Finance is undergoing a quiet but profound transformation. Once focused primarily on compliance and retrospective reporting, it is now expected to guide real-time decisions, model uncertainty, and act as a strategic nerve centre for the business. This evolution is being accelerated by new forms of AI.  

GenAI is already reshaping how finance teams operate, automating time-consuming tasks like reporting, analysis, and documentation; translating spreadsheets into plain-language insights; and preparing stakeholder updates. From board reports to risk disclosures, GenAI improves speed, clarity, and consistency, freeing up teams to focus on interpretation and strategic input. 

Agentic AI takes this further by introducing autonomous decision-making. It monitors systems in real time, flags anomalies, simulates scenarios, and takes targeted action. Acting like a digital financial analyst, it enables Finance to stay ahead of risk, optimise outcomes, and respond dynamically as situations unfold. 

Together, GenAI and Agentic AI are pushing Finance from the sidelines to the centre, embedded across teams, influencing performance, and enabling faster, smarter, and more proactive decision-making.  

Why Involving CFOs Is Critical Now 

One of the biggest challenges organisations face with AI isn’t adoption; it’s accountability. Despite rising investment, many still struggle to define the value of AI initiatives and measure whether that value is actually realised. 

That’s a missed opportunity. Finance brings the discipline and strategic lens needed to align AI initiatives with real business value. But the CFO’s role shouldn’t stop at oversight. With tools like Agentic AI, Finance can actively model ROI scenarios in real time, monitor performance signals as they emerge, and steer capital dynamically towards what’s working. 

This enables a shift from static business cases to continuous, evidence-based optimisation. CFOs can help the business move faster, ensuring AI delivers measurable outcomes. When Finance leads on AI, it turns experimentation into execution, and innovation into impact. 

Taking Finance a Step Ahead with Agentic AI 

That shift from oversight to impact is exactly where Agentic AI comes in. It equips Finance not just to track performance, but to shape it, enabling intelligent systems that monitor, plan, and act continuously. Here’s how.  

• Cash Flow Optimisation. Agentic AI tracks cash inflows and outflows across the business. When it detects a potential surplus or shortfall, it recommends steps such as rescheduling payments, accelerating collections, or reallocating funds. This keeps the organisation financially agile. 

• Scenario Simulation. Whether it’s a revenue drop, raw material price spike, or new regulatory cost, Agentic AI can instantly model the impact and suggest mitigating actions. This enables better, faster decision-making under uncertainty. 

• Expense Monitoring. Agentic AI watches spending in real time. If it detects duplicate invoices, unapproved vendor charges, or unexpected cost spikes, it flags them immediately and recommends next steps. This reduces waste and strengthens controls. 

• Automated Close and Reconciliation. Month-end processes often strain teams. Agentic AI helps reconcile transactions, review journal entries, and highlight discrepancies, making the close faster and more accurate. 

• Live KPI Tracking. Agentic AI keeps an eye on financial metrics like margins, liquidity, and burn rate. When something crosses a threshold, it sends alerts and proposes adjustments so teams can respond quickly. 

• Investor and Board Preparation. Agentic AI supports leadership by analysing previous board interactions, market sentiment, and current performance. It anticipates likely questions and ensures messaging is aligned and strategic. 

• Cross-Functional Planning. When marketing spends more or HR increases hiring, Agentic AI models the financial impact in real time. This helps keep forecasts aligned with real business activity and ensures coordination across teams. 

How GenAI and Agentic AI Elevate Finance’s Expanding Mandate 

As Finance takes on broader responsibilities within the enterprise, GenAI and Agentic AI offer targeted support across three critical fronts, transforming Finance into a strategic partner embedded across the organisation. 

1. Investor & ROI Gatekeeper: Turning Data into Capital Strategy 

Finance plays a central role in guiding where the business places its bets. GenAI accelerates financial modelling, investment case analysis, and performance reporting, helping teams move faster with greater clarity. Agentic AI enhances this by continuously scanning market signals, simulating return scenarios, and triggering early alerts when projections veer off course. Together, they enable sharper capital allocation, stronger investor narratives, and faster decision cycles. 

2. Risk & Compliance Steward: Navigating AI’s New Risk Landscape 

As AI adoption grows, so do regulatory expectations. GenAI streamlines documentation, audit preparation, and policy reviews, while surfacing potential compliance gaps. Agentic AI takes on active monitoring,  detecting anomalies in transactions, tracking adherence to evolving standards, and escalating risk signals in real time. These capabilities give Finance a proactive stance on governance, helping the business stay ahead of both financial and algorithmic risks. 

3. AI Champion & Business Leader: Driving Adoption in Finance 

Finance teams are well-placed to lead by example. By embedding GenAI into reporting, forecasting, and planning workflows, they demonstrate AI’s practical value. Agentic AI automates close processes, responds to budget deviations, and adapts forecasts dynamically. This not only boosts Finance’s efficiency but also positions the function as a credible champion of AI transformation across the business. 

Building a Future-Ready Finance Function 

Beyond task-level improvements, the combined power of GenAI and Agentic AI unlocks new strategic capabilities for Finance: 

• Capital Allocation with Confidence. AI helps prioritise investments not just by raw numbers but through scenario modelling and real-time feedback. Finance teams can compare expected ROI across initiatives and confidently direct capital where it matters most. 

• Strategic Risk Management. AI tools track internal data alongside external signals – policy changes, market movements, supply chain disruptions – to flag emerging threats early. This allows finance leaders to plan and adapt proactively. 

• Workforce and Headcount Planning. When business demands shift, Agentic AI recommends resource reallocations, hiring, training, or restructuring. GenAI then builds clear business cases and headcount proposals to support leadership decisions. 

• Policy Testing and Simulation. Agentic AI models the impact of policy changes, such as altering bonus structures or shifting to hybrid work, on morale, retention, and costs. GenAI produces change briefs and simulation reports to guide leadership through these decisions. 

• Intelligent Communication. GenAI strengthens Finance’s voice by transforming analysis into compelling narratives. Whether crafting strategy memos or investor updates, it ensures messaging is sharp, consistent, and aligned with business goals. 

Letting Finance Lead with Insight and Intention 

The goal of AI in finance isn’t to replace people. It’s to elevate their impact. 

GenAI takes care of routine documentation and reporting. Agentic AI senses risks and opportunities and acts in real time. Together, they create space for finance professionals to focus on what really matters – insight, strategy, and leadership. This shift goes beyond efficiency. It’s about reimagining the role of finance in a fast-moving world. AI doesn’t remove the human element. It enhances it. 

Finance is no longer just the record-keeper. With AI as a partner, it becomes a navigator, guiding the business with clarity, care, and conviction. 

Across our conversations with marketing leads, sales heads, customer experience owners, and tech architects, one theme keeps coming up: It’s not about collecting more data. It’s about making sense of what we already have.

As customer journeys grow more fragmented, leaders are grappling with a big question: how do we unify data in a way that helps teams act fast, personally, and responsibly?

This is where CRM and CDP integration becomes critical. Not a technical afterthought, but a strategic decision.

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 HR has often been positioned as a support function, called in to manage policies, resolve issues, or guide change already in motion. But as organisations become more distributed, dynamic, and employee expectations grow, that reactive model falls short. HR today is expected to shape culture, influence strategy, and stay embedded in the day-to-day experience of work. 

GenAI has already started to change how HR teams work, speeding up tasks like drafting policies, analysing engagement data, and generating learning content. But to go further, HR needs systems that can sense what’s happening in real time, respond with context, and act proactively. That’s where Agentic AI comes in. It goes beyond assistance to autonomous action – routing queries, flagging risks, triggering nudges, or coordinating tasks across systems. 

Together, GenAI and Agentic AI are shifting HR from supporting decisions to actively shaping them, and doing so at scale. 

Scaling HR Impact with GenAI and Agentic AI 

GenAI is changing how HR teams operate – accelerating everyday tasks like reviewing CVs, drafting job descriptions, or analysing employee performance reviews. It supports the creation of tailored policies, improves the quality and consistency of outreach, and helps surface insights from unstructured data. It also enables more targeted interview preparation and personalised learning journeys. 

These capabilities have helped HR move towards a more responsive, employee-focused model. But GenAI still works within the limits of the prompts it receives. It enhances productivity, not decision-making. Agentic AI builds on this by introducing autonomous action – planning, adapting, and executing tasks in real time to support evolving workforce needs more intelligently and at scale. 

Leading Use Cases of Agentic AI in HR 

Agentic AI is redefining how HR operates; not by replacing people, but by giving teams a responsive, intelligent system that works behind the scenes to personalise, prioritise, and act. These capabilities help HR teams move from static workflows to living, adaptive systems that support employees in real time. 

Onboarding Orchestration. Agentic AI coordinates onboarding journeys dynamically – scheduling meetings, nudging mentors, tracking task completion, and adapting the flow based on real-time feedback. If a new hire flags confusion or drops off mid-process, the system adjusts instantly, resends steps, or escalates support. The result is a personalised, seamless experience that sets the tone for inclusion and engagement from day one. 

Attrition Prediction and Retention Planning. By monitoring signals like reduced engagement, sudden PTO, or changes in team behaviour, Agentic AI can identify at-risk employees before they resign. It then suggests targeted retention strategies based on context, such as recognition nudges, growth conversations, or team adjustments, allowing HR to intervene early and with precision. 

HR Service Delivery at Scale. Agentic AI answers common employee queries about leave balances, policies, and benefits immediately and accurately, across channels like Slack or email. It reduces wait times, lowers HR workload, and ensures employees get consistent, policy-aligned answers. Complex or sensitive cases are routed to the right human stakeholder with full context for faster resolution. 

Organisational Health Monitoring. Sentiment doesn’t live in surveys alone. Agentic AI aggregates data from exit interviews, Slack threads, survey responses, and internal communications to identify patterns – burnout risk, morale dips, misalignment – and surface them as real-time dashboards. This gives leaders continuous visibility into cultural health and the opportunity to act before small issues escalate. 

When GenAI and Agentic AI Work Together, HR Moves Faster – and Smarter 

The real power of AI in HR lies not in isolated tools but in the synergy between two complementary capabilities. GenAI provides content intelligence, efficiently drafting, summarising, and personalising at scale. Agentic AI adds a layer of orchestration, reasoning, planning, and acting in real time. Together, they move beyond simple task automation to fundamentally reshape how HR thinks, responds, and leads, turning reactive processes into predictive insights, shifting HR’s role from support to strategic partner, and transforming manual work into more meaningful, human-centred action. 

Beyond Tasks: AI as a System-Level Enabler 

While the figure highlights clear task-level gains, GenAI and Agentic AI also enable more advanced HR capabilities: 

Workforce Modelling and Headcount Planning. Agentic AI evaluates business priorities, project demands, and team capacity to recommend hiring, restructuring, or upskilling strategies. GenAI supports this by synthesising these insights into clear headcount proposals, role rationales, and scenario narratives for leadership decision-making. 

Policy Testing and Scenario Simulation. Whether trialling a hybrid work policy or reworking bonus schemes, Agentic AI can model their downstream effects on retention, productivity, and morale. GenAI helps HR teams communicate these implications through simulation reports and change briefings that bring potential outcomes to life. 

Culture Mapping and Sentiment Analysis. Agentic AI continuously gathers and interprets signals across employee surveys, internal chat platforms, and exit interviews to track how organisational values are expressed and where they may be eroding. GenAI turns these inputs into thematic summaries, heatmaps, and action plans for cultural reinforcement. 

Manager Coaching and Engagement Support. Based on indicators like rising absenteeism or declining engagement, Agentic AI nudges managers to take early action, whether that’s scheduling a one-on-one, shifting team priorities or offering recognition. GenAI adds value by generating tailored messaging and coaching templates to support those interventions. 

Together, GenAI and Agentic AI don’t just optimise HR; they help it lead with greater clarity, care, and conviction. 

Human-Centred HR, Powered by AI 

GenAI streamlines routine work, while Agentic AI enables HR to anticipate needs, adapt quickly, and lead with insight.  

This shift goes beyond efficiency; it’s about reimagining how HR supports people, culture, and performance. Rather than reducing the human element, AI frees HR professionals to focus on meaningful connections, coaching, and fostering inclusive workplaces. Agentic AI doesn’t replace empathy; it strengthens and extends it. 

For decades, marketing has evolved alongside technology – from the rise of digital channels to the explosion of data and automation. The latest transformation began with GenAI, which gave marketers the power to scale content, personalise at speed, and experiment like never before. 

But now, a more profound shift is underway. With Agentic AI, marketers can autonomously plan campaigns, optimise customer journeys, and drive decisions across the entire marketing lifecycle. We’re moving beyond faster execution toward truly adaptive, self-improving marketing engines. Where GenAI changed what marketing teams can do, Agentic AI changes how they operate. 

The New Marketing Continuum 

GenAI has fundamentally reshaped marketing by automating and enhancing creative and content-driven tasks. It enables marketers to produce content at unprecedented scale and speed. Blog posts, social media captions, email campaigns, and ad copy can now be generated in minutes, dramatically reducing production time.  

GenAI also empowers teams to personalise messages based on user preferences, behaviours, and historical data, boosting engagement and relevance. Beyond text, it can generate images, videos, and audio, allowing marketers to rapidly develop a wide variety of creative assets. Many also use it as a brainstorming partner, ideating on campaign themes, taglines, or content formats. By taking on repetitive, time-consuming tasks, GenAI frees up marketing teams to focus on higher-value strategic and analytical work. 

But while GenAI has transformed content creation, it still relies on human input to orchestrate campaigns and continuously optimise performance. That’s where agentic AI takes over, opening up the possibilities of autonomous marketing. 

Unlike traditional GenAI tools, agentic AI is guided by strategic goals and capable of executing multi-step workflows independently.  

These intelligent agents reason, plan, and learn from feedback, managing entire initiatives with minimal intervention. They don’t just generate content; they drive results. 

Leading Use Cases of Agentic AI in Marketing 

Campaign Orchestration. Agentic AI transforms campaign management from a sequence of manual tasks into a continuous, autonomous process. Once given a strategic goal, such as increasing product sign-ups, driving webinar attendance, or launching a regional campaign, the system independently plans and executes the end-to-end campaign. It determines the optimal mix of channels (email, paid social, display ads, etc.), generates creative assets tailored to each, sets targeting parameters, and initiates deployment. As results come in, it monitors performance metrics in real time and adjusts messaging, budget allocation, and channel focus accordingly. 

For marketers, the shift is profound: they move from building and launching campaigns to supervising and steering them, focusing on goals, governance, and refinement rather than day-to-day execution. 

Customer Journey Optimisation. Traditional customer journeys rely on pre-defined paths and segmentation rules. Agentic AI makes these journeys dynamic, responsive, and personalised at the individual level. By analysing behavioural data, such as browsing patterns, clickstream data, cart activity, and time-on-page, agentic systems adjust experiences in the moment. 

For example, if a visitor shows sustained interest in a product category but doesn’t convert, the AI can trigger a personalised follow-up via email, offer a discount, or retarget them with tailored messaging. These interactions evolve continuously as more data becomes available, optimising for engagement, conversion, and long-term retention. 

It’s no longer about mapping a linear funnel; it’s about orchestrating adaptive journeys at scale. 

Martech Integration and Workflow Automation. Most marketing environments are fragmented across dozens of tools; from CRM and CMS to analytics dashboards and ad platforms. Agentic AI acts as the connective tissue across this stack. It reads signals from various tools, automates routine updates (e.g., adding leads to nurture flows, flagging sales-ready accounts, triggering re-engagement ads), and maintains data consistency across systems. Rather than relying on manual workflows or brittle APIs, agentic systems interpret context and sequence actions logically. 

This unlocks both speed and reliability; campaigns launch faster, reporting becomes more accurate, and marketing teams waste less time on coordination overhead. 

Continuous Experimentation and Optimisation. Most marketing teams run experiments manually and intermittently – A/B testing headlines, adjusting audience segments, or switching out creative. Agentic AI turns experimentation into a continuous, embedded capability. 

It sets up and runs multivariate tests across copy, format, targeting, time slots, and more, simultaneously and at scale. Then, based on performance data, it autonomously selects winning combinations and rolls out adjustments in real time. 

Importantly, it learns over time, building a knowledge base of what works for which audiences under which conditions. Optimisation becomes a learning loop – continuous, automated, and compounding in value. 

Strategic Decision Support: Where GenAI and Agentic AI Converge 

The real power of AI in marketing emerges when generative intelligence meets agentic autonomy. Together, they move beyond content creation or task execution to support high-level strategic decision-making with speed, context, and adaptability. 

Scenario Modelling. Agentic AI identifies potential decision points such as budget shifts, product launches, channel mix changes, while GenAI simulates and narrates the implications of each, turning complex trade-offs into clear, actionable insights for leadership teams. 

Market Research Synthesis. Agentic systems continuously scan external sources, from competitor sites to analyst reports and social chatter. GenAI distils this noise into crisp summaries, opportunity maps, and trend briefings that inform strategy and messaging. 

Persona and Journey Analysis. Agentic AI tracks behaviour patterns and detects emerging segments or friction points across touchpoints. GenAI contextualises this data, creating personas and journey narratives that help teams align content and campaigns to real-world user needs. 

Content Localisation and Alignment. Agentic AI ensures local relevance by orchestrating updates across regions and personas. GenAI rapidly adapts messaging – tone, imagery, and language – while preserving brand voice, enabling consistent global storytelling at scale. 

Together, they give marketing leaders a dual advantage: real-time situational awareness and the ability to act on it with clarity and confidence. Decisions aren’t just faster; they’re smarter, more contextual, and closer to the customer. 

Responsible Intelligence: Operationalising AI in Marketing 

The potential of AI in marketing is significant, but responsible adoption is key. Human oversight remains critical to ensure alignment with brand tone, strategic direction, and ethical standards. AI systems must also integrate seamlessly with existing martech stacks to avoid complexity and inefficiencies. Strong data foundations – well-structured, high-quality, and accessible – are essential to generate relevant and reliable outputs. Finally, transparency and trust must be built into every system, with explainable and auditable AI behaviours that support accountability and informed decision-making. 

Agentic AI marks a step change in marketing; from faster execution to intelligent, autonomous operations. For marketing leaders, this is a moment to rethink workflows, redesign team roles, and build AI-native operating models. The goal isn’t just speed. It’s adaptability, intelligence, and sustained competitive advantage in a rapidly evolving landscape. 

The latest shift in AI takes us beyond data analysis and content generation. With agentic AI we are now seeing systems that can plan, reason, act autonomously, and adapt based on outcomes. This shift marks a practical turning point for operational execution and strategic agility.

Smart On-Ramps for Agentic AI

Technology providers are rapidly maturing their agentic AI offerings, increasingly packaging them as pre-built agents designed for quick deployment. These often require minimal customisation and target common enterprise needs – onboarding assistants, IT helpdesk agents, internal knowledge copilots, or policy compliance checkers – integrated with existing platforms like Microsoft 365, Salesforce, or ServiceNow.

For example, a bank might deploy a templated underwriting agent to pre-screen loan applications, while a university could roll out a student support bot that flags at-risk learners and nudges them toward action. These plug-and-play pilots let organisations move fast, with lower risk and clearer ROI.

Templated agents won’t suit every context, particularly where rules are complex or data is fragmented. But for many, they offer a smart on-ramp: a focused, contained pilot that delivers value, builds momentum, and lays the groundwork to scale Agentic AI more broadly.

Here are 10 such opportunities – five cross-industry and five sector-specific – ideal for launching agentic AI in your organisation. Each addresses a real-world pain point, with measurable impact and momentum for broader change.

Horizontal Use Cases

1. Employee Onboarding & Integration Assistant

An AI agent that guides new hires through their critical first weeks and months by answering FAQs about company policies, automating paperwork, scheduling introductory meetings, and sending personalised reminders to complete mandatory training, all integrated with HRIS, LMS, and calendaring systems. This can help reduce the administrative load on HR teams by handling repetitive onboarding tasks, potentially freeing up significant time, while also improving new hire satisfaction and accelerating time-to-productivity by providing employees with better support and engagement from day one.

Consideration. Begin with a specific department or a targeted hiring wave. Prioritise roles with high turnover or complex onboarding needs. Ensure HR data is clean and accessible, and policy documents are up to date.

2. Automated Meeting Follow-ups & Action Tracking

With permission, AI agents can listen to virtual meetings, identify key discussion points, summarise decisions, extract and assign action items with deadlines, and proactively follow up via email or collaboration platforms like Slack or Teams to help ensure tasks are completed. By integrating with meeting platforms, project management tools, and email, this can reduce the burden of manual note-taking and follow-up, potentially saving team members 1-2 hours per week, while also improving execution rates and accountability to make meetings more action-focused.

Consideration. Deploy with a small, cross-functional team that has frequent meetings. Clearly communicate the agent’s role and data privacy protocols to ensure user comfort and compliance.

3. Intelligent Procurement Assistant

An agent that interprets internal requests, initiates purchase orders, compares vendor options against predefined criteria, flags potential compliance issues based on policies and spending limits, and manages approval workflows, integrating with ERP systems, vendor databases, and internal policy documents. This can help accelerate procurement cycles, reduce manual errors, and lower the risk of non-compliant spending, potentially freeing procurement specialists to focus more on strategic sourcing rather than transactional tasks.

Consideration. Begin with a specific category of low-to-medium value purchases (e.g., office supplies, standard software licenses). Define clear, rule-based policies for the agent to follow.

4. Enhanced Sales/Outreach Research Agent

Given a target account, citizen segment, or potential beneficiary profile, this agent autonomously gathers and synthesises insights from CRM data, public financial records, social media, news feeds, and industry reports. It then generates tailored talking points, personalised outreach messages, and intelligent discovery questions for human operators. This can provide representatives with deeper insights, potentially improving their preparation and boosting early-stage conversion rates, while reducing manual research time significantly and allowing teams to focus more on building relationships.

Consideration. Train the agent on a specific sales vertical or a targeted public outreach campaign. Ensure robust data privacy compliance when accessing and synthesising public information.

5. Proactive Internal IT Helpdesk Agent

This agent enables employees to describe technical issues in natural language through familiar platforms like Slack, Teams, or internal portals. It can intelligently troubleshoot problems, guide users through self-service solutions from a knowledge base, or escalate more complex issues to the appropriate IT specialist, often pre-filling support tickets with relevant diagnostic information. This approach can lead to faster issue resolution, reduce the number of common support tickets, and improve employee satisfaction with IT services, while freeing IT staff to focus on more complex problems and strategic initiatives.

Consideration. Start with a well-documented set of frequently asked questions (FAQs) or common Tier 1 IT issues (e.g., password resets, VPN connection problems). Ensure a clear escalation path to human support.

Industry-Specific Use Cases

6. Intelligent Insurance Claims Triage (Insurance)

This agent reviews incoming insurance claims by processing unstructured data such as claim descriptions, photos, and documents. It automatically cross-references policy coverage, identifies missing information, and assigns priority or flags potential fraud based on predefined rules and learned patterns. This can speed up initial claims processing, reduce the manual workload for claims adjusters, and improve the early detection of suspicious claims, helping to lower fraud risk and deliver a faster, more efficient customer experience during a critical time.

Consideration. Focus on a specific, high-volume, and relatively standardized claim type (e.g., minor motor vehicle damage, simple property claims). Ensure robust data integration with policy management and fraud detection systems.

7. Automated Credit Underwriting Assistant (Banking)

An AI agent that pre-screens loan applications by gathering and analysing data from internal banking systems, external credit bureaus, and public records. It identifies key risk factors, generates preliminary credit scores, and prepares initial decision recommendations for human loan officers to review and approve. This can significantly shorten loan processing times, improve consistency in risk assessments, and allow human underwriters to concentrate on more complex cases and customer interactions.

Consideration. Apply this agent to a specific, well-defined loan product (e.g., unsecured personal loans, small business loans) with clear underwriting criteria. Strict human-in-the-loop oversight for final decisions is paramount.

8. Clinical Trial Workflow Coordinator (Healthcare)

This agent monitors clinical trial timelines, tracks participant progress, flags potential non-compliance or protocol deviations, and coordinates tasks and communication between research teams, labs, and regulatory bodies. Integrated with Electronic Health Records (EHRs), trial management systems, and regulatory databases, it helps reduce delays in complex clinical workflows, improves adherence to strict protocols and regulations, and enhances data quality, potentially speeding up drug development and patient access to new treatments.

Consideration. Focus on a single phase of a trial or specific documentation compliance checkpoints within an ongoing study. Ensure secure and compliant access to sensitive patient and trial data.

9. Predictive Maintenance Scheduler (Manufacturing)

By continuously analysing real-time IoT sensor data from machinery, this agent uses predictive analytics to anticipate potential equipment failures. It then schedules maintenance at optimal times, taking into account production schedules, spare part availability, and technician workloads, and automatically assigns tasks. This approach can significantly boost machine uptime and overall equipment effectiveness by reducing unplanned downtime, optimize technician efficiency, and extend asset lifespan, resulting in notable cost savings.

Consideration. Implement for a critical, high-value machine or a specific production line where downtime is extremely costly. Requires reliable and high-fidelity IoT sensor data.

10. Personalised Student Success Advisor (Higher Education)

This agent analyses student performance data such as grades, attendance, and LMS activity to identify those at risk of struggling or dropping out. It then proactively nudges students about upcoming deadlines, recommends personalised learning resources, and connects them with tutoring services or academic advisors. This support can improve retention rates, contribute to better academic outcomes, and enhance the overall student experience by providing timely, tailored assistance.

Consideration. Start with a specific cohort (e.g., first-year students, transfer students) or focus on a particular set of foundational courses. Ensure ethical data usage and transparent communication with students about the agent’s role.

Pilot Success Framework: Getting Started Today

As we have seen in the considerations above, starting with a high-impact, relatively low-risk use case is the recommended approach for beginning an agentic AI journey. This focuses on strategic, measured steps rather than a massive initial overhaul. When selecting a first pilot, organisations should identify projects with clear boundaries – specific data sources, explicit goals, and well-defined actions – avoiding overly ambitious or ambiguous initiatives.

A good pilot tackles a specific pain point and delivers measurable benefits, whether through time savings, fewer errors, or improved user satisfaction. Choosing scenarios with limited stakeholder risk and minimal disruption allows for learning and iteration without significant operational impact.

Executing a pilot effectively under these guidelines can generate momentum, earn stakeholder support, and lay the groundwork for scaling AI-driven transformation throughout the organisation. The future of autonomous operations begins with such focused pilots.

Over the past year of moderating AI roundtables, I’ve had a front-row seat to how the conversation has evolved. Early discussions often centred on identifying promising use cases and grappling with the foundational work, particularly around data readiness. More recently, attention has shifted to emerging capabilities like Agentic AI and what they mean for enterprise workflows. The pace of change has been rapid, but one theme has remained consistent throughout: ROI.

What’s changed is the depth and nuance of that conversation. As AI moves from pilot projects to core business functions, the question is no longer just if it delivers value, but how to measure it in a way that captures its true impact. Traditional ROI frameworks, focused on immediate, measurable returns, are proving inadequate when applied to AI initiatives that reshape processes, unlock new capabilities, and require long-term investment.

To navigate this complexity, organisations need a more grounded, forward-looking approach that considers not only direct gains but also enablement, scalability, and strategic relevance. Getting this right is key to both validating today’s investments and setting the stage for meaningful, sustained transformation.

Here is a summary of the key thoughts around AI ROI from multiple conversations across the Asia Pacific region.

1. Redefining ROI Beyond Short-Term Wins

A common mistake when adopting AI is using traditional ROI models that expect quick, obvious wins like cutting costs or boosting revenue right away. But AI works differently. Its real value often shows up slowly, through better decision-making, greater agility, and preparing the organisation to compete long-term.

AI projects need big upfront investments in things like improving data quality, upgrading infrastructure, and managing change. These costs are clear from the start, while the bigger benefits, like smarter predictions, faster processes, and a stronger competitive edge, usually take years to really pay off and aren’t easy to measure the usual way.

Ecosystm research finds that 60% of organisations in Asia Pacific expect to see AI ROI over two to five years, not immediately.

The most successful AI adopters get this and have started changing how they measure ROI. They look beyond just money and track things like explainability (which builds trust and helps with regulations), compliance improvements, how AI helps employees work better, and how it sparks new products or business models. These less obvious benefits are actually key to building strong, AI-ready organisations that can keep innovating and growing over time.

2. Linking AI to High-Impact KPIs: Problem First, Not Tech First

Successful AI initiatives always start with a clearly defined business problem or opportunity; not the technology itself. When a precise pain point is identified upfront, AI shifts from a vague concept to a powerful solution.

An industrial firm in Asia Pacific reduced production lead time by 40% by applying AI to optimise inspection and scheduling. This result was concrete, measurable, and directly tied to business goals.

This problem-first approach ensures every AI use case links to high-impact KPIs – whether reducing downtime, improving product quality, or boosting customer satisfaction. While this short-to-medium-term focus on results might seem at odds with the long-term ROI perspective, the two are complementary. Early wins secure executive buy-in and funding, giving AI initiatives the runway needed to mature and scale for sustained strategic impact.

Together, these perspectives build a foundation for scalable AI value that balances immediate relevance with future resilience.

3. Tracking ROI Across the Lifecycle

A costly misconception is treating pilot projects as the final success marker. While pilots validate concepts, true ROI only begins once AI is integrated into operations, scaled organisation-wide, and sustained over time.

Ecosystm research reveals that only about 32% of organisations rigorously track AI outcomes with defined success metrics; most rely on ad-hoc or incomplete measures.

To capture real value, ROI must be measured across the full AI lifecycle. This includes infrastructure upgrades needed for scaling, ongoing model maintenance (retraining and tuning), strict data governance to ensure quality and compliance, and operational support to monitor and optimise deployed AI systems.

A lifecycle perspective acknowledges the real value – and hidden costs – emerge beyond pilots, ensuring organisations understand the total cost of ownership and sustained benefits.

4. Strengthening the Foundations: Talent, Data, and Strategy

AI success hinges on strong foundations, not just models. Many projects fail due to gaps in skills, data quality, or strategic focus – directly blocking positive ROI and wasting resources.

Top organisations invest early in three pillars:

• Data Infrastructure. Reliable, scalable data pipelines and quality controls are vital. Poor data leads to delays, errors, higher costs, and compliance risks, hurting ROI.
• Skilled Talent. Cross-functional teams combining technical and domain expertise speed deployment, improve quality, reduce errors, and drive ongoing innovation – boosting ROI.
• Strategic Roadmap. Clear alignment with business goals ensures resources focus on high-impact projects, secures executive support, fosters collaboration, and enables measurable outcomes through KPIs.

Strengthening these fundamentals turns AI investments into consistent growth and competitive advantage.

5. Navigating Tool Complexity: Toward Integrated AI Lifecycle Management

One of the biggest challenges in measuring AI ROI is tool fragmentation. The AI lifecycle spans multiple stages – data preparation, model development, deployment, monitoring, and impact tracking – and organisations often rely on different tools for each. MLOps platforms track model performance, BI tools measure KPIs, and governance tools ensure compliance, but these systems rarely connect seamlessly.

This disconnect creates blind spots. Metrics sit in silos, handoffs across teams become inefficient, and linking model performance to business outcomes over time becomes manual and error prone. As AI becomes more embedded in core operations, the need for integration is becoming clear.

To close this gap, organisations are adopting unified AI lifecycle management platforms. These solutions provide a centralised view of model health, usage, and business impact, enriched with governance and collaboration features. By aligning technical and business metrics, they enable faster iteration, responsible scaling, and clearer ROI across the lifecycle.

Final Thoughts: The Cost of Inaction

Measuring AI ROI isn’t just about proving cost savings; it’s a shift in how organisations think about value. AI delivers long-term gains through better decision-making, improved compliance, more empowered employees, and the capacity to innovate continuously.

Yet too often, the cost of doing nothing is overlooked. Failing to invest in AI leads to slower adaptation, inefficient processes, and lost competitive ground. Traditional ROI models, built for short-term, linear investments, don’t account for the strategic upside of early adoption or the risks of falling behind.

That’s why leading organisations are reframing the ROI conversation. They’re looking beyond isolated productivity metrics to focus on lasting outcomes: scalable governance, adaptable talent, and future-ready business models. In a fast-evolving environment, inaction carries its own cost – one that may not appear in today’s spreadsheet but will shape tomorrow’s performance.

We’re entering a new cycle of PC device growth, driven by the end-of-life of Windows 10 and natural enterprise upgrade cycles, brought into alignment by the COVID-era device boom. In Asia Pacific, PC shipments are expected to grow by 4-8% in 2025. The wide range reflects uncertainty linked to the US tariff regime, which could impact device pricing and availability in the region as manufacturers adjust to shifting demand globally.

To AI or Not to AI?

“AI PCs” (or Copilot PCs) are set to become a growing segment, but real AI benefits from these devices are still some way off. Microsoft’s announcement to embed Agentic AI capabilities into the OS marks the first step toward moving AI processing from the cloud to the desktop. However, for most organisations, these capabilities remain 12-24 months away.

This creates a strategic question: should organisations invest now in NPU-enabled devices that may not deliver immediate returns? Given typical refresh cycles of 3-5 years, it’s worth considering whether local AI processing could become relevant during that time. The safer bet is to invest in Copilot or AI PCs now, as the AI market is evolving rapidly; and the chances of NPUs becoming useful sooner rather than later are high.

Is the Desktop Being Left Behind?

PC market growth is concentrated in the laptop segment, drawing most manufacturers and chip providers to focus their innovation there. AI and Copilot PCs have yet to meaningfully enter the desktop space, where manufacturers remain largely focused on gaming.

This creates a gap for enterprises and SMEs. AI capabilities available on laptops may not be mirrored on desktops. Recent conversations with infrastructure and End-User Computing (EUC) managers suggest a shift in Asia Pacific toward laptops or cloud/ virtual desktop infrastructure (VDI) devices, including thin clients and desktops. If this trend continues, organisations will need to re-evaluate employee experience and ensure applications are designed to match the capabilities of each device type and user persona.

Fundamental EUC Drivers are Changing

As EUC and infrastructure teams revisit their strategies, several foundational drivers are undergoing significant change:

• Remote work is no longer a default. Once considered the norm for information workers, remote work is now being reconsidered. With some organisations mandating full-time office returns, device strategies must adapt to a more hybrid and unpredictable working model.
• Employee Experience is losing budget priority. During the pandemic, keeping employees productive and engaged was critical. But with rising cost pressures, growing automation through GenAI and Agentic AI, and changing labour dynamics, EX is no longer a top enterprise priority and budgets reflect that shift.
• Cloud-based EUC solutions are now enterprise-ready. Since 2022, cloud adoption in EUC has accelerated. Solutions like Microsoft 365, Google Workspace, AWS WorkSpaces, and VMware Horizon Cloud now offer mature capabilities. Unified Endpoint Management (UEM) is increasingly cloud-managed, enabling more scalable and agile IT operations.
• Zero-trust is moving security closer to the user. EUC security is evolving from perimeter-based models to identity-centric, continuous verification approaches. Investments in EDR, AI-driven threat analytics, MFA, biometric authentication, and proactive threat hunting are now standard, driven by the shift to zero trust.
• Device diversity is increasing. Standardised device fleets are giving way to more diverse options – touchscreen laptops, foldables, and a broader mix of PC brands. Enterprise offerings are expanding beyond traditional tiers to meet varied needs across user personas.
• Metrics are shifting from technical to outcome-based. Traditional KPIs like uptime and cost are giving way to metrics tied to business value – employee productivity, experience, collaboration, cyber resilience, and adaptability. EUC success is now measured in terms of outcomes, not just infrastructure performance.

Build a Modern and Future-Ready EUC Strategy

Organisations must reassess their plans to align with changing business needs, user expectations, and operational realities. Modern EUC strategies must account for a broad set of considerations.  

Key factors to consider:

Strategic Business Alignment

• Business Outcomes. EUC strategies must align with core business goals such as boosting productivity, enhancing employee experience, improving customer outcomes, and driving competitive advantage. Consider how device choices enable new work models, such as remote/hybrid setups, gig workforce enablement, and cross-border collaboration.
• Digital Transformation Fit. Ensure EUC refresh cycles are integrated with broader digital transformation efforts – cloud migration, AI adoption, automation, and innovation. Devices should be future-ready, capable of supporting the AI and automation needs of 2026 and beyond. While some workloads may shift to the cloud, others like GenAI-powered video and image creation, may demand stronger local processing across the broader workforce, not just specialist teams.

Technology Considerations

Qualtrics, a leading global Voice of Customer (VoC) provider, held its annual X4 conference in May, at the ICC in Sydney. The event included an exclusive media lunch and focused on Qualtrics’ latest announcements and product enhancements, many of which were first unveiled at its US event in March.

The conference combined insights into the company’s technology roadmap with real-world customer success stories, featuring organisations such as KFC, ServiceNow, David Jones, Hilton, and others.

In a world dominated by AI agents, the opportunity lies in building real human connections. The challenge, however, is to do this at scale. Empowering people with AI agents, rather than replacing them, can improve efficiency while also creating space for more empathetic, human-centred interactions, the vendor argues. The theme of building connections and making every connection count came through loud and clear and was weaved through the product announcements.  

Here are my key takeaways from attending the conference.

Culture is Key

It was refreshing to see culture take centre stage at a vendor briefing – a critical pillar for CX success that’s too often overlooked in technology conversations.

While technology is critical to enable a successful CX practice and continuously improve customer experience, building a culture of customer centricity must be the foundation for technology to be successful.