Tag: GenAI

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. 

 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. 

AI can no longer be treated as a side experiment; it is often embedded in core decisions, customer experiences, operations, and innovation. And as adoption accelerates, so does regulatory scrutiny. Around the world, governments are moving quickly to set rules on how AI can be used, what risks must be controlled, and who is held accountable when harm occurs. 

This shift makes Responsible AI a strategic imperative – not just a compliance checkbox. It’s about reducing reputational risk, protecting customers and IP, and earning the trust needed to scale AI responsibly. Embedding transparency, fairness, and accountability into AI systems isn’t just ethical, it’s smart business. 

Understanding the regulatory landscape is a key part of that responsibility. As frameworks evolve, organisations must stay ahead of the rules shaping AI and ensure leadership is asking the right questions.  

EU AI Act: Setting the Standard for Responsible AI  

The EU AI Act is the world’s first comprehensive legislative framework for AI. It introduces a risk-based classification system: minimal, limited, high, and unacceptable. High-risk applications, including those used in HR, healthcare, finance, law enforcement, and critical infrastructure, must comply with strict requirements around transparency, data governance, ongoing monitoring, and human oversight. Generative AI models above certain thresholds are also subject to obligations such as disclosing training data sources and ensuring content integrity. 

Although an EU regulation, the Act has global relevance. Organisations outside the EU may fall within its scope if their AI systems impact EU citizens or markets. And just as the GDPR became a de facto global standard for data protection, the EU AI Act is expected to create a ripple effect, shaping how other countries approach AI regulation. It sets a clear precedent for embedding safety, accountability, and human-centric principles into AI governance. As a result, it is one of the most closely tracked developments by compliance teams, risk officers, and AI governance leads worldwide.  

However, as AI governance firms up worldwide, Asia Pacific organisations must look beyond Europe. From Washington to Beijing, several regulatory frameworks are rapidly influencing global norms. Whether organisations are building, deploying, or partnering on AI, these five are shaping the rules of the game.  

AI Regulations Asia Pacific Organisations Must Track 

1. United States: Setting the Tone for Global AI Risk Management 

The U.S. Executive Order on AI (2023) signals a major policy shift in federal oversight. It mandates agencies to establish AI safety standards, governance protocols, and risk assessment practices, with an emphasis on fairness, explainability, and security, especially in sensitive domains like healthcare, employment, and finance. Central to this effort is the NIST AI Risk Management Framework (AI RMF), quickly emerging as a global touchstone. 

Though designed as domestic policy, the Order’s influence is global. It sets a high bar for what constitutes responsible AI and is already shaping procurement norms and international expectations. For Asia Pacific organisations, early alignment isn’t just about accessing the U.S. market; it’s about maintaining credibility and competitiveness in a global AI landscape that is rapidly converging around these standards. 

Why it matters to Asia Pacific organisations 

• Global Supply Chains Depend on It. U.S.-linked firms must meet stringent AI safety and procurement standards to stay viable. Falling short could mean loss of market and partnership access. 

• NIST Is the New Global Benchmark. Aligning with AI RMF enables consistent risk management and builds confidence with global regulators and clients. 

• Explainability Is Essential. AI systems must provide auditable, transparent decisions to satisfy legal and market expectations. 

• Security Isn’t Optional. Preventing misuse and securing models is a non-negotiable baseline for participation in global AI ecosystems. 

2. China: Leading with Strict GenAI Regulation 

China’s 2023 Generative AI Measures impose clear rules on public-facing GenAI services. Providers must align content with “core socialist values,” prevent harmful bias, and ensure outputs are traceable and verifiable. Additionally, algorithms must be registered with regulators, with re-approval required for significant changes. These measures embed accountability and auditability into AI development and signal a new standard for regulatory oversight. 

For Asia Pacific organisations, this is more than compliance with local laws; it’s a harbinger of global trends. As major economies adopt similar rules, embracing traceability, algorithmic governance, and content controls now offers a competitive edge. It also demonstrates a commitment to trustworthy AI, positioning firms as serious players in the future global AI market. 

Why it matters to Asia Pacific organisations 

• Regulatory Access and Avoiding Risk. Operating in or reaching Chinese users means strict content and traceability compliance is mandatory. 

• Global Trend Toward Algorithm Governance. Requirements like algorithm registration are becoming regional norms and early adoption builds readiness. 

• Transparency and Documentation. Rules align with global moves toward auditability and explainability. 

• Content and Data Localisation. Businesses must invest in moderation and rethink infrastructure to comply with China’s standards. 

3. Singapore: A Practical Model for Responsible AI 

Singapore’s Model AI Governance Framework, developed by IMDA and PDPC, offers a pragmatic and principles-led path to ethical AI. Centred on transparency, human oversight, robustness, fairness, and explainability, the framework is accompanied by a detailed implementation toolkit, including use-case templates and risk-based guidance. It’s a practical playbook for firms looking to embed responsibility into their AI systems from the start. 

For Asia Pacific organisations, Singapore’s approach serves as both a local standard and a launchpad for global alignment. Adopting it enables responsible innovation, prepares teams for tighter compliance regimes, and builds trust with stakeholders at home and abroad. It’s a smart move for firms seeking to lead responsibly in the region’s growing AI economy. 

Why it matters to Asia Pacific organisations 

• Regionally Rooted, Globally Relevant. Widely adopted across Southeast Asia, the framework suits industries from finance to logistics. 

• Actionable Tools for Teams. Templates and checklists make responsible AI real and repeatable at scale. 

• Future Compliance-Ready. Even if voluntary now, it positions firms to meet tomorrow’s regulations with ease. 

• Trust as a Strategic Asset. Emphasising fairness and oversight boosts buy-in from regulators, partners, and users. 

• Global Standards Alignment. Harmonises with the NIST RMF and G7 guidance, easing cross-border operations. 

4. OECD & G7: The Foundations of Global AI Trust 

The OECD AI Principles, adopted by over 40 countries, and the G7 Hiroshima Process establish a high-level consensus on what trustworthy AI should look like. They champion values such as transparency, accountability, robustness, and human-centricity. The G7 further introduced voluntary codes for foundation model developers, encouraging practices like documenting limitations, continuous risk testing, and setting up incident reporting channels. 

For Asia Pacific organisations, these frameworks are early indicators of where global regulation is heading. Aligning now sends a strong signal of governance maturity, supports safer AI deployment, and strengthens relationships with investors and international partners. They also help firms build scalable practices that can evolve alongside regulatory expectations. 

Why it matters to Asia Pacific organisations 

• Blueprint for Trustworthy AI. Principles translate to real-world safeguards like explainability and continuous testing. 

• Regulatory Foreshadowing. Many Asia Pacific countries cite these frameworks in shaping their own AI policies. 

• Investor and Partner Signal. Compliance demonstrates maturity to stakeholders, aiding capital access and deals. 

• Safety Protocols for Scale. G7 recommendations help prevent AI failures and harmful outcomes. 

• Enabler of Cross-Border Collaboration. Global standards support smoother AI export, adoption, and partnership. 

5. Japan: Balancing Innovation and Governance 

Japan’s AI governance, guided by its 2022 strategy and active role in the G7 Hiroshima Process, follows a soft law approach that encourages voluntary adoption of ethical principles. The focus is on human-centric, transparent, and safe AI, allowing companies to experiment within defined ethical boundaries without heavy-handed mandates. 

For Asia Pacific organisations, Japan offers a compelling governance model that supports responsible innovation. By following its approach, firms can scale AI while staying aligned with international norms and anticipating formal regulations. It’s a flexible yet credible roadmap for building internal AI governance today. 

Why it matters to Asia Pacific organisations 

• Room to Innovate with Guardrails. Voluntary guidelines support agile experimentation without losing ethical direction. 

• Emphasis on Human-Centred AI. Design principles prioritise user rights and build long-term trust. 

• G7-Driven Interoperability. As a G7 leader, Japan’s standards help companies align with broader international norms. 

• Transparency and Safety Matter. Promoting explainability and security sets firms apart in global markets. 

• Blueprint for Internal Governance. Useful for creating internal policies that are regulation-ready. 

Why This Matters: Beyond Compliance 

The global regulatory patchwork is quickly evolving into a complex landscape of overlapping expectations. For multinational companies, this creates three clear implications: 

• Compliance is no longer optional. With enforcement kicking in (especially under the EU AI Act), failure to comply could mean fines, blocked products, or reputational damage. 

• Enterprise AI needs guardrails. Businesses must build not just AI products, but AI governance, covering model explainability, data quality, access control, bias mitigation, and audit readiness. 

• Trust drives adoption. As AI systems touch more customer and employee experiences, being able to explain and defend AI decisions becomes essential for maintaining stakeholder trust. 

AI regulation is not a brake on innovation; it’s the foundation for sustainable, scalable growth. For forward-thinking businesses, aligning with emerging standards today will not only reduce risk but also increase competitive advantage tomorrow. The organisations that win in the AI age will be the ones who combine speed with responsibility, and governance with ambition. 

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.

In AI’s early days, enterprise leaders asked a straightforward question: “What can this automate?” The focus was on speed, scale, and efficiency and AI delivered. But that question is evolving. Now, the more urgent ask is: “Can this AI understand people?” 

This shift – from automation to emotional intelligence – isn’t just theoretical. It’s already transforming how organisations connect with customers, empower employees, and design digital experiences. We’re shifting to a phase of humanised AI – systems that don’t just respond accurately, but intuitively, with sensitivity to mood, tone, and need. 

One of the most unexpected, and revealing, AI use cases is therapy. Millions now turn to AI chat tools to manage anxiety, process emotions, and share deeply personal thoughts. What started as fringe behaviour is fast becoming mainstream. This emotional turn isn’t a passing trend; it marks a fundamental shift in how people expect technology to relate to them. 

For enterprises, this raises a critical challenge: If customers are beginning to turn to AI for emotional support, what kind of relationship do they expect from it? And what does it take to meet that expectation – not just effectively, but responsibly, and at scale? 

The Rise of Chatbot Therapy 

Therapy was never meant to be one of AI’s first mass-market emotional use cases; and yet, here we are. 

Apps like Wysa, Serena, and Youper have been quietly reshaping the digital mental health landscape for years, offering on-demand support through chatbots. Designed by clinicians, these tools draw on established methods like Cognitive Behavioural Therapy (CBT) and mindfulness to help users manage anxiety, depression, and stress. The conversations are friendly, structured, and often, surprisingly helpful. 

But something even more unexpected is happening; people are now using general-purpose AI tools like ChatGPT for therapeutic support, despite them not being designed for it. Increasingly, users are turning to ChatGPT to talk through emotions, navigate relationship issues, or manage daily stress. Reddit threads and social posts describe it being used as a therapist or sounding board. This isn’t Replika or Wysa, but a general AI assistant being shaped into a personal mental health tool purely through user behaviour. 

This shift is driven by a few key factors. First, access. Traditional therapy is expensive, hard to schedule, and for many, emotionally intimidating. AI, on the other hand, is always available, listens without judgement, and never gets tired. 

GenAI AI has truly transformed content creation by automating text, image, and video generation from simple prompts, slashing the time and skills once needed. Canva leads this shift, blending an intuitive interface with expansive templates and cutting-edge AI tools. This empowers anyone – individuals or businesses – to produce professional-quality visuals with ease, breaking down barriers and making design truly accessible. 

Canva’s “Create 2025” event in Los Angeles showcased its evolution from a simple design tool into a full enterprise platform for productivity, content creation, collaboration, and brand management – embedding visual communication across the modern workplace. For tech teams, marketers, and leaders, this shift brings opportunity but also demands careful strategy, integration, and governance to unlock Canva’s full potential in enterprise settings. 

Canva Create 2025: Key Announcements 

Visual Suite 2.0: A Unified Workspace & Single Design Canvas. Canva unveiled Visual Suite 2.0, a seamless platform combining presentations, documents, whiteboards, spreadsheets, and video editing into one design canvas. This unified workspace helps organisations streamline workflows, eliminate tool fragmentation, and ensure consistent visual communication across teams. 

Canva Sheets: Where Data Meets Design. Canva Sheets reimagines the spreadsheet by focusing on visualising data with rich charts, colour-coded cells, smart templates, automation, and AI-powered insights. Designed for teams that share data rather than just analyse it, Sheets empowers every user – including the “data shy” – to become a confident data analyst. 

Canva AI: GenAI for the Creative Enterprise. The enhanced Magic Studio integrates AI-driven writing, image editing, template creation, and video animation into one toolset. Features like Magic Write, Magic Design, and Magic Animate enable teams to create branded, engaging content at scale – quickly and cost-effectively – across the entire Canva platform. 

Canva Code: Low/No-Code Interactive Content. Canva Code enables users to build interactive content such as calculators, quizzes, websites, apps, and chatbots without complex coding. Combining this with Canva’s design and brand management tools lets teams create on-brand digital experiences and publish them to customers in minutes – transforming everyone into a coder and accelerating customer-facing innovation. 

Why Enterprises Should Adopt Canva 

Canva’s evolution into an enterprise platform offers several key advantages for larger organisations: 

• Streamlined Workflows. A unified workspace and single design canvas cuts the need to switch between tools, boosting efficiency and team collaboration. 

• Brand Consistency at Scale. Centralised brand controls and template governance ensure all content – from marketing to regional sales – stays on-brand. For example, eXp Realty’s central design team creates assets that agents nationwide confidently use, maintaining brand integrity. 

• Scalable Content Creation. GenAI accelerates content creation and localisation, while Canva Sheets lets designers update assets at scale, reducing days of work to a single click. 

• Cross-Functional Collaboration. By making design accessible, Canva empowers marketing, operations, sales, and finance teams to collaborate seamlessly on visuals, cutting bottlenecks. 

• Lower Barriers to Creativity. With an easy-to-learn platform, more employees can contribute to visual storytelling without needing design expertise. 

Beyond Licensing: Strategic Enterprise Adoption  

Successful enterprise adoption of tools such as Canva goes beyond licensing – it requires organisational change. Here’s how enterprises can prepare: 

1. Integration with the Digital Workplace Ecosystem 

Enterprises must integrate new platforms with the broader toolset employees use daily. Without this, they risk becoming just another siloed app, limiting adoption and ROI. 

• Enable SSO and identity management (e.g. via Azure AD or Okta). 

• Integrate with storage platforms like SharePoint, Google Drive, or Box. 

• Connect to collaboration and productivity tools such as Slack, Teams, Trello, and Salesforce. 

2. Structured Training and Enablement 

Though intuitive, enterprise features require tailored training to boost adoption and build a self-sustaining user community. Customers benefit from dedicated support – including brand kit setup, onboarding, billing, SSO configuration, and company-wide training with a dedicated Customer Success Manager. 

• Deliver role-based training for marketers, HR, sales, and support. 

• Establish champions in each business unit to drive adoption. 

• Provide regular updates and tips as new features launch. 

3. Design Governance and Brand Control 

Enterprises must address concerns around brand fragmentation. This ensures that the platform acts as brand enabler – not a brand risk. 

• Set up Brand Kits to enforce logos, fonts, and colours. 

• Use locked templates for consistency while enabling localisation. 

• Create layered permission structures to reflect organisational hierarchy. 

4. Data Security, Compliance and Governance 

As with any enterprise SaaS platform, security and compliance must be foundational and built into the rollout plan from day one. 

• Understand data residency and privacy policies. 

• Use admin controls, usage analytics, and audit logs to maintain oversight. 

• Define clear policies for external sharing and publishing. 

5. Defining Success Metrics 

Adoption should be measured by capturing metrics that enable IT and marketing leaders to demonstrate value to the C-suite. 

• Benchmark operations before and after rollout. 

• Track usage, asset creation, and publishing speed. 

• Monitor template use versus freeform content to gauge brand adherence. 

• Survey users on productivity improvements and satisfaction. 

Driving Adoption and Innovation: The Tech Team’s Mandate 

For the success of tools such as Canva in enterprise settings, technology teams must move beyond gatekeeping and become proactive enablers of adoption and innovation. This involves integrating them smoothly with identity management, storage, productivity, and collaboration tools to deliver a seamless user experience. At the same time, they must enforce strict security and access controls, manage user provisioning, and monitor usage to ensure compliance and safeguard sensitive data. 

But technology’s role doesn’t stop at governance. Teams need to set clear internal service standards, build strong vendor relationships, and drive consistent rollout across the organisation. Crucially, they should partner with business units to co-develop templates, embed these tools into daily workflows, and experiment with new features like AI-powered design, localisation, and self-service content creation.  

Ecosystm Opinion 

Canva is no longer just a tool for simple social posts or pitch decks; with its latest updates at Create 2025, it has evolved into a core platform for modern, visual-first enterprise communication. To fully realise this potential, organisations must approach Canva like any other critical enterprise platform – implementing the right structure, strategy, security, and support. For companies aiming to empower teams, speed up content creation, and maintain brand consistency at scale, Canva is now poised to take centre stage. 

Asia is undergoing a digital renaissance. Central to this transformation is the rise of digital natives: companies born in the digital era, built on cloud infrastructure, powered by data, and guided by customer-centric agility. Unlike traditional businesses retrofitting technology onto old models, Asia’s digital natives have grown up with mobile-first architecture, software-as-a-service models, and a mindset of continuous iteration. They’re not merely disrupting — they are reshaping economies, industries, and even governance. 

But we are seeing a new wave of change. The rise of Agentic AI – autonomous, multi-agent systems that handle workflows, decision-making, and collaboration with minimal human input – is set to redefine industries yet again. For digital natives, embracing Agentic AI is no longer optional. Those who adopt it will unlock unprecedented automation, speed, and scale. Those who don’t risk being leapfrogged by competitors operating faster, smarter, and more efficiently. 

The path forward demands evolution and collaboration. Digital natives must upgrade their capabilities, while large tech vendors must shift from selling solutions to co-creating them with these agile players. Together, they can accelerate time-to-market and build future-ready ecosystems. 

Reimagining Scale and Customer Experience 

Retail transformation is a continuous, dynamic journey of reinvention – driven by agility, experimentation, and the need to keep pace with ever-evolving consumer behaviour. It’s not a fixed destination but an ongoing process of innovation.

At its heart, retail transformation is about putting the customer squarely in control. It’s the strategic overhaul that allows retailers to truly understand individual desires, offering hyper-personalised journeys that blur the lines between online browsing and in-store discovery.

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