WordPress database error: [Table 'ecosystmprodwordpressdb_v1.geo_test' doesn't exist]
SHOW FULL COLUMNS FROM `geo_test`

Ecosystm Insights - A new age Technology Research platform to help you access latest market insights,expert opinions and research data
Agentic AI in Marketing: From Content to Campaign Command 

5/5 (1)

5/5 (1)

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 Research and Reports
0
0
Ground-Realities-Australia’s-Tech-Pulse
Ground Realities: Australia’s Tech Pulse

5/5 (1)

5/5 (1)

Australia is making meaningful progress on its digital journey, driven by a vibrant tech sector, widespread technology adoption, and rising momentum in AI. But realising its full potential as a leading digital economy will depend on bridging the skills gap, moving beyond surface-level AI applications, accelerating SME digital transformation, and navigating ongoing economic uncertainty. For many enterprises, the focus is shifting from experimentation to execution, using technology to drive efficiency, resilience, and measurable outcomes.

Increasingly, leaders are asking not just how fast Australia can innovate, but how wisely. Strategic choices made now will shape a digital future grounded in national values where technology fuels both economic growth and public good.

These five key realities capture the current state of Australia’s technology landscape, based on insights from Ecosystm’s industry conversations and research.

1. Responsible by Design: Australia’s Path to Trusted AI

AI in Australia is progressing with a strong focus on ethics and public trust. Regulators like ASIC and the OAIC (Office of the Australian Information Commissioner) have made it clear that AI systems, especially in banking, insurance, and healthcare, must be transparent and fair. Banks like ANZ and Commonwealth Bank, have developed responsible AI frameworks to ensure their algorithms don’t unintentionally discriminate or mislead customers.

Yet a clear gap remains between ambition and readiness. Ecosystm research shows nearly 77% of Australian organisations acknowledge progress in piloting real-world use cases but worry they’re falling behind due to weak governance and poor-quality data.

The conversation around AI in Australia is evolving beyond productivity to include building trust. Success is now measured by the confidence regulators, customers, and communities have in AI systems. The path forward is clear: AI must drive innovation while upholding principles of fairness, transparency, and accountability.

2. The New AI Skillset: Where Data Science Meets Compliance and Context

Australia is on track to face a shortfall of 250,000 skilled workers in tech and business by 2030, according to the Future Skills Organisation. But the gap isn’t just in coders or engineers; it’s in hybrid talent: professionals who can connect AI development with regulatory, ethical, and commercial understanding.

In sectors like finance, AI adoption has stalled not due to lack of tools, but due to a lack of people who can interpret financial regulations and translate them into data science requirements. The same challenge affects healthcare, where digital transformation projects often slow down because technical teams lack domain-specific compliance and risk expertise.

While skilled migration has rebounded post-pandemic, the domestic pipeline remains limited. In response, organisations like Microsoft and Commonwealth Bank are investing in cross-skilling employees in AI, cloud, and risk management. Government initiatives such as CSIRO’s Responsible AI program and UNSW’s AI education efforts are also working to build talent fluent in both technology and ethics.

Despite these efforts, Australia’s shortage of hybrid talent remains a critical bottleneck, shaping not just how fast AI is adopted, but how responsibly and effectively it is deployed.

3. Beyond Coverage: Closing the Digital Gap for Regional Australia

Australia’s vast geography creates a uniquely local digital divide. Despite the National Broadband Network (NBN) rollout, many regional areas still face slow speeds and outages. The 2023 Regional Telecommunications Review found that over 2.8 million Australians remain without reliable internet access. Industries suffer tangible impacts. GrainCorp, a major agribusiness, uses AI to communicate with workers during the harvest season, but regional connectivity gaps hinder real-time monitoring and analytics. In healthcare, the Royal Flying Doctor Service reports that poor internet reliability in remote areas undermines telehealth consultations, particularly crucial for Indigenous communities.

Efforts to address these gaps are underway. Telstra launched satellite services through partnerships with Starlink and OneWeb to cover remote zones. However, these solutions often come with prohibitive costs, particularly for smaller businesses, farms, and community organisations that cannot afford private network infrastructure.

The implications are clear: without reliable and affordable internet, regional enterprises will struggle to adopt AI, cloud-based systems, and digital tools that drive efficiency and equity. The next step must be a coordinated approach involving government, telecom providers, and industry, focused not just on coverage, but on quality, affordability, and support for local innovation. Bridging this digital divide is not simply about infrastructure, it’s about ensuring inclusive access to the tools that power modern business and essential services.

4. Resilience Over Defence: Australia’s Evolving Cybersecurity Focus

Australia’s cyber landscape has shifted sharply following major breaches like Optus, Medibank, and Latitude Financial, which pushed cybersecurity to the top of national agendas. In response, regulators and organisations have adopted a more urgent, coordinated stance. Under the Security of Critical Infrastructure (SOCI) Act, critical sectors must now report serious incidents within hours, enabling faster, government-led responses and stronger collective resilience.

Organisations across sectors are stepping up their defences, moving from reactive measures to proactive preparedness. NAB confirmed that it spends over USD 150M annually on cybersecurity, focusing on real-time threat hunting, simulation exercises, and red teaming. Telstra continues to run annual “cyber war games” involving IT, legal, and crisis communications teams to prepare for worst-case scenarios.

This collective focus signals a broader shift across Australian industries: cybersecurity maturity is no longer judged by perimeter defence alone. Instead, resilience – an organisation’s ability to detect, respond, and recover swiftly – is now the benchmark for protecting critical assets in an increasingly complex threat landscape.

5. Designing for the Long Term: Sustainability as a Core Capability

Organisations across Australia are under growing pressure – not only from regulators, but also from investors, customers, and communities – to demonstrate that their digital strategies are delivering real environmental and social outcomes. The bar has shifted from ESG disclosure to ESG performance. Technology is no longer just an efficiency lever; it’s expected to be a catalyst for sustainability transformation.

This expectation is especially acute in Australia’s core industries, where environmental impact is both material and highly scrutinised. In mining, for example, Rio Tinto’s 20-year renewable energy deal with Edify Energy aims to cut emissions by up to 70% at its Queensland aluminium operations by 2028. But the focus on transition is not limited to high-emission sectors. In financial services, institutions are actively supporting the shift to a low-carbon economy, from setting long-term net-zero targets to aligning lending practices with climate goals, including phasing out support for high-emission assets.

Yet for many, the path forward is still fragmented. ESG data often sits in silos, legacy systems constrain visibility, and ownership of sustainability metrics is scattered. Digital transformation efforts that treat ESG as an add-on, rather than embedding it into the foundations of data, governance, and decision-making, risk missing the mark. Australia’s next digital frontier will be measured not just by innovation, but by how effectively it enables a low-carbon, inclusive, and resilient economy.

Shaping Australia’s Digital Future

Australia’s technology journey is accelerating, but significant challenges must be addressed to unlock its full potential. Moving beyond basic digitalisation, the country is embracing advanced technologies as essential drivers of economic growth and productivity. Strong government initiatives and investments are creating a foundation for innovation and building a highly skilled digital workforce. However, overcoming barriers such as talent shortages, infrastructure gaps, and governance complexities is critical. Only by tackling these obstacles head-on and embedding technology deeply across organisations of all sizes can Australia transform automation into true data-driven autonomy and new business models, securing its position as a global digital leader.

AI Research and Reports
0
0
Responsible-AI,-Competitive-Advantage-A-Guide-to-Global-Regulation
Responsible AI, Competitive Advantage: A Guide to Global Regulation 

5/5 (1)

5/5 (1)

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. 

AI Research and Reports
0
0
Innovation-and-Backcasting-Paving-the-Roadmap-Forward-for-EU-Tech-Leadership
Innovation and Backcasting: Paving the Roadmap Forward for EU Tech Leadership

5/5 (1)

5/5 (1)

Earlier this week, during a virtual networking event, a group of tech entrepreneurs asked for an explanation of backcasting. I described it as not only a way to guide a firm and its technology toward a desired future, but also as a method for identifying and filling gaps along the way to maximise opportunity.

At this moment, the European Union (EU) stands at a pivotal moment with its renewed emphasis on fostering innovation and achieving digital independence. As geopolitical economic dynamics continue to evolve, a strategic approach is vital for EU tech vendors; not just to navigate uncertainty but to actively seize opportunities along the way for further advancement.

The reliance on global (read: US) tech players as platforms is seen as continuing to impact European innovation. Two recent announcements underpin this. Google warned EU antitrust regulators and its critics that the Digital Markets Act (DMA) is hampering innovation to the detriment of European users and businesses. However, Google is under pressure to address requested charges under the EU’s Digital Markets Act in building their case on compliance. And the EU set out plans to pool funding and expertise in quantum to build a competitive European ecosystem in this key technology area. This funding push goes into the private sector for assistance to attract private funding to help the EU take the lead in quantum technology by 2030. EU tech chief Henna Virkkunen said on 1 July, as she announced the EU Quantum Strategy, that the EU is working to cut its reliance in the sector on the US and China.

Can data save the day? And can filling those holes while backcasting the business direction create independence from reliance on big tech?

Data as the Fifth Freedom and the Drive for Tech Sovereignty

The EU’s single market is well-known for its four freedoms: the free movement of goods, services, capital, and labour. However, a 2024 report by former Italian Prime Minister Enrico Letta proposes a crucial “fifth freedom” – encompassing research, innovation, data, and knowledge. This recognises data’s role as a prime production factor in modern economies and a powerful catalyst for innovation.

The EU has acknowledged that over-reliance on global tech giants, often headquartered outside Europe, can hinder its strategic autonomy and economic security. This realisation has fuelled a push for “tech sovereignty”, an initiative aimed at building a robust European tech sector capable of global competition and reducing dependence on non-EU entities. Measures such as the European Commission’s AI strategy, the Digital Services Act (DSA), and the Digital Markets Act (DMA) are intended to strengthen the EU’s technological competitiveness and independence.

This drive goes beyond regulation. It’s about enabling the creation of new technologies and investing in strong European infrastructure across critical domains such as AI, quantum computing, and biotech. There’s also growing demand for European-based digital services, driven by privacy concerns and the concentration of power among non-EU tech firms.

A Strategic Opportunity: The US Tax Code Adjustment

Adding to this strategic window of opportunity is a fiscal development in the US tax code. Section 174 of the US tax code governs the treatment of Research and Development (R&D) expenditures. A significant change to this section came with the Tax Cuts and Jobs Act (TCJA) of 2017. For tax years beginning after December 31, 2021, companies can no longer immediately deduct 100% of their R&D expenses in the year they are incurred. For R&D conducted within the country, specified research or experimental expenditures (SREs) must now be amortised over a five-year period. All costs associated with software development are also explicitly categorised as Section 174 expenses and subject to these amortisation rules.

According to tax consulting firm KBKG, these changes have “significantly increased the tax burden on companies investing in innovation, potentially stifling economic growth and reducing the United States’ competitiveness on the global stage.” While bipartisan efforts are underway in the US to repeal or retroactively amend this change, the political landscape remains volatile, and any resolution may come too late for many affected companies.

This tax shift has been cited by many in the tech industry as a driver behind recent layoffs. It also presents a unique window of opportunity for EU tech firms to increase their R&D investments and gain a competitive edge while their US counterparts grapple with rising tax burdens on innovation.

Backcasting: Charting a Course for EU Tech Leadership

In an age characterised by unpredictability, policy shifts, trade disruptions, and macroeconomic instability, traditional forecasting often proves inadequate. This is where backcasting becomes an invaluable tool for EU tech vendors. Unlike forecasting, which projects current trends forward, backcasting starts with a clear, desired future state and works backward to identify the steps, milestones, and investments required to reach that vision, while helping to identify and fill critical gaps along the way.

This strategic approach offers several key advantages for European tech firms:

  • Strategic Alignment. Backcasting directly links today’s investments to tomorrow’s objectives, ensuring that current initiatives contribute purposefully to long-term ambitions.
  • Justifying Investment. By clearly outlining the journey from the present to the future, backcasting helps organisations articulate and justify long-term investments in innovation and R&D, crucial for securing budgets in a tightly scrutinised financial environment.
  • Adaptability and Agility. While grounded in a future vision, backcasting is an iterative process that allows course correction as the global landscape evolves, enabling teams to stay agile yet focused on their end goals.
  • Regional Resilience. In a world of increasingly fragmented global interconnectivity, using a backcasting framework to invest in local innovation, talent, and infrastructure strengthens economic independence and supports sustainable growth for the EU tech sector.

By adopting backcasting, EU tech vendors can turn the challenges of an uncertain future into a deliberate, confident path toward sustained growth and competitiveness. It marks a fundamental shift in mindset, empowering organisations not just to react to change, but to proactively shape the future they want to lead.

Partner with Ecosystm to Define Your Backcasting Journey

Envisioning the next decade – and systematically working backward from that future requires both imagination and rigour. My colleague Tim Sheedy and I at Ecosystm can help your organisation shape a clear, actionable backcasting strategy that connects long-term vision to immediate priorities.

We offer a range of ways to support your journey from workshops and internal training to client-facing sessions, webinars, and co-created content. Whether you’re looking to build internal capability or align stakeholders around future goals, we can tailor our approach to your needs.

If backcasting could support your growth or budget planning, we’d love to connect, either in person or via a quick call. And we welcome your feedback at any time. Feel free to reach out to me or Tim directly.

Forecasting is Dead – Use Backcasting to Win Budget for Big Moves
0
0
Building a Sustainable End-User Computing Strategy: A Practical Checklist for Responsible IT Leaders

5/5 (1)

5/5 (1)

In my previous insights, I explained why organisations need to rethink their End-User Computing (EUC) strategies and shared a simple checklist to help them build smarter, more responsible plans tailored to their goals, users, and regions.

As that foundation is laid, it’s critical to put sustainability at the core. From laptops and desktops to peripherals and accessories, the choices made around devices impact not only IT budgets and user productivity but also environmental footprints and regulatory compliance.

Sustainable EUC means selecting devices that align with your company’s climate goals, regulatory mandates, and ethical commitments, while delivering reliability and performance in diverse working environments.

This guide offers a comprehensive sustainability checklist to help IT leaders embed responsible sourcing and lifecycle management into their EUC strategy.

Click here to download “Building a Sustainable End-User Computing Strategy: A Practical Checklist for Responsible IT Leaders” as a PDF.

Building a Sustainable End-User Computing Strategy-1
Building a Sustainable End-User Computing Strategy-2
Building a Sustainable End-User Computing Strategy-3
Building a Sustainable End-User Computing Strategy-4
Building a Sustainable End-User Computing Strategy-5
Building a Sustainable End-User Computing Strategy-6
Building a Sustainable End-User Computing Strategy-7
Building a Sustainable End-User Computing Strategy-8
Building a Sustainable End-User Computing Strategy-9
previous arrowprevious arrow
next arrownext arrow
Building a Sustainable End-User Computing Strategy-1
Building a Sustainable End-User Computing Strategy-2
Building a Sustainable End-User Computing Strategy-3
Building a Sustainable End-User Computing Strategy-4
Building a Sustainable End-User Computing Strategy-5
Building a Sustainable End-User Computing Strategy-6
Building a Sustainable End-User Computing Strategy-7
Building a Sustainable End-User Computing Strategy-8
Building a Sustainable End-User Computing Strategy-9
previous arrow
next arrow
Shadow

What to Demand from Vendors & Devices

  • Specify recognised eco-label tiers (e.g., TCO Gen 9, EPEAT Climate+). Ensures devices meet verified environmental and social standards, reducing overall carbon footprint.
  • Request embodied-carbon disclosures (ISO 14067, PAS 2050). To understand full lifecycle emissions to inform refresh cycle decisions.
  • Insist on vendor-funded take-back in all deployment regions. Supports responsible recycling and circular economy for end-user devices.
  • Audit supply-chain ethics (latest RBA VAP score, Modern Slavery compliance). Certifies devices against verified environmental and social standards, cutting their overall carbon footprint.
  • Set minimum firmware support periods and repairability targets. Extends usable device lifespan, lowering total cost of ownership and e-waste.
  • Test devices for local climate conditions (humidity, altitude). Guarantees device reliability and energy efficiency in diverse workplaces.

Key Eco-Labels & Certifications for EUC Devices

Not all certifications are created equal. Here are the most relevant for end-user devices, what they mean, and recent updates to watch:

Key Eco-Labels & Certifications for EUC Devices

Regional Regulations & Compliance for EUC

EUC devices often span multiple jurisdictions; understanding regional regulations helps avoid compliance risks and future-proofs procurement:

Australia & New Zealand. Minimum Energy Performance Standards (MEPS) for monitors and power supplies; NTCRS take-back requirements; Modern Slavery Act disclosures

Singapore. Resource Sustainability Act (EPR for IT equipment) since 2021; green procurement guidelines for public sector

Japan. Minimum Energy Performance Standards (MEPS) for monitors and power supplies; NTCRS take-back requirements; Modern Slavery Act disclosures

China. China RoHS 2 with new 2024 testing standards for restricted substances

India. E-Waste (Management) Rules 2022 requiring OEMs/importers to collect 80% of products sold; ongoing amendments under legal review

South Korea. Eco-Label expansion to tablets and mini-PCs; EPR scheme in public tenders

Embedding Ethical Sourcing in Your EUC Strategy

Ethics matter beyond environmental impact; responsible sourcing reduces risk and protects brand reputation:

Responsible Business Alliance (RBA) Code of Conduct v8.0. Check for vendor audit results to ensure compliance.

Conflict Minerals / Responsible Minerals Initiative. Especially relevant for supply chains feeding US/EU markets.

Modern Slavery Legislation. Mandate supplier disclosures and risk assessments, especially in Australia and New Zealand.

Public Sector Procurement & EUC Sustainability

Many government buyers set strong sustainability expectations, which can serve as best-practice benchmarks:

Australia (Commonwealth & States). Preference for EPEAT Silver+, NTCRS take-back, and Modern Slavery compliance statements

Singapore GovTech. ENERGY STAR compliance, Resource Sustainability Act adherence, and use of low-halogen plastics

Japan National Procurement. Top Runner energy efficiency, Eco-Mark or equivalent certification

Why Sustainability Matters for End-User Computing

Sustainability in your EUC strategy drives more than just environmental benefits. It:

  • Reduces Total Cost of Ownership (TCO) by extending device lifecycles and lowering energy consumption
  • Mitigates Supply Chain Risks by ensuring ethical sourcing and regulatory compliance
  • Supports Corporate Climate Commitments with transparent carbon accounting and circular economy practices
  • Enhances User Satisfaction and Reliability by testing devices for local conditions and durability

By integrating these sustainability criteria into procurement, IT leaders can transform their EUC strategy into a powerful enabler of business value and responsible growth.

More Insights to tech Buyer Guidance
0
0
Unlocking-Autonomy-10-Agentic-AI-Pilots-That-Can-Transform-Organisations-Now
Unlocking Autonomy: 10 Agentic AI Pilots That Can Transform Organisations Now

5/5 (1)

5/5 (1)

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.

AI Research and Reports
0
0
Ground Realities: Singapore’s Tech Pulse

5/5 (1)

5/5 (1)

As one of Asia’s most digitally mature economies, Singapore was an early mover in national digital transformation and is now turning that head start into resilient, innovation-led economic value. Today, the conversation across boardrooms, regulators, and industry circles has evolved: it’s no longer just about adopting technology but about embedding digital as a systemic driver of competitiveness, inclusion, and sustained growth.

Singapore’s approach offers a model for the region, with its commitment to building a holistic digital ecosystem. This goes beyond infrastructure, it includes nurturing digital talent, fostering a vibrant innovation and startup culture, enabling trusted cross-border data flows, and championing public-private collaboration. Crucially, its forward-looking regulatory stance balances support for experimentation with the need to uphold public trust.

Through our conversations with leaders in Singapore and Ecosystm’s broader research, we see a country intentionally architecting its digital future, focused on real-world outcomes, regional relevance, and long-term economic resilience.

Here are five insights that capture the pulse of Singapore’s digital transformation.

Theme 1: Digital Governance as Strategy: Setting the Pace for Innovation & Trust

Singapore’s approach to digital governance goes beyond policy. It’s a deliberate strategy to build trust, accelerate innovation, and maintain economic competitiveness. The guiding principle is clear: technology must be both transformative and trustworthy.

This vision is clearly visible in the public sector, where digital platforms and services are setting the pace for the rest of the economy. Public service apps are designed to be citizen-centric, secure, and efficient, demonstrating how digital delivery can work at scale. The Government Tech Stack allows agencies to rapidly build and integrate services using shared APIs, cloud infrastructure, and secure data layers. Open data initiatives like Data.gov.sg unlock thousands of datasets, while tools such as FormSG and SG Notify make it easy for any organisation to digitise services and engage users in real time.

By leading with well-designed digital infrastructure and standards, the public sector creates blueprints that others can adopt, lowering the barriers to innovation for businesses of all sizes. For SMEs in particular, these tools and frameworks offer a practical foundation to modernise operations and participate more fully in the digital economy.

Singapore is also setting clear rules for responsible tech. IMDA’s Trusted Data Sharing Framework and AI Verify establish standards for secure data use and transparent AI, giving businesses the certainty they need to innovate with confidence. All of this is underpinned by strategic investments in digital infrastructure, including a new generation of sustainable, high-capacity data centres to meet growing regional demand. In Singapore, digital governance isn’t a constraint, it’s a catalyst.

Theme 2: AI in Singapore: From Experimentation to Accountability

Few places have embraced AI’s potential as strongly as Singapore. In 2022 and 2023, fuelled by the National AI Strategy and commercial pressure to deliver results, organisations across industries rushed into pilots in 2022 and 2023. Ecosystm research shows that by 2024, nearly 82% of large enterprises in Singapore were experimenting with AI, with 37% deploying it across multiple departments.

However, that initial wave of excitement soon gave way to realism. Leaders now speak candidly about AI fatigue and the growing demand for measurable returns. The conversation has shifted from “What can we automate?” to “What’s actually worth scaling?” Organisations are scrutinising whether their AI projects deliver tangible value, integrate into daily operations, and meet evolving regulatory expectations.

This maturity is especially visible in Singapore’s banking sector, where the stakes are high and scrutiny is intense. Banks were among the first to embrace AI aggressively and are now leading the shift toward disciplined prioritisation. From actively hunting down use cases, they’ve pivoted to focusing on the select few that deliver real business outcomes. With increasing pressure to ensure transparency, auditability, and alignment with global standards, finance leaders are setting the tone for AI accountability across the economy.

The result: a more grounded, impact-focused AI strategy. While many regional peers are still chasing pilots, Singapore is entering a new phase, defined by fewer but better AI initiatives, built to solve real problems and deliver meaningful ROI.

Theme 3: The Cyber Imperative: Trust, Recovery, and Resilience

Singapore’s digital leadership brings not only opportunities but also increased exposure to cyber threats. In 2024 alone, the country faced 21 million cyberattacks, ranking eighth globally as both a target and a source. High-profile breaches, from vendor compromises affecting thousands of banking customers to earlier incidents like the SingHealth data breach, have exposed vulnerabilities across critical sectors.

These incidents have sparked a fundamental shift in Singapore’s cybersecurity mindset from building impenetrable digital fortresses to embracing digital resilience. The government recognises that breaches are inevitable and prioritises rapid containment and recovery over prevention alone. Regulatory bodies like MAS have tightened incident reporting rules, demanding quicker, more transparent responses from affected organisations.

For enterprises in Singapore, cybersecurity has moved beyond a technical challenge to become a strategic imperative deeply tied to customer trust and business continuity. Leaders are investing heavily in real-time threat detection, incident response, and crisis management capabilities. In a landscape where vulnerabilities are real and constant, cyber resilience is now a critical competitive advantage because in Singapore’s digital economy, trust and operational reliability are non-negotiable.

Theme 4: Beyond Coding: Singapore’s Quest for Hybrid Digital Talent

Singapore’s digital ambitions increasingly depend on its human capital. While consistently ranking high in global talent competitiveness, the city-state faces a projected shortfall of over 1.2 million digitally skilled workers, particularly in fields like cybersecurity, data science, and AI engineering.

But the challenge isn’t purely technical. Organisations now demand talent that bridges technology, business strategy, and regulatory insight. Many digital initiatives stall not from technology limitations, but from a lack of professionals who can translate complex digital concepts into business value and ensure regulatory compliance.

To address this, government initiatives like the TechSkills Accelerator (TeSA) offer training subsidies and career conversion programmes. Meanwhile, leading tech providers including AWS, Microsoft, Google, and IBM, are stepping up, partnering with government and industry to deliver specialised training, certification programmes, and talent pipelines that help close the skills gap.

Still, enterprises grapple with keeping pace amid rapid technological change, balancing reskilling local talent with attracting specialised professionals from abroad. The future of Singapore’s digital economy will be defined as much by people as by technology; and by the partnerships that help bridge this critical gap.

Theme 5: Tracking Impact, Driving Change: Singapore’s Sustainability and Tech Synergy

Sustainability remains a core pillar of Singapore’s digital ambitions, driven by the government’s unwavering focus and supportive green financing options unlike in some markets where momentum has slowed. Anchored by the Singapore Green Plan 2030, the nation aims to double solar energy capacity and reduce landfill waste per capita by 30% by 2030.

Digital technology plays a critical role in this vision. Initiatives like the Green Data Centre Roadmap promote energy-efficient infrastructure and sustainable cooling technologies, balancing growth in the digital economy with carbon footprint management. Singapore is also emerging as a regional hub for carbon services, leveraging digital platforms such as the Carbon Services Platform to track, verify, and trade emissions, fostering credible and transparent carbon markets.

Government-backed green financing schemes, including the Green Bond Grant Scheme and Sustainability-Linked Loans, are accelerating investments in eco-friendly projects, enabling enterprises to fund sustainable innovation while meeting global ESG standards.

Despite these advances, leaders highlight challenges such as the lack of standardised sustainability metrics and rising risks of greenwashing, which complicate scaling green finance and cross-border sustainability reporting. Still, Singapore’s ability to integrate sustainability with digital innovation underscores its ambition to be more than a tech hub. It aims to be a trusted leader in building a responsible, future-ready economy.

From Innovation to Lasting Impact

Singapore stands at a critical inflection point. Already recognised as one of the world’s most advanced digital economies, its greatest test now is execution transforming cutting-edge technology from promise into real, everyday impact. The nation must balance rapid innovation with robust security, while shaping global standards that reflect its unique blend of ambition and pragmatism.

With deep-rooted trust across government, industry, and society, Singapore is uniquely equipped to lead not just in developing technology, but in embedding it responsibly to create lasting value for its people and the wider region. The next chapter will define whether Singapore can move from digital leadership to digital legacy.

AI Research and Reports
0
0
Ground-Realities-Leadership-Insights-on-AI-ROI
Ground Realities: Leadership Insights on AI ROI

5/5 (2)

5/5 (2)

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.

Head of Digital Innovation

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.

CIO

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.

Director of Data & AI Strategy

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.

CTO

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.

AI Strategy Lead

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.

AI Research and Reports
0
0