IDC

IDC’s 2024 Customer Experience Management Strategies survey found that businesses globally have shifted to a focus on improving the effectiveness of their customer experience (CX) investments while driving profitable revenue growth. IDC finds that C-suite priorities for customer experience are squarely focused on optimizing experience delivery, making experience consumption easy, and achieving experiential value parity. But achieving these directives requires organizations to rethink experience delivery as greater than just the sum of multi-channel, front-office interactions. When done right, IDC research shows that unified CX can lead to higher customer retention, improved advocacy and ultimately profitable revenue growth. What does it mean to orchestrate unified experiences across the full lifecycle of customer outcomes? IDC’s 2024 CX Path survey found that improving scale and consistency for orchestrating experiences across the enterprise was the #1 business driver for companies implementing customer experience orchestration solutions. To negotiate the complexity across channels, touchpoints, journeys, and systems/applications across functions, that deliver value-based outcomes based on changing customer context at every moment, organizations must adopt a multi-layered approach to CX delivery across all organizational layers, including middle and back-office functions. This involves integrating systems of record, insights, control, and engagement to ensure seamless and consistent customer interactions. Systems of Record Organizations need to address data fragmentation by unifying customer and operational data. This integration is crucial for scalable AI workloads and deeper customer insights. IDC’s 2024 CXMS survey found that only about 24% of organizations have access to a single source of customer data with full integration across the stack. By pooling data from front-office and back-office functions, companies can create a comprehensive view of the customer journey. Systems of Insights A cohesive fabric of customer insights is essential. Organizations should combine structured and unstructured data to generate actionable customer intelligence. Sharing and integrating these insights into daily operations can significantly improve customer engagement and business outcomes. Systems of Control The orchestration layer acts as the connective tissue, integrating various systems and ensuring real-time, context-aware customer engagement. This layer leverages AI and automation to manage workflows, business logic, and customer interactions, enhancing decision-making across the organization. Systems of Engagement This layer includes, but is not limited to, the various customer engagement channels across digital, physical, and blended, employee-facing tools and platforms that may be either exclusive or shared by both employees and customers, and various engagement channels through which 3rd parties engage with the brand. Organizations that isolate experience delivery to one stakeholder group will ultimately get left behind. Where do AI Agents and Agentic systems fit into intelligent experience orchestration? AI Agents are a route to deliver greater sophistication for unified experience orchestration. Data and computing power advancements and the availability of advanced reasoning models are driving more sophisticated, more advanced Agentic AI capabilities. Core characteristics of AI Agents make them optimally suited to address the gaps organizations must fill in order to deliver unified intelligent experience orchestration. AI Agents all share the following core characteristics: Planning: AI agents can plan and sequence actions to achieve specific goals. Empowers Al to break down complex tasks into manageable steps, developing structured approaches to problem-solving. The integration of LLMs has revolutionized their planning capabilities. Perception: AI agents can perceive and process information from their environment, to make them more interactive and context aware. This information includes visual, auditory, and other sensory data. Tool usage: Advanced AI agents can use various tools, such as code execution, search, and computation capabilities, to perform tasks effectively. AI agents often use tools through function calling. Multi-Agent Collaboration: Facilitates multiple Al agents working together, each with specialized roles, to tackle complex problems more effectively. Memory: AI agents have the ability to remember past interactions (tool usage and perception) and behaviors (tool usage and planning). They store these experiences and even perform self-reflection to inform future actions. This memory component allows for continuity and improvement in agent performance over time. This enables AI Agents to create a new layer of ‘intelligent actions’ on top of CX and business applications. AI Agents consume and hand off customer and operational data, insights, tasks, across systems, workstreams, processes, and business functions, in a continuous, context-aware, manner. A system of AI agents essentially functions as the glue / connective tissue that connects organizational functions, systems/applications (customer-facing, operational systems, and employee tools), and data. This offers organizations the needed automation and scale for seamless vertical integration through the multiple layers for unified experience orchestration. With 35% of enterprises reporting that fulfillment aspects of the customer journey don’t extend beyond front-office processes, enterprises face a significant gap in orchestrating and delivering whole journey customer experiences. Agentic AI presents an equally significant opportunity to unlock machine scale, beyond just productivity gains to improve connectedness across the entire CX ecosystem with intelligence at the core. AI Agents can even bridge gaps in the fragmented technology landscape at many organizations. However, harnessing value from Agentic AI for CX will require enterprises to address long-standing, foundational challenges such as customer and operational data integrity, unification, and governance, AI governance, and customer privacy/security. Crucially, organizations must prioritize approaches to address the workforce impact of GenAI and agentic technology capabilities up front, especially front-line employees, who will continue to remain an integral part of delivering excellent customer experiences. source

The U.S.-China tech rivalry has escalated to a new level this April 2025 with U.S. tariffs becoming a targeted trade tool. The Trump administration unleashed​ waves of tariffson Chinese goods: on March 4, a 10% tariff on all imports was imposed on top of raising tariffs from 10% to 20% on many Chinese electronics, machinery and industrial components; on April 2, ending of de minimis eligibility for China and Hong Kong (from May 2) and the “reciprocal tariffs” on key critical sectors imposed an additional 34%; and on April 8, an additional 50% tariff on semiconductors, EVs, and robotics was announced. There also continues to be tariff escalations, clarifications and exemptions like in cases where final products have more than 20% of U.S. produced components. Chinese imports can be as high as 245% on needles and syringes or as low as zero for children’s books. Imported smartphones, computers and electronics appear to be currently granted a partial tariff reprieve and may only be subject to the March tariffs of 20%. These adjustments will probably continue as the impacts are felt by American consumers and the global markets. Some view these measures as a means to derail China’s technological ascendancy by inflating costs, disrupting supply chains, and isolating it from global markets. Beijing’s counterstrategy, a mix of aggressive fiscal stimulus packages, supply chain resilience frameworks, and enforced technology self-reliance, suggests a calculated pivot to absorb short-term shocks while securing long-term growth. The question then is: can China’s 2025 policy playbook neutralize U.S. tariffs’ impacts and sustain its ICT ambitions? U.S. Tariffs’ Impact on China’s ICT Sector IDC’s 2025 projections reveal a sector under strain but adapting. Our baseline scenario, with 20% tariffs in place, China’s ICT spending is expected to grow at 9.1% driven by domestic AI, cloud, and industrial software demand. With 50% tariffs, IDC’s downside scenario will slow down growth to 5.7%, with consumer electronics (PCs, smartphones) declining ​7.6% due to inflated import duty. An optimistic scenario, with tariffs rolled back, sees China’s ICT spending growth at 9.9%, fueled by pent-up innovation and continued existing global partnerships. Contributing to China’s ICT spending in 2025 are the key trends we are seeing in software/cloud services (+10 to 16% YoY), largely due to organizations prioritization of digital efficiency, as well as in industrial technologies such as AI, IoT, automation, which remains resilient due to state subsidies. While the consumer hardware export market is expected to falter (e.g., iPhone costs rose 25% post-April hikes), domestic demand is expected to remain steady with government subsidies. China’s Growing Tech Independence There is increased emphasis on China’s “dual circulation” strategy that was originally a response to the U.S. tariffs and other sanctions introduced between 2018 and 2020. The strategy seeks to prioritize domestic consumption and non-western international trade to gain greater self-reliance and resilience. This strategy can be seen at work in the likes of DeepSeek whose open-source models are now powering a ​significant portion of Chinese Cloud services including Tencent, Alibaba and many more. Huawei’s Ascend AI chips also increased their share of AI-accelerator chips to ​27% in 2024 and is expected to reach 40% by the end of 2025. Companies’ Response: Increased Agility to Respond to Tariff Chaos Agility is the name of the game amid all this tariff chaos. Chinese tech giants are restructuring their supply chains by accelerating offshoring to Southeast Asia, shifting their assembly lines to sidestep tariffs. They are also diversifying their markets by pivoting to emerging markets, such as expanding electric vehicle and cloud service exports to tariff-immune regions. Some companies are also innovating operations by adopting leaner strategies like AI-powered factories to cut waste or using direct shipping tech from e-commerce platforms to bypass tariffs. China’s 5-Point Plan: A Phase-Matched Counterattack In response to each wave of tariffs, a 5-point plan helped blunt immediate impacts while increasing long-term leverage: 1. Domestic Demand Boost via “Consumer Upgrade Action” Plan With the aim of boosting domestic demand and spurring economic growth, the Chinese government has put in place subsidies and trade-in programs for eligible consumer goods. For smartphones, tablets, and smartwatches, the government subsidy is up to 15% of product price, capped at ¥500/item. This trade-in program is expanded in 2025 to apply to other electronics, EVs and home appliances as well, as illustrated in the following chart: The subsidies also target rural/low-tier cities for 5G adoption, smart home devices, and rural e-commerce logistics. There are also plans to stabilize consumer confidence through stock/real estate market reforms and wage growth policies. The effect of these subsidies can be seen in the latest sales-out PC shipments with flat growth of 1% in 1Q 2025 compared to -16% in 1Q 2024. 2. Increased Funding for Emerging Tech China’s $138B Innovation Fund aims to boost homegrown tech innovation and reduce foreign reliance amid escalating U.S. tariffs. It focuses on discovering and increasing “original technological breakthroughs” in early-stage startups in AI, quantum computing, hydrogen energy, biomanufacturing, and 6G technology. Funding is a combination of state capital and private/local government long-term (over 20 years) investments in R&D infrastructure and tech-to-product pipelines. The innovation fund also involves industry stakeholders such as the MIIT (Ministry of Industry and Information Technology), academia, enterprises (to enhance smart manufacturing), and foreign collaborators in the telecom/robotics sectors. The program also aims to cultivate and highlight domestic STEM talent to offset global supply chain risks, with existing success stories like DeepSeek. 3. China’s “Five Financial Priorities” Guidelines These guidelines provide financial support to organizations providing technology, green finance, digitalization, financial inclusion, and pension products and services. It uses technology investments to bolster innovation and self-reliance. Key measures include: comprehensive financing for national tech projects and SMEs via equity, debt, and insurance tools; capital market focus prioritizing early-stage investments in emerging technology through multi-layered markets; risk mitigation mechanisms to disperse R&D risks and expand venture capital/angel funding; and patient capital to cultivate long-term investments that nurture tech leaders, unicorns, and specialized SMEs. This framework integrates financial resources to advance China’s tech competitiveness and industrial

As Asia/Pacific businesses accelerate their digital transformation journeys, artificial intelligence (AI) is becoming a core innovation enabler. From identity and access management (IAM) to risk-based trust frameworks, AI is reshaping the cybersecurity landscape. However, as AI adoption grows, so do concerns around security, trust, and compliance.   According to IDC’s Asia/Pacific Security Study, 2024, 76.5% of enterprises in the region say that they are not confident in their organization’s ability to detect and respond to AI-powered attacks. Most are concerned about AI-driven vulnerability scanning by attackers, the rapid exploitation of zero-day vulnerabilities, increasingly personalized and effective social engineering attacks that leverage AI, and AI-powered ransomware attacks with dynamic negotiation and extortion tactics. The risk of AI-driven risk vectors increases in verticals dealing with sensitive and confidential information such as Banking and Financial Services (BFSI) and Healthcare as well as critical infrastructure sectors like energy, transportation, and telecommunications, where disruptions can have widespread consequences.  With cybersecurity emerging as a central theme across the region, AI-fueled business models must address key challenges:   How can organizations ensure AI systems are secure, transparent, and resilient?   How should regulatory frameworks evolve to accommodate AI-driven cybersecurity?   What steps can businesses take to balance AI innovation with trust?   How can enterprises implement a robust AI governance framework to manage security, compliance, and ethical risks effectively?  To navigate these challenges, enterprises must address three key areas that impact the secure and responsible deployment of AI:  1. Integration and Cost Barriers to AI Security Adoption  Despite its potential, AI-driven security automation struggles with integration issues and high costs. According to IDC FutureScape: Worldwide Security and Trust 2025 Predictions – Asia/Pacific (Excluding Japan) (APJ) Implications, by 2027, only 25% of consumer-facing companies in the region  will use AI-powered IAM (Identity and Access Management) for personalized, secure user experiences due to persistent difficulties with process integration and cost concerns, creating a trust gap in AI authentication and identity protection, particularly in consumer-facing sectors like retail, banking, and e-commerce.  2. Regulatory Fragmentation Complicates Compliance  Asia/Pacific’s inconsistent AI regulations make compliance difficult. While Singapore and Australia lead AI governance, India and ASEAN nations lag behind, creating inconsistencies in how businesses implement AI security solutions. China has implemented strict AI laws focused on security assessments and algorithmic transparency, while Japan follows a more flexible, self-regulatory approach emphasizing Responsible AI. One of the most critical shifts in cybersecurity will be the introduction of AI Bills of Materials (AI BoM). By 2028, 70% of data products will include a Data BoM, detailing how data was collected, processed, and consent was obtained. This evidentiary trail will be essential for demonstrating compliance and ensuring AI systems do not operate as black boxes. Alongside, AI governance is mandatory, rather than exploratory. Some nations have demonstrated leadership in already initiating AI governance frameworks – such as Singapore, Australia, India, and Japan – setting the stage for responsible and secure AI adoption across the region. These countries are proactively developing policies and frameworks to ensure AI-driven technologies align with security, compliance, and ethical standards.  3. Unchecked GenAI Adoption Creates Security and Compliance Risks  The rapid expansion of GenAI poses major security and governance challenges for enterprises. IDC predicts that in 2025, 20% of organizations in APJ will move from proof-of-concept (POC) to production in specific GenAI use cases without a comprehensive risk-based assessment of their trust capabilities, potentially creating a cybersecurity house-of-cards scenario. Key risks include data leaks, bias in AI models, and regulatory penalties as governments tighten AI security laws. Without proactive governance, enterprises risk non-compliance, reputational damage, and increased exposure to AI-driven threats.  To mitigate these risks and build trust in AI-powered security, organizations must establish a robust governance framework that ensures transparency, compliance, and operational resilience. This is where IDC’s Unified AI Governance Model comes into play.  IDC’s Unified AI Governance Model  IDC’s Unified AI Governance Model is a strategic framework that balances innovation with risk management, ensuring AI deployment aligns with compliance, security, transparency, and ethical standards. It is built on four key pillars: transparency and explainability, security and resilience, compliance and privacy protection, and human-in-the-loop (HITL) governance.  IDC defines AI governance as a system of laws, policies, frameworks, practices, and processes that enable organizations to manage AI risks while driving business value. Governance must be integrated into strategy rather than treated as a reactive measure. Without it, enterprises face operational inefficiencies, legal exposure, and reputational risks. The model also acknowledges external influences, such as regional regulations, ethical considerations, and societal expectations, which vary significantly across APJ markets. Ensuring that AI governance adapts to these external factors is critical for sustainable and trusted AI adoption.  IDC’s Unified AI Governance Model provides a structured approach to managing AI security and trust by addressing some key questions such as:   Who is using what data, and where is it stored?   How is personally identifiable information (PII) data protected through encryption or anonymization?   Are AI models being tested against risk controls and compliance requirements?   Is there a risk assessment framework for GenAI deployments?  Path Forward: Cybersecurity and AI Governance for Asia/Pacific Businesses  To foster a secure AI-driven future, businesses must take a proactive approach to cybersecurity and AI governance. Key steps include:  Embedding AI Bill of Materials (BoM) in Cybersecurity Practices: Developing transparent AI security frameworks that document data provenance, consent mechanisms, and compliance checkpoints.  Investing in AI-Powered (Identity and Access Management) IAM with Risk-Based Authentication: Incorporating adaptive authentication, behavioral analytics, and risk scoring to strengthen trust in AI-driven security systems, instead of relying solely on AI-driven IAM.  Conducting Comprehensive Risk Assessments for GenAI Deployments: Establishing robust governance policies to prevent unintended risks when moving from GenAI POC to production.  Integrating Autonomous AI for IT Operations: By 2027, GenAI and analytics deployments for IT operations use cases will increase team productivity by 15%, generating $1.5 billion in economic and business value. Automated IT service desk responses, anomaly detection, and predictive resource capacity planning will be critical for AI-enabled security frameworks.  Collaborating with Regional Regulatory Bodies: Actively participating in shaping AI governance

Technology is no longer just an enabler – it transforms how organizations function, compete, and deliver value.  Specifically, AI is now fundamentally changing business processes, operations, and experiences, something that presents CIOs with both challenges and opportunities. As a result, the role of the CIO and IT must evolve beyond an enabling function. Historically focused on operational excellence, CIOs now face a strategic imperative: becoming orchestrators of business value. Leveraging deep technical expertise and proactively acquiring new capabilities, CIOs are expected to effectively harness AI to drive meaningful innovation and measurable business outcomes. However, many CIOs and IT departments remain too focused on traditional IT and IT-related metrics, limiting their ability to drive broader business outcomes. Closing this gap requires a fundamental shift that was already anticipated as part of digitalization efforts but now intensified by the pressure to deliver value on the AI agenda. CIOs must evolve from technology stewards to strategic innovators, driving resilient and adaptive organizations. Those who proactively do and align AI with business priorities will shape their organization’s future, those who hesitate risk being left behind. To successfully navigate this evolution, CIOs must address six imperatives. This is what this looks like in practice. 1. Manage Regulatory Complexity and Enforce AI Governance Rapidly changing AI regulations present major hurdles. In the report IDC FutureScape: Worldwide CIO Agenda 2025 Predictions – Asia/Pacific (Excluding) Japan Implications, IDC predicts that in 2025, 50% of the Asia-based top 1000 (A1000) organizations will struggle with divergent regulatory changes and rapidly evolving compliance standards, challenging their ability to adapt to market conditions and drive AI innovation. CIOs must proactively address these challenges by developing agile compliance frameworks. Also, 41% of APEJ organizations are focusing on establishing organizational data governance policies for AI/GenAI usage, according to the IDC 2024 CIO Sentiment Survey. We expect this to increase as this regulatory complexity demands organizations to have unified AI governance, and IDC predicts that by 2025, 70% of organizations will be formalizing policies and oversight to address AI risks (e.g., ethical, brand, and PII), aligning AI governance with strategic business goals. CIOs must develop trust-centric AI governance models that align clearly with strategic business objectives. This can help organizations use AI responsibly, maintaining customer trust, while still capturing the benefits of rapid technological innovation. 2. Reduce Technical Debt to Accelerate Innovation Modernizing IT is the top strategic priority for 37% of CIOs in the Asia/Pacific region, according to the same survey. This is because technical debt creates complexity, slows innovation, and restricts the ability to effectively adopt and scale new technologies like AI. IDC predicts that responding to the drag of technical debt, 40% of CIOs in 2025 will drive enterprise initiatives in high-impact areas to remediate technical debt for competitive advantage. Clearing technical debt – from aging codebases to outdated systems, and inefficient processes – enables organizations to quickly adopt new technologies, reducing barriers to innovation and accelerating AI integration. CIOs who prioritize tackling technical debt will position their organizations to adopt technology innovations faster, ensuring readiness for more complex AI-driven transformations. This can help boost innovation, improve agility, and increase the return on technology investments. 3. Turn AI Experimentation into Enterprise Value Although AI adoption has rapidly moved from niche to mainstream, many organizations remain stuck in pilot paralysis, struggling to advance beyond the proof-of-concept (PoC) stage. According to the IDC’s 2024 Future Enterprise Resiliency and Spending (FERS) survey, wave 4, organizations in Asia Pacific conducted an average of 24 GenAI pilots over the past 12 months, but only 3 progressed into production, partly due to the lack of clear direction. In fact, IDC predicts that in 2026, over one-third of organizations will be stuck in the experimental, point-solution phase of AI experimentation, requiring a shift of focus to enterprise use cases to deliver ROI. This stagnation hinders competitiveness, slows growth, and increases exposure to ethical risks and regulatory scrutiny. CIOs must become orchestrators of business value by effectively partnering with other CxOs to translate unclear ideas into practical AI applications. They should establish an AI Center of Excellence (CoE) to centralize expertise, share best practices, and coordinate cross-functional teams, accelerating AI deployment and ensuring consistency. Additionally, CIOs must lead the creation of a strategic roadmap for responsible AI that maximizes business impact, ensures ethical deployment, and proactively mitigates risks. So, we expect that by 2026, 70% of CIOs will lead the creation of a strategic road map to rapidly implement responsible AI solutions, maximizing benefits while mitigating risks across their operations. CIOs who bridge the gap between innovative AI experimentation and enterprise-wide deployment will help their organizations capture substantial competitive advantages and achieve tangible financial returns. 4. Strengthen Cybersecurity with AI-Driven Defense Cybersecurity is much more than just an IT issue; it is a strategic business imperative. Yet, CIOs are ultimately responsible for safeguarding their organizations, particularly as threats grow increasingly sophisticated. IDC predicts that in 2026, 50% of CIOs will diversify and broaden security strategies across their organization’s IT and security teams to address new/fast-evolving threats to their technology and supply chain ecosystem. CIOs must actively integrate AI and ML into their cyber-defense systems to protect against advanced threats, both internal and external. AI-driven cybersecurity can help not only improve threat detection, but also enhance incident response times, potentially reducing risks to operations and reputation. CIOs who effectively leverage AI to protect their IT infrastructure can improve organizational resilience, positioning their organizations as leaders in cybersecurity effectiveness. 5. Embed Sustainability into IT Strategy Sustainability has become a core business priority and technology investments play a critical role in achieving organizations’ ESG goals. IDC predicts that by 2027, 50% of CIOs will be accountable for embedding sustainability goals into every technology project, measuring outcomes to refine investments and align with environmental objectives. CIOs must actively incorporate environmental considerations into IT investment decisions, embedding clear sustainability metrics into infrastructure development and AI initiatives. By proactively integrating sustainability into their technology agendas, CIOs are effectively linking technology leadership with broader corporate responsibility.

Just when businesses in Asia/Pacific thought they were getting to grips with artificial intelligence-led disruptions in their industries, here comes something new called Agentic AI and its ability to turn generative AI (GenAI) functionality and capabilities into actionable services. In practical terms, Agentic AI is more than just advanced chatbots, this is the next step in the evolution of AI, allowing AI agents to act with increased autonomy. A simplistic retail example would be, where previously the AI will recommend restaurants based on a user’s preferences, now it can book the restaurant and offer alternatives that match its users’ health and dietary needs (vegan, gluten-free, high protein, etc). From a marketing perspective, this progression in AI utilization has a drastic impact on how businesses promote their goods and services to potential customers. Previously, marketers would target their campaigns directly towards the customers but now the shortlisting and decisions are made by the AI. How does the AI choose the “right” product/service? What changes do marketing teams need to make to incorporate Agentic AI? Before we try and understand where we are going let’s first come to grips with where we are and look at the current state of marketing in Asia and how marketers are currently using AI. Some of the key goals for marketing in the region today are: Develop a more unified and enterprise-wide marketing strategy: Provide consistent marketing experiences across various touchpoints Personalization as a differentiator: Making marketing campaigns relevant to segments, micro-segments or even individuals based on their personal preferences and characteristics Streamline processes through automation Asian businesses, particularly those in China, have been relying on AI in all of these areas and some of the more common uses of AI are: Automated content creation (including visual content such as videos and images) for campaigns Predictive analytics to improve overall campaign effectiveness and performance Data analysis and insights into customer behavior trends, gauging public sentiment and achieving a better understanding of the customer journey More detailed insights of how Chinese firms are using AI in marketing can be found in IDC PeerScape: C2G Peer Insights to Augment Customer Intelligence Using Generative AI. By leveraging AI, marketers can optimize their marketing campaigns for targeted messaging to a wide range of customer segments across numerous online channels, while controlling distribution frequency to minimize advertising fatigue in a cost-effective method. For example, as shown in the below figure, digital advertising is still the most time-consuming process for marketers.  Actions such as amending and formatting communications for different digital mediums as well as determining which segments to target take a significant amount of time and prolong the time required to launch a campaign – all of which can now be done by GenAI and Agentic AI. With a better understanding of how marketers in the region are using AI, let’s now look at Agentic AI and what the future holds for marketing. Agentic AI Will Impact the Marketing Workforce Composition We will see that with continued use of AI, especially in campaign cost optimization,  impacts marketing workforce composition. Currently, marketers are using AI to take over mundane and repetitive tasks (e.g. formatting images across different social media platforms) which will eventually transition to taking over full-time marketing roles allowing humans to focus on more strategic initiatives. In the IDC FutureScape: Worldwide Chief Marketing Officer 2025 Predictions — Asia/Pacific (Excluding Japan) Implications, IDC lists the top most urgent trends that marketing leaders must pay attention to. One of our predictions on the impact of Agentic AI on workforces states that by 2028, 1 out of 5 marketing roles or functions will be held by an AI worker, shifting human expertise to driving strategy, creativity, ethics and managing a blended human and AI workforce. Increased Focus on AI Governance by Marketing not IT There will be a need to supervise and ensure proper performance with Agentic AI taking on more responsibilities. This will require monitoring by the marketing team themselves who know when something goes wrong as opposed to IT who monitor based on code alerts. This will require the marketing team to be trained on AI use, to make them comfortable with the use of AI and understand how it can help rather than replace them so they can truly appreciate the technology and learn  the processes and systems to use in finetuning AI performance or troubleshooting errors when they occur. Marketing Workflows Will Change The use of Agentic AI by consumers will force a change in the marketing mindset, creating new processes and areas of focus which will force businesses and marketers to rethink how they operate. As an example, let’s address the question raised earlier in this blog – How does the AI choose the “right” product/service? When the Internet and search engines grew in popularity, search engine optimization was created to improve the quality and quantity of website traffic. Companies had to rethink how they setup their websites to ensure it was “visible” in rankings to the search engines. In addition to this, many paid to have their websites listed on top of searches. IDC predicts that businesses in Asia will have to work with AI companies and start spending on Large Language Model (LLM) optimization in the same manner so that businesses and their products and services are visible to Agentic AI systems. By 2029, companies will spend up to 3x more on LLM optimization than search optimization to influence GenAI systems and raise the priority & ranking of their brands. The AI road ahead has no doubt more bumps and turns and marketers must be willing to meet these changes and challenges head on. Here are a few things marketers can do to prepare for Agentic AI: Build a portfolio of AI case studies and use cases to determine what works best for you. By matching thought leadership initiatives with AI-infused case studies, marketers will be able to develop campaigns that competitively differentiate their companies and products. Work with the technology team to ensure marketing