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  • AIA Indonesia Leadership Insight bersama Pak Ignasius Jonan

    Hari ini mendapat kesempatan belajar dari Pak Ignasius Jonan tentang kepemimpinan dan bagaimana membangun organisasi yang mampu terus berkembang, bahkan setelah pemimpinnya tidak lagi berada di dalamnya.

    Ada beberapa hal yang menarik bagi saya.

    1. Perubahan dimulai dari pola pikir.

    Mengubah sistem dan prosedur saja tidak cukup. Yang lebih penting adalah mengubah cara orang berpikir dan bekerja, hingga akhirnya menjadi budaya yang terus berjalan, bukan hanya karena arahan seorang pemimpin.

    2. Pahami kebutuhan pelanggan dan ciptakan nilai yang berbeda.

    Organisasi harus memahami apa yang benar-benar dibutuhkan pelanggan, memiliki keunikan dibandingkan kompetitor, dan memberikan nilai nyata melalui kualitas layanan. Pelanggan akan melihat nilai yang mereka dapatkan, bukan sekadar produk yang ditawarkan.

    3. Pemimpin harus mampu membangun tim, bukan hanya memberikan arahan.

    Kepemimpinan bukan tentang memaksakan kehendak. Pemimpin perlu membangun kesepakatan, menjelaskan alasan di balik keputusan, memberi contoh, dan berani menerima kritik. Tantangannya bukan sekadar mencapai target, tetapi membuat tim mampu bergerak bersama.

    Satu hal yang paling saya ingat dari sesi ini adalah:

    “Strategy is important, but execution is everything.”

    Strategi yang baik tidak akan berarti banyak tanpa pelaksanaan yang konsisten.

    Pada akhirnya, menurut saya, keberhasilan seorang pemimpin bukan hanya tentang hasil yang dicapai selama ia menjabat, tetapi juga tentang bagaimana ia membangun orang-orangnya dan meninggalkan organisasi yang lebih baik.

    Terima kasih AIA atas sesi pembelajaran yang sangat bermanfaat.

  • AI Should Not Be Built as a Collection of Use Cases

    Many companies are investing heavily in AI, yet struggle to translate that investment into measurable business value.

    The problem is often not the technology. It is the way AI is being built.

    AI should start with the business outcome, not the AI use case.

    A better approach is:

    1. Anchor AI to the P&L
    Define the business value first, revenue growth, productivity, cost reduction, or customer value.

    2. Reimagine the domain, not just a task
    Instead of asking, “Where can we use AI?”, ask, “How should this entire domain work differently with AI?”

    3. Redesign the workflow before implementing AI
    Remove unnecessary steps, simplify processes, then embed AI where it creates the most impact.

    4. Build reusable foundations
    Data products, platforms, governance, model-risk controls, and AI capabilities should be designed once and reused across multiple domains.

    5. Keep humans in the loop
    AI should augment human decisions, especially where judgment, accountability, and complex decisions matter.

    The key mindset shift is simple:

    Don’t build AI activity. Build an AI-powered business.

    Start with Business Value → Domain → Workflow → AI → Scale.

    That is how AI moves from experimentation to sustainable enterprise impact.

  • From AI Adoption to Business Value

    With all the AI hype today, I think there is one important question we often overlook:

    It’s not “Where can we use AI?”
    It’s “How do we turn data and AI into measurable business value?”

    For me, the value creation journey can be simplified into one flow:

    Data → AI → Decision → Action → Business Value

    Data helps us understand customers, transactions, behaviour, risk, and operations.

    AI turns that data into predictions, recommendations, and insights.

    But AI itself does not create business value.

    Value starts when AI enables better decisions.

    Those decisions then need to translate into actions, such as offering the next-best product, preventing fraud, accelerating claims, improving customer retention, or optimising operations.

    And ultimately, those actions need to connect to measurable business outcomes:

    Revenue ↑
    Cost ↓
    Risk ↓
    Customer Experience ↑
    Productivity ↑

    This is particularly relevant for banking and life insurance, where data is abundant and decisions happen at scale.

    So perhaps the question we should be asking is not:

    “How many AI use cases do we have?”

    But:

    “How much measurable business value are we creating through better decisions and better actions?”

    For me, this represents an important shift:

    From AI as a technology → to AI as a decision engine for the business.

  • The Future of Work: From Using AI to Working with AI Agents

    The future of work is not simply about AI answering our questions. It is about Agentic AI executing work alongside us.

    Routine, repetitive, and manual activities will increasingly be handled by AI agents, operating faster and, in many cases, 24/7. This allows humans to focus on what matters more: oversight, judgment, context, insight, and better decision-making.

    The future workforce will be hybrid: humans + AI agents.

    This shift is not only about productivity or headcount reduction. It is about augmenting human capability, accelerating turnaround time, strengthening risk management, and creating capacity for higher-value work.

    One key takeaway for me: we may need to treat AI agents like new hires. They need to be trained, guided, evaluated, and continuously upgraded, while we continuously reskill ourselves to manage and collaborate with them.

    The question is no longer “Will AI change the way we work?”

    It is: “How quickly can we redesign the way we work with AI?”

  • AI Implementation: Beyond Efficiency, Toward Customer Experience

    In many organizations, the first motivation to adopt Artificial Intelligence is clear:
    – Increase efficiency
    – Reduce costs
    – Boost productivity

    While these goals are valid, they often overlook the true source of long-term business value, the customer. AI should not only make our processes faster or cheaper. It should make our customers feel more understood, supported, and valued.

    When we design AI with the customer in mind, it stops being just a tool for automation, it becomes a way to deepen connection.
    It helps us:
    – Create interactions that feel personal, not programmed.
    – Anticipate what customers need, sometimes before they even ask.
    – Earn trust by being transparent, responsive, and consistently human in how we serve.

    Efficiency may drive short-term ROI. But customer satisfaction and trust build sustainable growth. Organizations that combine operational excellence with a genuine focus on people will be the ones that truly lead in this new era of intelligent experiences.

    Let’s shift the AI conversation, from “How can we do more with less? to “How can we create more value by serving customers better?”