Podcasts
AI BizSeptember 17, 2025 · Host: Avik Chakraborty

AI business opportunities and responsible scaling - from productivity wins to new value chains

Amir Elion joins Avik Chakraborty on AI Biz to map where the real business value is in AI today - covering three buckets of opportunity from operational productivity to new value chains, how Amazon's Working Backwards methodology keeps AI projects customer-anchored, responsible AI principles including fairness, transparency, and privacy, sustainable AI beyond just environmental impact, and why the biggest risk is waiting on the sidelines.

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Originally published on AI Biz

What you'll learn in this episode

In this conversation, Amir Elion, CEO of Think Big Leaders and former AWS Innovation Programs lead in the Nordics, joins Avik Chakraborty on AI Biz to cut through the AI hype and map where the real business value is today. Based in Stockholm, Sweden, Amir shares his three-bucket AI value framework, explains how Working Backwards keeps AI projects customer-anchored, and discusses responsible and sustainable AI scaling for executives, builders, and public sector leaders. These are topics Amir regularly speaks about at conferences and corporate events.

Three buckets of AI business opportunity

Amir opens with a practical framework for leaders feeling overwhelmed by AI. Bucket one: operational productivity - getting things done faster and at larger scale with intelligence on tap. Bucket two: enhancing products and engagement - upgrading experiences and offerings for customers, stakeholders, and the public through AI. Bucket three: disruption - understanding how AI will impact value networks, business models, and operational models, and building new value chains before someone else does. The key advice: build a portfolio across all three buckets, not just the obvious productivity plays. This framework is a core part of Amir's keynote presentations and executive workshops.

Working Backwards keeps AI customer-anchored

Amir connects Amazon's Working Backwards methodology directly to AI adoption. Start with five questions before choosing any technology: who is the customer, what is their problem, and what is the most important benefit? Teams often force the customer answer onto the solution they already have in mind - and Amir pushes back until they ground their answers in validated evidence rather than assumptions. The key insight: if the customer needs speed and low cost without complexity, AI might actually get in the way. AI is not a magic bullet - it has to serve the customer need.

Responsible AI scaling in practice

The conversation tackles what responsible AI looks like beyond principles on paper. Fairness and inclusiveness by involving diverse voices in development. Transparency about AI use in recommendations, analysis, and decision-making. Security and privacy of user data, especially when training models. Amir connects these principles to his work on sustainable AI through Global Green Action Day - sustainability in the broader sense including both environmental impact and social elements like bias and fairness. The advice: educate yourself and your teams about risks, apply mitigations, but do not let those risks hold you back from experimenting.

Regulation as guidance, not a barrier

Amir shares a nuanced perspective on AI regulation. Regulation will not stop AI innovation but may impact the speed in certain regions. The EU AI Act, for example, provides useful risk-level guidance - helping organizations identify lower-risk opportunities where they can move ahead, while adding protections for high-stakes decisions around health and lives. The balance is tricky, but responsible innovation is not about slowing down - it is about building trust as you move forward.

Book Amir as a speaker

Amir regularly delivers keynotes and workshops on the topics covered in this episode - the three buckets of AI business value, Working Backwards for customer-centric AI adoption, responsible and sustainable AI scaling, and practical frameworks for navigating AI disruption. He speaks at conferences, corporate leadership offsites, and industry events across Sweden, the Nordics, and Europe. Learn more about Amir's speaking topics and availability.

Key Topics Discussed

Three buckets of AI business value

A practical framework for executives - bucket one is operational productivity at scale, getting things done faster with intelligence on tap. Bucket two is enhancing products and customer engagement with AI-embedded experiences. Bucket three is understanding the disruption potential and building new value chains and business models before someone else does.

Working Backwards for AI projects

How Amazon's Working Backwards methodology prevents organizations from chasing AI hype - starting with five questions about who the customer is, what problem matters most, and what the most important benefit is before choosing any technology. If the customer needs speed and low cost, AI might actually get in the way. AI is not a magic bullet.

Portfolio mindset for AI investment

Why organizations should build a portfolio across all three AI value buckets rather than stopping at productivity. Balance near-term productivity plays with measured bets on AI-enhanced features and longer-horizon business model shifts. The mix depends on your organization type, risk tolerance, and culture.

Responsible AI scaling in practice

Practical principles for scaling AI responsibly - fairness and inclusiveness by involving diverse voices in development, transparency about AI use in recommendations and decision-making, security and privacy of user data especially when training models, and not letting risks dissuade you from experimenting but educating teams about mitigations.

Sustainable AI beyond environment

Sustainability in the broader sense applied to AI - not just energy and water consumption of AI systems, but also the social elements including fairness and inclusiveness. Connected to Amir's work on Global Green Action Day, bringing together innovation, circular economy, sustainability, regulation, and AI through multidisciplinary collaboration.

Regulation as guidance, not a barrier

Why AI regulation will not stop innovation but may impact speed in certain regions. The EU AI Act provides useful risk-level guidance, helping organizations identify lower-risk opportunities and protecting people's interests in high-stakes decisions around health and lives. Regulation builds trust rather than just slowing things down.

Holistic thinking for complex problems

How Amir's background as a shiatsu therapist connects to AI leadership - looking at problems from a holistic, systems perspective rather than treating symptoms. Complex problems like sustainability and AI require multidisciplinary approaches where technologists, sustainability experts, innovation managers, and legal teams learn to speak each other's language.

Act now or risk falling behind

The closing advice for business leaders - do not ignore this, do not sit on the fence. Pick a real project with impact, pick a tool, expect challenges and surprises, and start experimenting. The biggest risk is not doing anything and waiting for things to mature, because then it may be too late.

Frequently Asked Questions

What are the three buckets of AI business value?
Bucket one is operational productivity - getting things done faster at scale with AI. Bucket two is enhancing products and customer engagement by embedding AI into experiences and services. Bucket three is understanding the disruption potential and building new value chains and business models. Organizations should build a portfolio across all three rather than stopping at productivity.
How does Working Backwards prevent AI hype chasing?
Amazon's Working Backwards methodology starts with five questions before choosing any technology - who is the customer, what is the problem, and what is the most important benefit. If the customer needs speed and low cost without complexity, AI might actually get in the way. This keeps teams anchored on solving real problems rather than forcing AI where it does not fit.
What does responsible AI look like in practice?
Responsible AI involves fairness and inclusiveness by involving diverse voices in development, transparency about AI use in recommendations and decisions, and maintaining security and privacy of user data. Organizations should educate teams about risks and apply mitigations, but not let those risks prevent them from experimenting with AI.
How does sustainability apply to AI beyond energy use?
Sustainable AI goes beyond environmental concerns like energy and water consumption to include social elements - fairness, inclusiveness, privacy, and avoiding bias amplification. Addressing these requires multidisciplinary collaboration across technology, sustainability, innovation, regulation, and circular economy perspectives.
Will AI regulation help or hinder innovation?
Regulation will not stop AI innovation but may impact speed in certain regions. Frameworks like the EU AI Act provide useful guidance on risk levels, helping organizations identify lower-risk opportunities while protecting people's interests in high-stakes decisions. Responsible innovation builds trust rather than just slowing things down.
What is a portfolio mindset for AI investment?
Rather than putting all resources into one type of AI initiative, organizations should balance near-term productivity plays with measured bets on AI-enhanced products and longer-horizon business model shifts. The right mix depends on organization type, risk tolerance, and culture - public sector organizations have different constraints than private corporations.
What topics does Amir Elion speak about?
Amir Elion delivers keynotes and workshops on the three buckets of AI business value, Amazon's Working Backwards methodology for customer-centric innovation, responsible and sustainable AI adoption, and practical frameworks for executives navigating AI disruption. His talks combine insider experience from AWS with actionable advice for leaders at every level.
Can I book Amir Elion as a speaker for my event?
Yes. Amir speaks at corporate events, conferences, and leadership offsites across Europe and internationally. His speaking topics include AI strategy for business growth, responsible AI scaling, Working Backwards methodology, and navigating AI disruption. Visit amirelion.com to learn more and book a session.

About Amir Elion

Amir Elion is an AI strategist, innovation consultant, and keynote speaker based in Stockholm, Sweden. As CEO of Think Big Leaders, he helps Nordic and European enterprises develop practical AI strategies, run innovation workshops, and build AI-powered products. Previously, Amir led the AWS Innovation Programs in the Nordics, bringing Amazon's Working Backwards methodology to companies like Volvo and KONE. He combines 25+ years of innovation experience with hands-on generative AI expertise.