.... Be Smart & Leverage Both

I’ve been reading the book Scary Smart by Mo Gawdat. One chapter that stood out, “Summary of the Scary Part,” outlines three unavoidable truths:

1. AI will happen; there is no stopping it.
2. The machines will become smarter than humans, sooner rather than later.
3. Mistakes will happen. Bad things will happen.

Points 1 and 2? Almost here!. Point 3 - Sounds apocalyptic?

No ... I'm not starting a book review, but it got me thinking about how AI is quietly, behind the scenes, but powerfully, reshaping our supply chains.

A couple of years ago, sustainability tracking felt like chasing smoke. Endless spreadsheets. Missing data. Stressful audits. Today, AI offers supply chain professionals tools that feel like X-ray vision, spotting emissions, predicting waste, and reframing compliance as an opportunity.

But the road isn’t simple, and adoption is still low. Modern supply chain professionals now have access to a wide range of cutting-edge technologies that can operate with minimal human oversight, including predictive analytics, digital twins, autonomous robots, and generative AI.

McKinsey estimates that early adopters of Artificial Intelligence in supply chain have leveraged their increased analytical capabilities to improve logistics costs by 15%, inventory levels by 35%, and service levels by 65%.

In this edition, we peel back the layers to reveal why AI is essential for sustainable supply chains, the real-world benefits, the inevitable challenges, and how smart adoption can transform regulatory pressure into a competitive advantage.

Let’s dive in.

Beyond Efficiency: How AI Unlocks (and Complicates) Sustainability

AI is transforming how supply chains operate. It’s not just automating tasks but reshaping decision-making across procurement, planning, and logistics. It improves demand forecasting, streamlines production planning, and enhances shipment visibility, all while reducing costs and environmental impact.

In demand forecasting and planning, AI analyses historical sales, market trends, and external factors like weather or geopolitical risks to predict future demand with greater accuracy. This enables better inventory decisions, reduces overproduction, and minimises excess stock.

Take, for example, IKEA South Korea invested €11M in an AI-powered fulfilment system that boosted picking productivity eightfold, packing 300 boxes per hour and fulfilling 2,000 orders daily while improving labour, material, and space efficiency.

What this means, companies can now align production more closely with actual need, which cuts waste, lowers emissions from unnecessary transport and storage, and frees up working capital. These gains directly support sustainability goals while improving service levels, reducing stockouts, and increasing responsiveness to market shifts.

On the flip side, AI is only as effective as the ecosystem it operates in. Poor data quality, lack of cross-functional alignment, and weak talent engagement are common barriers. Flawed or siloed data leads to misleading outputs. Without integration across functions like planning, procurement, and sustainability, AI insights don’t translate into action. And without skilled people to interpret and apply the insights, the value gets lost.

These challenges must be addressed together, because when they are, AI becomes a real engine for sustainable performance.

Why “Sustainability Needs AI” More Than Ever

The stakes are higher than a few bad audit reports. Supply chains are under growing pressure from investors, regulators, and consumers demanding transparency and action on climate impact. With emissions regulations tightening worldwide, including mandatory Scope 3 disclosures, the standard manual approaches simply cannot keep pace.

AI enables us to speed, scale, and achieve accuracy in sustainability data management, turning a sprawling, complex problem into manageable, actionable insights.

Moreover, frequent disruptions (research says averaging every 3.7 years) challenge resilience and cost stability. AI not only supports compliance but also helps anticipate risks and optimise resources for sustainability and business continuity.

The Green Elephant in the Room: Challenges of AI for Sustainability

If AI were a superhero, its kryptonite would be energy consumption and data quality. Training large AI models and running complex analytics requires significant energy, sometimes offsetting gains if deployed without care. Plus, dirty or siloed data can mislead decisions and erode trust.

This means AI for sustainability must be smart and selective, prioritising specialised models focused on supply chain forecasting and emissions tracking, using greener infrastructure, and ensuring clean, integrated data flows. Responsible AI governance isn’t optional; it’s mission-critical to avoid greenwashing or unintended environmental harm.

Compliance Crunch: Navigating Increased Regulatory Demands

Regulatory landscapes are shifting fast. Governments and regional bodies are now requiring comprehensive, audit-ready Scope 3 emissions reporting. This means companies must trace carbon footprints across dozens or even hundreds of suppliers, often spanning continents.

Failing to comply leads to riskier fines, reputational damage, and exclusion from sustainability-focused markets. Whilst AI-driven automation and analytics streamline compliance workflows, detect non-conformance early, and produce transparent, evidence-based reports. What was once a looming headache is now a potential competitive differentiator. [Voila].

Five Smart Steps to Implement AI-Enhanced Sustainability

1. Start Small, Think Big: Pilot AI tools on priority pain points like inventory waste or supplier emissions before scaling broadly.
2. Clean House: Invest in data governance to ensure accurate, complete, and integrated supplier and operational data.
3. Make It Transparent: Use AI platforms that provide explainable, auditable outputs to satisfy both regulators and stakeholders.
4. Collaborate Across Functions: Break silos, finance, procurement, sustainability, and IT must drive AI adoption together.
5. Measure What Matters: Define clear KPIs tied to carbon reduction, waste minimisation, and risk mitigation, and track progress relentlessly.

The Payoff: How AI Powers Sustainable Value Creation

When done right, AI doesn’t just make sustainability easier; it makes businesses stronger. AI-driven companies report up to 40% reductions in waste, enhanced supply chain resilience, and faster adaptation to regulatory changes.

By freeing teams from manual report wrangling, AI enables more strategic focus. It fosters innovation, strengthens supplier collaboration, and supports better decision-making.

Sustainability shifts from a compliance cost to a growth driver and brand enhancer.

B2G Academy Section:

"Stop making sustainability someone else’s job. Put it where it belongs - at the core of business decisions."

I hosted an unfiltered webinar discussion with Magali Anderson (ex-CSO at Holcim) and Divya Demato (CEO & co-founder at GoodOps) at the ISCEA - International Supply Chain Education Alliance 5th Annual Supply Chain Pledge Day 2025. We covered these key points in sustainable supply chain:

• Big promises, no plans
• Reporting ≠ impact
• Sustainability is strategy, not charity

Watch the full conversation, click the link: Pledge Day 2025.

Ready to Take Up a New Skill - CSSCP

AI is coming fast, but without sustainability, it’s just noise. Learn where ESG fits before the algorithms take over.

This sets up the need for B2G Academy's Certified Sustainable Supply Chain Professional programme [CSSCP] accredited by ISCEA, in a direct, relevant way - connecting sustainability knowledge to the current AI shift.

📍 For more information, check the link to register.

Final Thought: You Choose...

Let's get real. AI is not a fad. It is shaping the future of how supply chains operate. Ignore it, and sustainability becomes a juggling act you are bound to drop. Embrace it, and you gain visibility, resilience, and a competitive edge that is more than green. It is an intelligent, responsible business.

Critically, technology alone is not the solution - ethics, governance, and intent matter. How we use AI, what data we train it on, who we involve, and what values we embed will define whether it solves real problems or creates new ones.

So, is AI in sustainable supply chains about compliance or competitive advantage? It is both. Compliance gets you in the game, but competitive edge and lasting impact come from using AI wisely and ethically for the future of our planet.

I opened with a thought from Scary Smart. Now, I’ll close with the author’s own words.

“We are the only ones who can create our future.”

So the question is, what kind of future are you building? I hope for a sustainable one!