Forbes Japan features Georg Kell on luxury, transparency and the role of AI.

Forbes Japan has featured Arabesque’s Chairman, Georg Kell, on the future of transparency in luxury — a conversation that matters well beyond the sector, and directly to the investment community.

In the piece, Georg delivers a keynote at ‘Accelerating Transformation Through Dialogue’, an event organised in Tokyo by jewellery brand Tom Wood together with the Norwegian Embassy in Japan. The premise: as scrutiny over sourcing, supply chains, and environmental impact intensifies, the companies that lead are the ones willing to make their data public, comparable, and investable.

Why luxury is the frontier for transparency

Luxury occupies an unusual place in the transparency debate. Consumers expect craftsmanship, provenance, and materials of exceptional quality — and they increasingly expect the evidence to back those promises up. Regulators are following, and capital is following regulators. The sector that built its value on storytelling is now being asked to show its working.

Georg’s argument, as featured by Forbes Japan, is that this is not a constraint but an opportunity. Firms that can articulate where their materials come from, how they are processed, and what the environmental and social footprint looks like create a clearer picture for customers, regulators, and investors alike. Brands like Tom Wood — which has rebuilt its jewellery around 100% traceable and recycled metals — are showing what that standard looks like in practice.

As scrutiny on sourcing and supply chains increases, technology is playing a growing role in turning complex sustainability data into clearer, more comparable insights.
Georg Kell, Chairman, Arabesque

From fragmented data to decision-ready signals

Underneath the luxury conversation is a broader question for every allocator: how do you actually use the flood of sustainability data now available on public companies?

Disclosures arrive from disclosures frameworks, satellite providers, supply-chain auditors, product-level lifecycle databases, and regulatory filings — in different formats, on different schedules, with different degrees of reliability. No human research team can read, reconcile, and weight it all in the time a portfolio decision actually has.

This is the problem AI was built for. Models can normalise disparate inputs, flag inconsistencies, detect structural changes in a company’s profile, and translate that into signals that sit alongside the financial ones portfolio managers already use. For investors, the shift is from narrative to numbers: a company’s sustainability story is no longer a side panel in a pitchbook, it is an input to the risk and return models.

What this means for portfolio managers

Two things follow for institutional investors:

• Data coverage needs to go wide and deep. Treating sustainability as one or two headline scores misses the point. The useful signal sits at the intersection of emissions, water, biodiversity, labour, governance, and supply-chain data — and it has to be refreshed as the companies do.
• Interpretation needs to be machine-scale. Integrating those inputs into portfolios manually isn’t viable. AI does the heavy lifting: ingesting, scoring, stress-testing, and translating into position-level guidance within a mandate.
• Evidence needs to travel with the decision. Regulators, clients, and boards all want to see the reasoning. The same AI system that produces the signal should also produce the audit trail behind it.