What Is Value in the 21st Century and How Can AI Be Its Catalyst?

Scaling AI Stéphanie Griffiths, Valentine Reltien

As Jean-Paul Mazoyer, Deputy General Manager at the international French bank Credit Agricole told me recently, We need to detangle value from short-term profit.”  Quite a statement from a banker, right?

Short-term profit as an objective for your stakeholders is not enough anymore. The “greed is good” mantra displayed by movies like “Wall Street” now feels old-fashioned and horrified my children when I showed them what the 1980s looked like. Accelerating factors such as legislation on decarbonization, growing environmental citizenship, and the fear of an impending food and energy crisis sow the seeds for creative disruption. How can we re-engineer capitalism in the 21st century? 


Source: Unsplash, 'Kaleidoscope 2020 coastal microplastic'

A new breed of venture capital firms is developing, such as 2050, focused on long-term returns and alignment with the best interest of people, society, and the planet. Entrepreneurs are funding an education revolution (i.e., Ecole 42, HECTAR, Tumo, Albert School) to explore digitalization and impact. New business classifications such as B Corp have triggered a change in the concept of corporate value. Many startups, such as Provenance (tracks production origin) and Impossible Foods (meat alternative), reinvent daily impact and profit. 

Despite these factors, most businesses only associate AI with short-term operational gains (speed, agility, stack efficiency). While these denote improvements, we shouldn’t delay impactful transformations any further. The commonly heard, “We'll get creative with data later. First, we need to optimize,” is a red flag for me. One way of ensuring a fertile future during this period of economic flux is to redefine the meaning of value itself and capture the role AI can play. As Mike Barry, previous Director of Sustainable Business (Plan A) for Marks & Spencer and founder of MikeBarryEco, recently told me, “ Data is at the heart of solving the problem.”

We might jeopardize some more substantial value by focusing only on incremental value (fast, small gains to reassure CFOs and justify one’s role) and should assess different types of values — linked with our capacity to adapt (adaptive value), brand mission (purpose value), and collective welfare (regenerative value) — all of which we’ll touch on in this article.

Building on change management experience, various roles in AI and analytics, and talks with global C-suite executives from blue chip companies, Stephanie suggests practical ways to assess what value could mean in businesses and opens the door to exploratory discussions, very much needed in a period where recession is looming. With her experience working in sustainability and with Ikig.ai, Valentine adds insight into the harnessing of data for good.

So, what should businesses consider as value to build resilience? By interrogating the concept of profit (“Beyond Profit”) and the role of AI (“More Extraordinary People”), we suggest ways to build resilience and assess value in a modern context (“Regenerative / Collective Value”).

1. Beyond Profit

Value’s etymology means “to be strong” and what better way of demonstrating strength than showcasing high profits? In old French, value alludes to both “reputation” and “moral worth.” In order to reconcile both definitions, let’s assess how data and AI can create an all-encompassing, thorough, and grounded notion of value — from fast, small value gains to steadier, more substantial returns.

Adaptive Value

You are progressing from descriptive analytics to predictive analytics and have implemented a few algorithms. Excellent, well done. 

But what is the point of working with probabilities? If your answer is “business as usual,” try again. Companies don’t need anticipation per se; they need decisions. Effective decision-making — connected, contextual and continuous — results in adaptive value, including greater transparency, risk management, an upskilled workforce, and scalability.

You must imagine various futures and tailor processes and mindsets to adapt faster to changing contexts. According to Gartner, “Bringing together forecasts (a form of predictive analytics) with optimization (a form of prescriptive analytics) lets an organization explore how changes to different variables are likely to affect the outcomes or alter the relative trade-offs.” This combined approach crystallizes adaptive value creation. 

Transformational or Purpose Value

Efficiency for efficiency’s sake is not the answer. The goal is to reach transformational value to help businesses deliver their mission. So perhaps we should be talking about purpose value rather than transformational value.

Bringing together everyone on the same platform and the rise of citizen data science, enabled us to embed data science across the organization to create more value towards fulfilling our mission.” 

- Bruno Sainte-Rose, Lead Computational Modeler at The Ocean Cleanup

Purpose value helps eliminate that tension between small and significant changes, between a business that serves the progress of society and a business that serves the wealth of some. It keeps us on our toes because metrics will evolve. As Jean-Paul Mazoyer (Credit Agricole) described, “Our value is about regions’ development, its services, businesses, and inhabitants.” It keeps us motivated because it unifies strength and reputation (as long as your mission is not to suffocate people with nicotine, melt the ice cap, or inundate kids with sugar-fat-full-sweets). Gartner predicts that by 2026, organizations that develop trustworthy, purpose-driven AI will see over 75% of AI innovations succeed, compared to 40% among those that don’t.

We need to harness both values in parallel with operational efficiencies. But how can your teams do it all? We must empower staff with time to explore and collaborate to move beyond short-term profit and towards building long-term value. My eyes roll when I hear data directors saying, “If I manage to gain trust as I deliver on business requests like churn models, then I’ll carve time to explore data with my team on Friday afternoon.” To truly move “Beyond Profit,” you need to tap into collective intelligence in a repeatable, scalable way. 

2. More Extraordinary People

The role of AI (in the simplest terms) is to make us less ordinary. It isn’t about replacing humans with robots but, instead, it is about augmenting our collective intelligence. This would free us from repetitive, soulless tasks, giving us the time and space to build an easily shareable, collective memory to build on each other’s strengths.

More Creative: Collective Intelligence

As groups of people are more intelligent than individuals, we are about to see widespread implementation of collective intelligence thanks to AI and hyperconnectivity. Connecting people on a massive scale towards meaningful goals with the added power of anticipative analytics (i.e., ML, NLP) will catalyze our ability to solve issues. And will help dissipate stereotypes of “Man versus Machine.” 


As Mattia Cinquilli (Data Director, SkyItalia) explained, we need to “Look at value beyond the incremental revenue driven by data. Staff Happiness — do they have meaningful work?” It is much more than a retention topic; well-being and self-realization are essential for collaboration and creativity. “Data allows us to model and simulate real-life experiences and possible futures,” explains Fanny Cabanne, Insights Specialist at Dassault Systèmes’ Design Studio.


What is the value for our society? Does the data community need to define a standard to avoid creepy customer experiences? What about the ethical approach to data?" - Mattia Cinquilli (Sky Italia) 

When exploring and analyzing data, we need to confront different points of view to move from correlation to causation. “Data & AI shouldn’t only be left to tech people,” said Fanny Cabanne (Dassault Systemes). According to Fanny, the objective is to “collectively  foster a world in which we still want to live in our old age.” Therefore, “The future of AI is to put humans first” when developing any system. Ethics and self-development are two sides of the same coin. We must continuously learn and improve our approach to data and AI to avoid bias.

As articulated by Jean-Paul Mazoyer (Credit Agricole), “We have an obligation of curiosity,” namely, we are in the era of lifelong learning. Just as we continually update software, it is our responsibility to keep learning continuously, composing with our changing environment and socio-eco-environmental needs, and staying agile with the way and purpose with which we conduct business. Data, now considered a product, is accelerating the transition in the analytics field. 

In the words of ex-Chief Scientist Adviser to the U.K. Sir David King in Barclay’s 2022 annual report: “What we do in the next five years will determine humanity's fate.” Might it, therefore, be worth harnessing our strengths to tackle our most significant challenges? Ought we apply our most promising technologies to accelerate the move beyond profit to build regenerative value?

3. Regenerative Value

A BCG survey conducted in May 2022 found that 87% of leaders responsible for climate or AI topics believe that “AI is a useful tool in the fight against climate change.” 

Reduce Impact as a First Step

In every digital activity, we lead, we should ask ourselves how to reduce: 

  • Compute resource requirements and needs
  • Storage infrastructure and data collection/processing costs
  • Energy costs — in the data field, those associated with training and operationalizing AI systems. 

As professionals working in data, we are fully aware that the exponential growth of data generates its own carbon footprint. Big data is dead. Long live essential data. (Please refer to our Frugal AI series by Simone Larsson to go further). 

Regenerative Value as a Collective Endeavor

As Mike Barry explained, “We are at a tipping point. Doing less bad is not good enough. We need to transform.” Regeneration involves avoiding environmental damage and repairing the harm caused by past and present business activities over recent centuries. Some will decry greenwashing and wishful thinking. While the early stage of regenerative business may justify some such allegations, the real-world grounds that make these changes necessary will not recede anytime soon (as repeatedly warned by the IPCC). Fortunately, data and AI can be instrumental in accelerating this learning curve and finding new solutions to be nature positive. (and still make money whisper, my CFO friends).

We are seeing a growing number of regenerative business models: ML, computer vision, and automation are revolutionizing waste sorting on a global level. By turning waste into resources, it is now possible to identify materials and provide real-time composition data for traceability and transparency. 

Companies are moving from ownership to sharing platforms (automotive, clothes, houses) using subscription-based models. While this trend is far from complete, it may challenge the status quo by making us rethink what value we wish to maximize, from reputation to moral worth. For instance, Decathlon launched Weplaycircular in Belgium, a kind of Spotify for sports equipment, an innovation made possible thanks to data and AI. Further, Patagonia launched WornWear to facilitate the trading of used items. To build resilience, we need to think about adaptive and purpose values. This multi-faceted value unrestrained by short-term profit will create myriad more net positive returns, as described by Paul Polman, the former Unilever CEO.

Towards "Patient Capital"

AI will facilitate this value by enhancing collective intelligence via the integration of extra-financial data and anticipations. This paves the way to an economy where “we use the tools of capitalism without being controlled by them,” Jacqueline Novogratz (Founder and CEO of Acumen). By systemizing AI, we aim to enable businesses to combine long-term vision (transformational value) and short-term execution. 

Deloitte’s latest “Global Turning Point” report estimated climate inaction would cost the world about $178 trillion over the next 50 years, compared to $43 trillion in potential gains were we collectively to accelerate the economy’s decarbonization. In light of this, could the reason why CFOs still hesitate to invest boldly in their business’s transition to Net Zero boil down to a data/model gap? Could helping businesses integrate predicted costs and gains help them reconcile short-term expectations with medium-term viability? What is more, the Future of Nature and Business report estimates that a nature-positive economy can unlock $10 trillion of business opportunities. Of course, one would need a sandbox to experiment with such novel and creative AI use cases.  

This is precisely why Dataiku set up its Ikig.AI program. Since 2018, we provide free licenses to nonprofits to the likes of SensingClues, JustDiggit, and Project Canopy, to name a few of our climate action-oriented AI partners. By giving them free access to best-in-class tools and data scientist support, Dataiku endeavors to unleash the full potential of AI by developing creative solutions to pressing challenges, which would not otherwise be prioritized by commercially-driven metrics. 

Whilst these may not immediately serve the CFO’s quarterly report, Ikig.AI-enabled use cases unlock precious purpose value. By galvanizing employees around meaningful projects, Ikig.AI inspires transposable approaches to problem-solve with data, and, most of all, helps build compelling brand reputation as an experimenter for the next chapter of capitalism.

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