Who doesn’t like chocolate? 

Tony’s Chocolonely is a superb example of a cast-iron purpose-driven enterprise. And while the chocolate bars they make and sell, now in serious numbers in supermarkets and shops across Europe and the world, taste great, they aren’t actually the reason for the brand and business to exist.

The chocolate bars – which are sold to keep the organisation running – have essentially become a marketing tool. That’s because the real focus of Tony’s Chocolonely is to end the exploitation of cocoa growers around the world. Their purpose isn’t to sell more chocolate – it’s to highlight the pressures and unethical practices that the whole chocolate supply chain puts on some of the poorest communities on earth.

Purpose-driven enterprises aren’t just another branding fad or smoke and mirrors to hide behind while focusing on profit (think greenwashing): entire organisations are putting their ethical objective, environmental or benevolent activity first and foremost, and even building a profitable organisation entirely around a ‘force for good’ mantra.

Making chocolate bars – or for other businesses, growing coffee or selling T-shirts – almost becomes secondary, as the brand narrative focuses on reducing waste, supporting communities, becoming 100% sustainable or going carbon negative, not just carbon neutral.

So how could AI, used with intent and expertise, become a force multiplier for a business model that focuses on the overall mission, rather than just the balance sheet? Let’s find out.

Starting with friction, not hype

For organisations like Tony’s Chocolonely, the opportunity that arises from AI is deeper than surface-level: there’s a very real prospect of reinforcing an already strong brand purpose and mission through generative AI-complemented marketing.

Rather than attempting to stay on trend, AI could dig into the actual day-to-day and start looking to remove ‘friction’. But what do we mean by friction?

Almost every organisation has three zones where AI can start to make a huge difference rapidly if applied correctly. Take the productivity layer as an example. AI can get us through our inboxes quicker, do our admin more efficiently, organise our calendars, supplier compliance, accounting, or anything else in a more effective timeframe.

Then there’s the supply chain. AI can get raw materials, products or services to our customers or clients faster, and at a lower cost.

On top of this is the communication layer. By collapsing marketing setup into days rather than months, testing campaigns using human-like feedback and responses, or using generative AI to support ad creative, costs come tumbling down.

But according to AI future-thinker and Satalia CEO Daniel Hulme, all of these positives can quickly become the victim of hype.

Daniel believes that this isn’t down to speed-to-implement or speed-to-market, but sequencing. It’s better to list frictions like a backlog, e.g. repetitive activity, inefficient scheduling or manual input, then tackle each one pragmatically. It’s as simple as remembering not to spread your organisation too thinly.

For a purpose-driven organisation, this isn’t about getting excited because AI can come up with a better brand narrative or reduce headcount; it’s about making teams more effective rather than reducing their size.

Measuring better

Let’s think about just how hard it is for an organisation to measure its purpose. How do you prove you’re cutting emissions, improving working conditions or reducing waste, especially if you’re helping other organisations to do all of those, with limited access or inputs? And not just once a year for a glossy report, but every single day.

Consider for a moment an organisation that’s focused on cutting food waste. Instead of trying to time the seasonality of crops using spreadsheets, or influencing consumers to change their habits with marketing campaigns, AI can dig infinitely deeper, faster – even with messy, incomplete or fragmented data.

A sophisticated AI forecasting model could crunch weather data, local events, historical sales – even traffic patterns, to predict demand far more accurately and put the right amount of food for sale in the right place, at the right time.

Case in point: UK-based charity Too Good To Go, which lets users know about end-of-day food reductions at local retailers. The consumer grabs a bargain; the retailer avoids dumping close-to-sell-by-date goods in a waste trolley and instead makes revenue they otherwise might not.

One of the smartest parts of Too Good To Go’s software is an AI pricing system that ensures better sell-through and strikes the right balance: cheap enough to excite the consumer but still delivering close-to-margin for the retailer.

With proven, impactful and well-delivered data collected and curated by AI, stakeholders in the entire food supply chain get a much better idea of the ‘bigger picture’. This could help them to focus on a better long-term strategy, rather than worrying about yield, profit, or targets right now – typical symptoms of organisational decision making based on an incomplete view of the full ecosystem.

And in more practical terms, AI planning and logistics could get food from farm to fork quicker and more efficiently. Imagine if those peaches you just bought weren’t already going soft thanks to drawn-out, manual procurement and days of freshness lost sitting waiting for a human to fill out a customs form?

Add in hypotheticals like improving fuel efficiency, shortening procurement routes or even just knowing the best times to dim the office lighting or turn down the heat, and it becomes much easier to say, honestly, that everything is being done to maximise the effectiveness of a purpose or mission statement.

Tackling contradictions

Of course, even with all of our efforts, there will be bumps in the road. Constantly promoting a cause only to find that somewhere within the supply chain, or the organisation itself, there’s a system, practice or process that goes completely against the mission can cause consternation.

Whether it’s something as simple as the necessary evil of using a fossil-fuel vehicle to deliver T-shirts made from recycled plastic to a store, or something as complex as relying on established, profit-first financial and accountancy approaches while trying to promote ethical or community banking, there will always be some sort of fly in the ointment. That’s just part of the journey. That is, until AI digs into the problem.

From looking at ways to go carbon positive in the quickest and cheapest timeframe possible to doing a deep-dive into suppliers, third parties or internal teams to identify practices that go against core values or purpose, AI has the capability to identify pain points and suggest the right course of action.

But beyond just reacting to existing contradictions, AI can also be used to identify, measure and potentially avoid risks before they become a problem.

Rather than relying on a brochure and sales pitch from an allegedly like-minded supplier, AI could analyse the hard data, carry out a social listening exercise or scrape detailed information to truly confirm whether the third party is fully aligned with the purpose before anyone has a chance to sign a contract.

Amplifying advocacy

Whether it’s making sure our plastics end up in the correct recycling bin or checking our favourite coffee shop is part of the Rainforest Alliance, we’re very used to attempting to make small changes in our lives for what we believe is the greater good. And ‘attempting’ is where things get problematic. 

Although many of us will buy with ethics as a key decision driver, the same number of us will buy the coffee regardless of the ethics behind how the beans are sourced, or the rights of the workers behind the counter, if we really want or need a coffee.

And when the chips are down, we may still choose the cheaper but potentially less ethical option if we’re feeling a financial pinch, or forgo our commitment to sticking to public transport and hop in a taxi if it starts raining. Unsurprisingly, much of the research around ethical purchasing focuses on privileged audiences, brands and economies, meaning the data is skewed to the top handful of consumers worldwide.

Now, imagine if purpose-led organisations could meet those ‘human weaknesses’ head on, making us think twice about choosing convenience or cost over sustainability.

Take Audience AI. Instead of opting for tried-and-tested messaging, or jumping on a reactionary trend, AI could be used to build simulated audiences to act as a test bed. Then, different messaging, language or even visual or audio cues could be applied to see what gets the best reaction.

And then, AI could use smart targeting to present the correct piece of content or advertising to change our mind just as we’re about to choose that leather handbag rather than the sustainable hemp one that does exactly the same job, or persuade us to swap a plane ticket for a train ticket where possible.

Freeing up purpose-driven humans

One of the most powerful assets for a purpose-led organisation is a team of people who are also fully aligned with the purpose, both at work and on a personal level.

There’s a reason why Greenpeace volunteers are willing to risk their lives to stop whaling vessels. Some purposes can offer infinitely more than just a salary or career path.

By using AI correctly, you could strip away a lot of the banal, repetitive or administrative work that goes with saving the world, fighting the good fight or simply promoting an important message. And the same tech can be used to arm your most powerful advocates with opinion-changing data, game-changing marketing collateral, or just a seriously empty calendar to go off and convince, converse or lobby instead of trying to do everything at once.

This may be where AI becomes the real game-changer for purpose-led organisations. By handing over ‘administrator privileges’ and enabling oversight-oriented work, the purpose becomes the main focus once again, rather than something diluted by the need to keep the lights on…