Here’s Why Analysts Say Machine Buying Will Be The Megatrend Of AI

AI For Business

Tech firms are scrambling to develop ‘AI agents’ that can perform complex, multi-step tasks on our behalf.

While the current wave of enthusiasm for AI was ignited by large language models that can show and tell us things, there is firm consensus across the industry that the next and potentially much larger wave will be led by bots that can increasingly do things too.

A lot of these tasks will inevitably involve some form of purchasing. So what happens when we let AI go out and do the shopping for us? Or negotiate our corporate contracts? At scale, the implications are vast, say researchers.

AI isn’t swayed by clever marketing slogans. It doesn’t feel pressured by hard sales tactics. It’s not impressed by influencer endorsements. It’s not dazzled by pretty packaging. It’s not bamboozled by gimmicks (and nor will it miss the crucial small print). It can’t be wined and dined. And it doesn’t care for tickets to whichever big game is coming up.

But AI can make timely and highly informed purchases with unprecedented efficiency. It ignores low value features, filters out irrelevant information, and makes more rational, quicker purchases based on the vast amounts of data it can collect and consume about both the market and the needs of the person or company it serves.

As a result, AI is increasingly being deployed by both consumers and companies for the purpose of machine buying.

It already exists in the form of dedicated hardware like smart speakers, connected products capable of communicating their own needs, and – increasingly – buyer bots that can operate across any internet-connected device. These machine buyers are creating significant new value and making purchases that their human owners and operators previously wouldn’t even often have time to research.

As this trend continues, some analysts believe that machine buying will be as transformative to global business as e-commerce and will unleash a wave of economic growth comparable to the rise of emerging markets in the 1990s.

Don Scheibenreif and Mark Raskino are senior analysts at Gartner and have conducted extensive research into the evolving impact of technology on purchasing, including through interviews with a wide range of business leaders. They see a two decade transition to full scale machine buying, with about 20% of business revenue in 2030 already coming from what they call ‘custobots’.

Just this week, for example, leading telcos added their voices to the consensus that machine buying is the future.

Rather than just fending off malicious bots with captcha forms, businesses have begun thinking about how to also welcome bots as some of their most valuable customers, while also sending out their own buyer bots to conduct autonomous negotiations with their suppliers.

This is leading to a radical rethink of how all businesses function, whether their customers are high street consumers or Fortune 500 companies.

Scheibenreif and Raskino have compiled their analysis in When Machines Become Customers, which they describe as a field guide and survival manual for “this profound change to business in the 21st century”. It’s highly recommended reading for all C-suite leaders and procurement professionals.

“It’s a now thing”

Until now, it’s sellers who have been able to take the lead and phase in new automation technology – although not always smoothly for customers. We’ve all experienced a clueless chatbot, a QR code menu that doesn’t load, or an “unexpected item in the bagging area”.

Nevertheless, the trend is clear.

Automation keeps generating new value from greater efficiencies, while the technologies behind it keep improving, and we all keep accepting these new innovations as the new normal.

In many parts of the world, people don’t even bat an eyelid at the sight of autonomous delivery drones carrying groceries up and down the sidewalk.

However, there’s no reason to assume this trend would remain purely in the control of sellers. AI is already flipping the initiative, enabling buyers to introduce more automation to create efficiencies from their end too.

Scheibenreif and Raskino point out that machines are already capable of handling each part of the buying process. They can weigh up reviews, receive messages from sellers, request more information, negotiate for the best deals, make payments, request support when needed, and share feedback.

Gartner Vice President and analyst Michelle DeClue says “machines as customers is not just an ‘in the future’ type of thing. It’s a now thing.”

When Scheibenreif and Raskino began exploring the subject of machine buying more deeply, they found examples of it in various forms emerging and fast evolving across every type of purchasing – all the way up to Fortune 500 companies that send out bots to conduct autonomous negotiations with their suppliers, including over multi-million dollar deals.

By studying its ongoing evolution and impact, they concluded that machine buying will be the most significant and disruptive megatrend of AI over the next few decades, just as e-commerce was the most significant for the internet.

The evolution of machine buying

Scheibenreif and Raskino see buyer bots evolving in three stages.

Prior to these, there’s a zero ‘announcer’ stage that serves as the primordial soup for the emergence of machine buying. This is when technology simply tells us things that help prompt and inform purchasing, like a printer that says it’s running low on ink or a car that tells you it’s time for a maintenance inspection.

This creates routine work for human owners who are therefore receptive to the idea that the machine could help take some of the hassle out of the process. Meanwhile, the manufacturers are certainly more than happy to help guide further spending.

In the case of printers, for example, selling the ink cartridges later is actually a key part of the business model.

This, therefore, leads to the first ‘bound’ stage of machine buying where machines evolve from merely announcing information to acting upon it, although still within the confines of a preset choice.

Many people will have experienced this for the first time when their printer ordered its own ink, but this approach is now used by a wide variety of consumer products – from smart cars to connected toothbrushes.

In the defense industry, this has been happening even earlier and on a far more complex scale. BAE Systems, whose products include multibillion dollar warships for the British Royal Navy, has been using machine learning starting with its Type 45 destroyers, which can diagnose their own maintenance needs and communicate them to its shore-based supply chain, drastically reducing the inefficiency of routine maintenance.

So far, these examples are still led by sellers.

However, consumers don’t want their machines to be kept in walled gardens from where they can only order replenishments from their original manufacturers.

Anyone currently developing AI agents for machine buying should be mindful of the hard lessons learned by Yahoo in earlier days of the internet. After obtaining a strong market position as a web directory, it developed a business model based largely on leading its users towards its own products. But Google soon leapfrogged Yahoo and achieved astronomical growth because it understood that being the leading web portal meant prioritizing the best results for users from across the web.

This same kind of market pressure leads to the next phase of machine buying.

Buyers will keep favoring the development of buyer bots that can operate at their most freely and make the best purchasing choices from across the widest possible markets.

So bound machine buying inevitably becomes adaptable machine buying, in which the best purchases can be made among competing choices.

Many people already have a home assistant in the form of a smart speaker, such as Amazon’s Alexa, which can order a wide variety of products on a simple voice command and with ever increasing sophistication to help determine which products are most suitable among competing choices available.

As the technology continues to evolve, crunch more data, and become ever more intuitive for users, next comes the autonomous stage of machine buying. This is where things get really interesting, according to Scheibenreif and Raskino.

In this stage, buyer bots can mimic a full range of human buying behaviors, but with superhuman capabilities. Purchases are then increasingly made based on the inferred needs of the person or company that the AI is serving.

“It is inevitable,” argue Scheibenreif and Raskino, “that machines, now endowed with ever increasing levels of intelligence, will do more of our work for us, including our work as customers.”

AI Won’t Take Your Job, But It Might Take Your Customers Elsewhere

Some may resist this future out of concern that we might be losing something human on a significant scale.

But does outsourcing the purchasing of laundry detergent to a machine really make you less human?

Most buying is tedious work and not a good use of human time, nor something that humans are even particularly good at.

The inefficiency of the way we currently shop can also be deeply unfair on poorer and otherwise less advantaged members of society who pay more for almost everything, from groceries to energy bills.

“You may enjoy shopping some of the time,” write Scheibenreif and Raskino. “Maybe you enjoy looking for clothes, or going to the bookstore, or buying a special gift for a friend or partner. But you probably don’t enjoy shopping for toilet tissues, tires, or life insurance. And you almost certainly don’t like making business purchases.”

Even in the absence of AI, most purchasing doesn’t actually involve any meaningful human connections. Instead, it saps away time that could be used for exactly that.

At large enterprises, the vast majority of supplier contracts are not being negotiated between humans in any significant way. Most suppliers are given standardized, ‘cookie cutter terms’ that get rolled over, even though both sides could find better terms and generate more value together if they simply had more time to negotiate with each other.

It’s just not economical for humans to properly research and spend time negotiating all the potential deals around them that could bring them value.

That’s why, where buyer bots have already been deployed by large enterprises for autonomous negotiations with suppliers, they haven’t been replacing human work so much as they’ve been extending human capabilities to engage with suppliers who were previously unengaged.

We don’t worry that human connections are being lost when we step into an elevator that no longer has an attendant or when we board a plane that now also has an autopilot. Buyer bots, like modern elevators and modern aircraft, can help us make more human connections as an outcome of the value they deliver.

Scheibenreif and Raskino say the rise of machine buying will be a 20 year change wave that will always require considerable human work managing these bots in order to ensure they create maximum value. Every context in which machine customers operate will be humanly conceived and enacted. So professional human buyers will have much to gain as they manage this transition.

As with the internet, machine buying will very likely be a net positive for jobs, both directly and also through its wider value creation across the economy.

But, as machine buying takes away significant drudgery and opens new markets, capturing their business will require radically rethinking every business function.

Don’t worry about AI taking your job, say Scheibenreif and Raskino, but do worry about AI taking your customers elsewhere.

The jobs most at risk in an age of highly efficient machine buying are those that are currently being sustained by inefficient or under-informed human purchasing decisions, as well as those that depend on persuading humans to make purchasing decisions.

If you are trying to get people to buy insurance that is unnecessary or a timeshare scheme with hidden fees, then it’s fair to say you should already start reconsidering your long-term career prospects.

Meanwhile, roles in marketing and sales will become mostly technical as companies increasingly focus on reverse engineering how to attract buyer bots, just as e-commerce led to the rise of optimization specialists focused on reverse engineering search engine results.

You may not be able to take an AI out for dinner, but it is certainly hungry for data that you can feed it.

It will be fascinating to see how this more efficient and sophisticated approach to purchasing on a wide scale can help incentivize wider benefits for society and the planet – such as by calculating how ethically sourced different products are and letting buyers choose based on those factors.

While AI won’t be dazzled by pretty packaging, it could extract the data on the carbon footprint of that packaging (along with the rest of the product) in order to judge which of competing products come out top by environmental standards.

This is far from hypothetical. Fortune 500 companies already use their buyer bots to collect all kinds of data about their suppliers within the purchasing process in order to help inform their drive towards corporate social responsibility goals like diversity, equality, and inclusion.

Ahead Of Schedule

Since Scheibenreif and Raskino first published their book last year, we have more data and a better understanding of how their analysis is tracking against real world progress.

If their conclusions are wrong, then it might just be in underestimating the scale and positive impact of machine buying.

For a start, the pace of AI development has since exceeded expectations.

In addition, the authors argued in their book that buyer bots acting on behalf of large enterprises will be tough negotiators for suppliers due to their ability to consume vast market data and reach out to the widest range of alternative suppliers.

However, AI will always search for the most optimal way to achieve its objectives, which means playing games its own way. In negotiation, AI has recognized that this is not a zero-sum game and so has not been mimicking hardball negotiators.

Instead, the more that AI has been used for autonomous negotiations between companies, the more that AI has learned to achieve the best outcomes through a collaborative approach that seeks to maximize value for both sides.

This is smart not just for getting the most value out of any one deal, but also to ensure the long term health and motivation of the supply chain.

In many cases, machine buying involves bots that are just going out to existing suppliers in the midst of their contracts and offering business growth opportunities as that helps both sides unlock more value.

The more that people and companies use buyer bots, the more ways they discover those bots can create new value. Large enterprises originally measured only the hard savings, but there’s now an increased focus on the way that machine buying can increase business velocity through better processes and governance, as well as stronger and more flexible relationships with suppliers.

On an individual level, our purchasing power has a large influence on our quality of life and opportunities. In business, few things define a company more deeply than what it buys.

While the global market will continue to change and there will almost certainly be more black swan events on the way, the people and companies able to react fast and extend their spending most efficiently will dominate the world of tomorrow.

The age of machine buying isn’t just inevitable. It’s already begun.

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