B2M Sales – The Business-to-Machine Model in Practice

I foresee a change coming in sales. Traditional models, based on human-to-human contact, will be complemented by processes involving algorithms and machines – this is precisely the essence of the B2M model (Business-to-Machine). In this scenario it is a variety of systems – from smart home devices to intangible AI-powered algorithms – that become actors in purchasing processes.

This text continues the previous article outlining the general assumptions of the B2M model – I encourage you to read it for broader context.

B2M sales – why it matters

Gartner forecasts indicate that by 2030 a noticeable share of purchasing decisions, in both the consumer and B2B sectors, will be made by machines. This is expected to translate into turnover measured in billions of dollars, although I suspect we have already crossed the billion-dollar threshold – only somewhat unconsciously, because B2M is still talked about very little, if at all. If these predictions come true, marketers and salespeople will be required to adopt a new perspective on their activities.

The first signs of the birth of the B2M model are already visible. Smart printers order ink on their own, and fridges “see” dwindling supplies and initiate deliveries. In more advanced scenarios, autonomous vehicles will be able to book tyre-service appointments by themselves (they may even drive to the workshop unaided), and production-management systems in factories will automatically order missing components. Companies such as Amazon with Alexa or Tesla with Autopilot already leverage the potential of interacting with machines to automate purchases and optimise processes. These – as I would call them – early examples show that the boundary between a human customer and a machine customer is blurring. This opens up new possibilities and challenges for companies.

Soon cars may start booking themselves in for repairs or tyre changes. In general, their level of autonomy is expected to increase.

The future of B2M reaches far beyond simple automations. In such an ecosystem AI agents acting on behalf of consumers and businesses will autonomously seek, negotiate and execute transactions. For the inquisitive: I will mention terms such as agent-driven commerce and API-first commerce – it is worth reading more in my introductory text on B2M.

Companies that recognise this change and learn how to “sell” effectively to machines stand to gain a significant advantage over their competitors. I’m keeping my fingers crossed that many Polish firms will seize the opportunities that come with it!

Time for some inspiration and to present my ideas on how the B2M model can operate in relation to sales.

Examples of B2M sales

Below are several concepts related to sales in the Business-to-Machine model, based on an AI-centred ecosystem that includes agents, robots and other devices. For each idea I provide a brief description, outline a potential mode of operation and present a rationale.

If any example inspires you to start a new company or change an existing business, don’t hesitate to offer me shares (just kidding – or am I? ;)).

1. Selling through a marketplace for AI agents

Imagine Allegro or Amazon, but without the need to browse hundreds of offers… Basically, you just issue a purchase order and pick up the parcel.

Possible application:

• A dedicated platform on which sellers’ AI agents “present” products and services directly to the AI agents of consumers or other companies on the basis of clearly defined customer needs. Queries can be phrased in natural language, e.g. “Find a juicer that performs well for daily carrot-juice making.”
• Instead of people trawling through dozens of websites, consumer or corporate agents broadcast requirements, and sellers’ agents supply appropriately matched offers with all the details.

Potential operation:

1. The client’s agent publishes a request on the platform in a structured format, e.g. JSON, based on the needs set out by the user.
2. The same marketplace contains sellers’ offers – agents representing clients can choose the most advantageous offer or initiate additional questions or negotiations.
3. Finally, the client’s agent evaluates the offers, checking various parameters (including certificates, user reviews, etc.) and constraints (budget, delivery time, etc.).
4. The client’s agent either automatically selects the winning offer or asks for human approval (always or in specific situations, e.g. if the purchase exceeds a certain amount).

Rationale:

• Time savings because there is no need to search through many suppliers’ sites.
• A structured data format allows every offer to be precisely matched to the user’s requirements.
• Transparency arising from the fact that agents operate in a standardised environment, creating fairly equal opportunities for both large and small enterprises.

2. Auctions on an agents’ marketplace

Before I outline the idea: bots that bid on Allegro on users’ behalf have long been present on the Polish market. I assume that with AI these bots will become smarter and more advanced. Here, however, we are considering the opposite scenario…

Possible application:

• Dynamic auctions in which users’ agents remain open for a period to offers that meet specified objectives (e.g. insurance policies, regular household-goods purchases).
• Sellers’ agents monitor those open “requests” and adjust their bids in real time to stay competitive, while knowing their current chances of closing the deal.
• The challenge I foresee is that agents would prefer to submit an offer as late as possible to minimise bid count and thus secure more favourable terms from the seller’s perspective.

Potential operation:

1. The client instructs AI agents to search for, say, the best third‐party car‐insurance policy over a set period, costing under PLN 1,000 per month with minimum coverage.
2. Insurance providers’ agents run an auction: if a competitor lowers the rate, they automatically adjust their own offer.
3. Price need not be the sole deciding factor – other criteria may also be scored.

Rationale:

• Enables users to secure optimal offers without manually scanning the market or negotiating. Since an agent selects the most attractive current offer, companies must continually offer compelling terms to retain clients.
• Suppliers compete almost in real time with low operating costs and potentially reduced spend on traditional advertising.
• I recently booked a venue for a training course – reviewing a dozen imperfect offers and choosing the best one was a real chore, but I managed it!

3. Product bundles curated by sellers’ agents

We are used to getting a better deal when purchasing a bundle. AI can elevate this principle, benefiting both consumer and seller – not least because only a single packing and delivery are required.

Possible application:

• Sellers or service providers collaborate to create carefully curated bundles that address recurring customer needs or a one-off client request.
• Bundles could also arise from current market demand. Partnerships between sellers might be governed by a smart contract on a blockchain.

Potential operation:

1. Agents representing companies in related or diverse sectors assemble bundles of products and/or services, driven by ongoing client requests, market observations or advertising data indicating interest in certain solutions.
2. Clients’ agents compare those bundles with the cost of acquiring each product and/or service separately.
3. Bundles update automatically whenever any component’s price or stock level changes.

Rationale:

• This approach enables upselling and cross‐selling at the very first transaction and on a broader scale than if a single company made the offer.
• Agents present only those bundles matching the user’s requirements. For the client, buying a bundle likely means a lower price and the opportunity to identify other purchasing needs earlier.
• Ultimately, it can save both money and attention – a win–win for both parties.

4. Loyalty programmes and purchase rewards

As I wrote in the general article on B2M, loyalty in sales may come to mean something slightly different than before. These changes can benefit both customers and companies running or participating in loyalty programmes.

Possible application:

• Clients’ agents or devices can enrol in a loyalty programme. By identifying themselves, they collect and redeem points or other benefits – across one or multiple providers.
• Because they operate continuously and automatically, they can, at scale, seek optimal ways to earn and use the perks offered by the programme.

Potential operation:

1. The loyalty programme provides an API for agents to record and redeem points.
2. The client’s agent consolidates the loyalty schemes of all participating brands and identifies the optimal redemption paths (e.g. flights, hotel upgrades, spa treatments – whatever holds the greatest value for the client).
3. The system can suggest that the user shift spending to specific providers if doing so would lift them to a higher programme tier.

Rationale:

• Automatic point redemption and tier monitoring save the client time and deliver both financial and non-financial benefits.
• If a brand’s programme integrates well with agent systems, consumer agents will favour it. This may also encourage consolidation of loyalty schemes – though whether that truly serves customers is another question (see: airline miles becoming a de facto currency that can be issued without limit).
• Transparency and automatic verification of point accrual would be greatly improved if agents maintained a standardised transaction ledger.

5. ‘Conscious consumerism’ on autopilot

Fast fashion (low-quality clothing produced en masse and discarded frequently), disposable culture (frequent reliance on single-use items) and similar trends are neither good for us nor for the environment – not least because of the waste they generate. But there is hope for change – perhaps the times are coming when conscious consumerism will not mean spending masses of time analysing what we buy. This could also be an opportunity for local producers and farmers, for example those organised in cooperatives.

Possible application:

• Brands publish information and metrics demonstrating their care for the environment and employees, supply chain and other ethical issues in a standardised data format. A starting point could be the ESG metric already used by some companies, although I’m not sure it’s the most suitable option.
• Machines and algorithms making purchases on consumers’ behalf filter shopping recommendations based on the user’s value preferences.

Potential operation:

1. The user sets a rule, for example: “Prefer products from companies using recycled packaging”, or “Prefer products from Poland to support local businesses”.
2. Sellers’ agents provide data on their supply chain, production processes and certifications.
3. Consumers’ agents highlight (or select only) suppliers that meet the user’s criteria.

B2M may enable support for local companies and/or those that stand out in values important to the customer.

Rationale:

• Local companies that strongly differentiate themselves by component quality or other ethical criteria may generate higher sales.
• People “vote” with their wallets for the ideas they care about – effortlessly, as their agents enforce those preferences at scale.
• Since the B2M model largely operates in the digital world via APIs, information provided by the seller can be automatically verified. For example, the authenticity and validity of a certificate can be checked with a recognised body using blockchain – so there’s no need to rely solely on the seller’s declaration.
• A challenge I foresee is the need for trusted certifying institutions or a decentralised network to confirm the authenticity of declarations.

6. Recommendations for cross-selling and up-selling

I’ve already written about selling products in bundles. But thanks to B2M the possibilities are greater and not necessarily immediate – additional purchases may be suggested after a certain time. They may stem from predicted usage, but they can also be initiated by the purchased devices themselves.

Possible application:

• An innovative approach to recommending “related products”, but at machine scale, managed by the client’s agent, which suggests additional purchases based on forecasts, observed promotions, or signals from sellers’ agents.
• Instead of cluttering our attention with suggestions, the client’s agent analyses relevant cross-selling and up-selling options in the background.

Potential operation:

1. At the time of purchase, the user’s agent checks whether complementary products are available at a discounted price or if extended warranties/upgrades are advantageous.
2. If a cross-selling offer meets the user’s requirements (price, brand preferences, existing products), the agent automatically adds it to the basket or suggests it.
3. Sellers’ agents may offer real-time cross-selling bundles with dynamic discounts to encourage additional purchases. They can also take the initiative based on the typical usage path of the product.

Rationale:

• Cross-selling occurs only when an additional product or service genuinely appears to match the user’s preferences or is simply advantageous. This could theoretically reduce impulse purchases and help avoid unnecessary spending, for example when we are unaware of the risk that an extended warranty – so zealously offered by some sellers – protects against.
• Businesses gain additional sales, and consumers find suitable, attractively priced accessories or consumables without intrusive advertising.

7. Group purchases overseen by agents

AI can take us back to the days of “Groupon on steroids.” What do I mean by that? Let’s see what AI agents could hypothetically offer us…

Possible application:

• Group purchases organised by consumer or corporate agents, where the group consists of clients not necessarily connected to each other who, thanks to their combined “mass,” can secure better terms.
• Groups can often access bulk discounts unavailable for single orders, so purchases may resemble wholesale in scale.

Potential operation:

1. The user’s agent recognises that several neighbours or friends (or even unconnected individuals who – via their agents – have somehow “banded together”) intend to buy the same category of products (e.g. electric scooters).
2. Clients’ agents connect to form a potential group purchase request.
3. Sellers’ agents detect the large collective transaction and automatically apply a wholesale discount (without separate negotiations).

Rationale:

• Traditional group-purchase methods rely on social media or dedicated platforms. In the B2M model, agent-driven group sales could minimise operational costs.
• Sellers’ agents see aggregated demand, giving them clearer insights into pricing and logistics.
• Ultimately (in an ideal world) the company reduces costs while clients obtain a better price.

8. Reservations managed by agents

I believe current booking platforms could undergo a true revolution or give way to entirely new solutions.

Possible application:

• A B2M platform for scheduling professional services – dentists, beauty salons, personal trainers, property viewings – where time slots are automatically reserved/sold by AI agents.
• Agents make bookings based on multiple criteria, whether one-off or recurring – e.g. regular hairdresser appointments.
• Of course, the same could apply to restaurant table reservations.

Potential operation:

1. Sellers’ agents publish current availability calendars, accounting for changes – including the impact of external factors such as weather on slot availability (good weather might open more outdoor tables).
2. Clients’ agents, with access to providers’ calendars, book time slots that match their users’ preferences (e.g. the earliest morning appointment under $30).
3. If a booking is cancelled at the last minute, the seller’s agent can announce a “last-minute” slot at a reduced rate. Agents can immediately reserve it for their clients.

Rationale:

• Clients save time and no longer need to remember to schedule appointments.
• Providers fill empty slots with minimal manual effort.
• Peak-hour rates can be dynamically adjusted, as can quieter-period pricing.
• Agents can manage reminders or automatic cancellations in case of calendar conflicts.

Summary

All these ideas revolve around one key feature of B2M: machines take centre stage in searching for offers, negotiating, purchasing and post-sale support – supporting or even replacing humans in these areas. Companies that adapt their sales processes to the B2M model stand to gain a great deal. I’m especially keeping my fingers crossed for Polish firms!

Let’s recap:

• Sales may shift towards automatically matching offers to user intent rather than grabbing attention through advertisements.
• Customer loyalty shifts towards consistency, trust and seamless integration with clients’ agents. Technology will play an even more important role, with all the downsides that entails.
• The human role in the purchasing process can still be important. At the same time, in B2M clients can delegate decision-making to their agents.

Will the future be as I’ve outlined? I don’t know, though I can imagine it. If so, thanks to B2M the entire sales funnel (awareness, consideration, conversion, retention) may centre on agent-to-agent interactions.

The future of B2M sales also involves a change in marketing strategies. I believe that traditional methods of capturing human attention will give way to precisely delivering information that is relevant and “understandable” to algorithms. Marketing is likely to become a more automated process of matching offers to defined intents and needs expressed by AI agents. Very soon I will publish a post with ideas for B2M marketing.

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