The next major change in industrial sales may not come from artificial intelligence at all. It will come from an economic reality in which supply chains, sales markets, and procurement decisions have become significantly less stable.
For many years, German industrial sales operated under a comparatively reliable model. Companies knew their markets, salespeople knew their customers, and new target customers could be identified based on relatively stable characteristics. Industry, company size, region, revenue, technologies used, or specific applications provided a useful answer to the question of which companies might generally be a good fit for their offerings.
This logic still works. It’s just no longer enough.
Because in a market that is changing more rapidly, another question is becoming increasingly important: Who needs our offering right now?
Current figures from the DIHK show just how much the business environment has changed. In the “Going International 2026” study, 69 percent of German companies active internationally report increasing trade barriers. This is the highest figure since the survey began. Among the factors cited are higher tariffs, export controls, sanctions, additional certifications, local content requirements, and increasingly complex regulatory requirements. A total of 2,400 German companies operating internationally participated in the survey, more than half of which were from the manufacturing sector. (DIHK: Going International 2026)
From a sales perspective, the fact that trade barriers are increasing is less significant. What matters is how companies respond to them.
They are seeking additional suppliers, exploring new procurement markets, reducing dependencies, and relocating parts of their value chain. Some companies are tapping into new sales markets, while others are looking for regional alternatives to existing suppliers or deliberately establishing a second source of supply.
The “Supply Chain Pulse Check 2026” by Deloitte and the BDI also describes this trend. The authors expect that the growth of German industrial exports in the coming years will be significantly weaker than in the past. At the same time, growth potential is shifting between regions and markets. Companies should therefore monitor geopolitical developments and risks in their sales and procurement markets much more systematically. (Deloitte: Supply Chain Pulse Check 2026)
As a result, supply chains are not simply changing—they are becoming more dynamic.
And it is precisely this dynamism that is creating new demand.
A supplier that goes out of business is initially a risk for a company. For an alternative supplier, the same situation can be a concrete sales opportunity.
The same applies when a manufacturer is looking for a second source, relocating production from Asia to Europe, needs additional capacity, or must substitute a raw material. Site expansions, new product lines, or regulatory changes can also cause a company to suddenly need different suppliers, technologies, or production partners within a short period of time.
The ifo Institute shows that such situations are no exception. In June 2026, 17.2 percent of German industrial companies reported difficulties in procuring intermediate goods. Particularly affected sectors included electronics and optical products, the chemical industry, and manufacturers of electrical equipment. Companies in the mechanical engineering sector also continued to report material shortages. (ifo: Material Shortages in Industry)
A further study by the ifo Institute also shows that unexpected material shortages can significantly disrupt industrial production in the short term. (ifo: Effects of Material Shortages)
For sales, this represents a decisive shift in perspective.
After all, a company isn’t interesting simply because it belongs to a certain industry or has a certain number of employees. It becomes interesting because something is changing there.
Let’s take two companies as an example.
Company A fits the classic Ideal Customer Profile perfectly. The industry is right, the company size is appropriate, the region is promising, and the technological requirements look good as well.
But there’s currently no reason for a change. The existing suppliers are working well, capacity is sufficient, and no major investments are planned.
Company B may only fit the existing target customer model moderately well. But a strategic supplier has just dropped out. At the same time, production is growing, and additional manufacturing capacity is needed in the short term.
Which company should the sales team approach first?
This example clearly illustrates why static target customer models alone are no longer sufficient.
They help answer the question of who could, in principle, be a customer.
But they do not answer who currently has a reason to make a change.
And it is precisely this distinction that is becoming increasingly important.
Traditional B2B sales often begin with the question: Which companies are a good fit for our offering?
In dynamic markets, a second question must be added: What is happening right now at which companies that makes a purchase more likely?
At first glance, this sounds like a minor difference. In fact, it significantly changes the sales logic.
Industry, number of employees, revenue, and location describe a company. But they provide little insight into why this company should act differently today than it did six months ago.
That requires context.
What products does the company manufacture? What technologies does it use? Which markets does it serve? Where is it investing? Which locations are being expanded? What new products are being developed? Are there signs of capacity issues, supplier changes, or new regulatory requirements?
Only when such information is linked together does a company’s address give rise to a true sales picture.
McKinsey describes the current trends in industrial sales as one of the most significant commercial upheavals in decades.
Traditional industrial sales rely heavily on technical expertise, personal relationships, an installed base, and established networks. This model is particularly successful among German small and medium-sized enterprises (SMEs).
However, it works particularly well in familiar markets.
As soon as companies seek to tap into new industries, applications, or regions, the personal knowledge of individual salespeople quickly becomes insufficient. At the same time, geopolitical shifts and volatile supply chains are causing potential demand to shift more rapidly. (McKinsey: How AI-led commercial transformation can power industrial growth)
In this context, McKinsey describes an industrial company whose lead generation relied heavily on manual research and external company lists. The information was expensive, updated slowly, and often did not match the respective products and applications precisely enough.
The company therefore developed an AI-powered “Lead Hunter” that could automatically identify, qualify, and prioritize potential customers.
This example is interesting because it shows where AI can actually make a structural difference in sales.
Not when it comes to writing the next email.
But rather in determining which companies are even relevant.
Actually, this idea isn’t new.
A good industrial salesperson observes their market very closely anyway. They know when a competitor is having problems. They find out when a customer is planning a new plant. At a trade show, they pick up on the fact that a certain material is in short supply or that a company is looking for a new manufacturing partner.
This knowledge is incredibly valuable.
The only problem is scaling.
A salesperson might be able to monitor 50, 100, or 200 companies very closely. But they can’t constantly analyze 5,000 or 10,000 companies. They can’t read every press release, review every job posting, track every facility expansion, or keep up with every technological change.
This is exactly where AI changes the game.
It can analyze very large volumes of publicly available information, compare companies with one another, and identify changes that could be relevant to a specific sales proposal.
AI does not replace the salesperson’s understanding of the market.
It scales it.
This is precisely where the distinction between lead data and commercial intelligence becomes interesting.
A traditional company database answers questions such as: Which companies exist? In which industry do they operate? How large are they? Where are they located? Who is the CEO, purchasing manager, or sales manager there?
This data remains important.
Commercial intelligence, however, goes a step further. It seeks to understand what’s happening behind the scenes with this company data.
What changes are currently taking place? What developments could create demand? How well does this potential demand align with our own offerings? And which of the many possible opportunities actually deserve attention?
This also changes the nature of sales work.
The task is no longer to generate the largest possible lists of potential customers and then work through them.
The more exciting task is to filter out, from among thousands of companies, precisely those where the fit and timing align.
That’s why we didn’t develop bloo.research as just another lead database.
The starting point was a different question: How can we apply the knowledge and experience of a good sales team to a much larger number of companies?
That’s whybloo.research doesn’t just analyze companies based on traditional company data. What matters most are their products, technologies, markets, applications, and other characteristics that may be relevant to a specific offer.
This makes it possible, first and foremost, to determine much more precisely which companies are truly a good fit for your own offering.
The more interesting next step, however, begins where changes come into play.
Then the question is no longer just:
Which companies could use our products?
But rather:
Which companies are undergoing developments that could currently create a specific need?
This can transform a traditional search like “Find me mechanical engineering companies with 50 to 500 employees” into a much more precise investigation.
For example, searching for industrial companies that use specific components but whose current supply chain is under pressure and for which an alternative European source of supply might be of interest.
Or a search for companies that are expanding their production and therefore need additional manufacturing capacity.
Or manufacturers whose products and technologies suggest that a specific solution could become relevant in light of a current change.
This is a different approach.
We’re no longer just looking for companies.
We’re looking for situations that could give rise to a need.
Good salespeople have always wanted to identify such situations early on.
The idea isn’t new.
What’s new is the scale at which we can implement it today.
Just a few years ago, it would have been virtually impossible from an economic standpoint to analyze thousands of companies individually, understand their products and technologies, consolidate a wide variety of information, and systematically derive hypotheses about potential needs from it.
With today’s AI, this equation is changing.
At the same time, the value of such a capability is increasing because markets are moving faster.
Supply chains are changing. Sales markets are shifting. Technologies are evolving faster. Geopolitical decisions influence procurement and production. Material shortages are altering priorities.
As a result, sales opportunities are also changing more rapidly.
And that is precisely why market monitoring is becoming an increasingly important sales discipline.
Personal relationships are not becoming any less important in industrial sales.
Complex industrial purchasing decisions require trust, technical expertise, and people who truly understand the customer’s situation.
AI should not replace these relationships.
However, it can help identify earlier on where a meaningful relationship should even be established.
Perhaps that is why this is the more important change in sales:
Not that AI replaces the salesperson.
But rather that, for the first time, the salesperson can observe a market that is much larger than their personal network.
Traditional B2B sales was developed for relatively stable markets.
That stability is declining.
And as a result, it’s increasingly not enough to know who might buy in principle.
We need to identify who currently has a reason to buy.
That’s exactly where commercial intelligence begins for me.
DIHK: Going International 2026: Trade Barriers at Record Levels
Deloitte & BDI: Supply Chain Pulse Check 2026
ifo Institute: Material shortages in industry are worsening
ifo Institute: Material Shortages Are Slowing Industrial Production
BDI: Companies Are Strengthening Their Resilience
McKinsey & Company: How AI-led commercial transformation can drive industrial growth
In addition to manual research, we’ve developed automated tools for you:
bloo.research— Find the right B2B companies in minutes
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