Industrial companies, including heavy equipment manufacturers, service providers, and leasing companies, have invested heavily in deploying AI and digital capabilities across their operations, reshaping core functions such as field service, supply chains, and pricing operations to improve labor productivity, reduce costs, and enhance decision making.
While there is still more value to be captured from AI internally, many industrials are increasingly turning their attention towards the next frontier: leveraging the technology to develop products and solutions that customers will pay for, including analytics subscriptions, AI-powered service packages, and more.
For most offerings in this space, strong technical performance and demonstrated customer value have yet to translate into direct monetization. End users adopt the tools willingly, but resistance to paying for them persists. And while pilots frequently succeed, scaling AI commercially across the sales force, operations, and customer base remains a difficult hurdle to overcome.
The gap between value creation and value capture is the central challenge for industrials who find themselves on this journey. Drawing on our experience in agriculture, construction, automation machinery, power generation, fleet operations, and more, we outline a proven commercial and go-to-market framework for monetizing AI-enabled solutions. We also clarify the specific choices companies must make across product design, customer engagement, pricing, sales, and organizational structure.
Why Selling Performance Is More Than Deploying AI
A key difficulty lies not in the AI and digital solutions themselves, but in how customers perceive those solutions. Most industrial customers are long-accustomed to traditional commercial models, willingly paying for tangible assets and clearly defined services. AI-enabled solutions, by contrast, are unfamiliar, and the value they create is not as immediately apparent. Several barriers repeatedly surface:
- Legacy expectations around what is “included.” Customers who already pay significant sums for equipment often assume that AI, analytics, and digital tools should come bundled at no additional cost.
- Skepticism about paying for intangibles. Compared with physical assets, software and analytics can feel abstract, making customers hesitant to pay even when the value is real.
- Confusion between data ownership and value creation. Customers rightfully see raw machine data as inherently theirs, without fully appreciating the investment required to turn that data into actionable insights, predictions, and recommendations.
As a result, companies often find themselves in a paradoxical position: Customers acknowledge the value of AI-enabled aftermarket solutions and use them actively, but remain unwilling to pay for them.
A Go-to-Market Framework for AI-Enabled Offerings
Industrial companies that successfully monetize AI-enabled solutions do not simply deploy a product and wait for customers to pay. They build a deliberate commercial and go-to-market capability across value proposition and offer design, customer engagement, pricing and packaging, and sales and conversion. These are not sequential steps; they must be developed and refined in parallel, and they require changes to both the organization and operating model. (See exhibit.)
One of the biggest pain points we consistently observe is not on the customer side. It is with legacy sales organizations that are unaccustomed to selling SaaS solutions, and which often lack enabling capabilities such as packaging and pricing, customer-specific value quantification, and marketing. Companies that fail to build these internal capabilities will struggle to convert even the most differentialed offerings.
A Value-Based Development Approach from Day 1
From the beginning of the AI monetization journey, industrial companies must be laser-focused on creating value for their customers. This means grounding the product in the “physics of the business” for the customer, and prioritizing features and development work accordingly.
Focus on driving business actions.
AI is most powerful when it is pointed at concrete, actionable recommendations for operational improvements, such as suggestions about an underperforming asset. The differentiator is not simply analytics; rather, it is being able to tell a customer: “This is the asset that is having a problem. Here’s what’s causing it, and here’s how to fix it.” That level of specificity is what customers will pay for over the long run, as the AI provides continuous business improvement.
Quantify value impact.
As business actions are identified and interventions observed, companies should work to calculate the anticipated ROI for individual customers, clearly articulating how performance improvements translate to financial outcomes. This means making specific assumptions tailored to the customer’s unique context—including the size of the relevant operation, such as a specific plant, the baseline, and its improvement potential—so that value estimates feel concrete and credible.
Educate on the value of the underlying models.
Leading companies also explain why their solution is more than “just data,” highlighting the investments in proprietary algorithms, models, and continuous improvement. In doing so, they should also showcase why the solution is better than alternatives in the market.
The recent experience of a global manufacturer of food processing machinery exemplifies these principles. The company set out to evolve beyond equipment sales, repositioning itself as a full solutions partner. It built an AI-powered platform spanning throughput maximization, quality improvement, and cost reduction. Early adopters quickly saw tangible benefits: faster parts identification and ordering, proactive maintenance triggered by AI-detected machine signals, and reduced unplanned downtime.
Yet despite demonstrated value, customers consistently pushed back on paying for the tool. Their core objection was data ownership: they viewed the underlying machine data as inherently theirs and struggled to see why they should pay for insights derived from it. What value was the machinery company truly providing?
To shift this perception, the manufacturer redesigned its approach. Rather than leading with the AI product, the team invested heavily to understand its customers’ existing production process and tightly integrated the AI solution into a newly reshaped and improved process with clear “moments of truth” for the AI to influence actions. This allowed customers to experience firsthand that the value came not from raw data but from the specific design of the product and embedded workflows, the proprietary analytical models and predictive algorithms, and process changes built on top of it.
This approach worked. Customers saw the value, increased their engagement, and opted into paid programs. Many became advocates for additional features, spurring further innovation.
The Power of Customer Engagement
Companies must engage customers throughout the development stage prior to the deployment of AI products and solutions. The most successful companies treat select customers as co-development partners—not just end-users—sharing prototypes, soliciting feedback on the accuracy of analytics and the actionability of recommendations, and iterating rapidly. Deep engagement, including three efforts in particular, produces better products and stronger willingness to pay.
Co-develop with customers to sharpen the offering.
Companies should identify a set of anchor customers willing to engage deeply in the product development process. Give them early access to working prototypes populated with their own data. Solicit structured feedback on accuracy, usability, and whether the AI recommendations can credibly influence the decisions they make and actions they can take. Iterate on specific offerings, tiers, and positioning.
Lower the bar for what is tested with anchor customers.
AI tools across the software development lifecycle are making iteration faster than ever before, in some cases enabling live product edits in real time while working with the customer present. Companies should lean into this to accelerate their development and stay at the cutting-edge, even if it means showing actual work-in-progress to anchor customers.
Help customers understand their starting point.
Customers often overestimate their own performance, believing they have no need for solutions that could improve it. Exposing and then bridging the gap between them and their peers is a powerful selling point for AI-enabled solutions.
A vehicle fleet leasing provider offers a useful illustration of these principles. It launched an AI platform to help customers improve fuel efficiency, utilization, and maintenance through benchmarking against similar fleets and targeted recommendations for improvement.
Rather than treating early customers simply as sales targets, the company worked hand-in-hand with them, understanding how the platform fit into their operating rhythms and making the recommendations more actionable through iterative co-development. Customers who were involved in this process became enthusiastic adopters and gained a strong belief in the differentiated capabilities of the solutions. Because the platform had been built around their own workflows, paying for it felt natural.
A Clear Commercialization Strategy
Once the offer is developed and validated with customers, companies face a set of explicit commercial choices that determine whether they can actually scale adoption and capture value.
Choose your resourcing model and make it explicit.
One of the most important and often underdiscussed decisions is how much service to wrap around the digital product. A “resourcing light” model focuses primarily on analytics and software with limited on-site support. This approach commands a lower but more scalable price. A “resourcing heavy” model includes on-site customer success resources, deep integration support, and hands-on value realization; it justifies a higher price and drives stronger renewal rates. Companies can also ladder these tiers in their pricing and packaging model. Explicit decisions about this architecture must be made: Companies that try to serve all customers the same way inevitably under-serve some and over-invest in others.
Price services commensurate with value, and prove it.
Pricing should reflect the value created. A well-observed principle is that providers should expect to capture 25% to 30% of the value that its solutions create for customers. But customers will not pay such fees unless value has first been demonstrated convincingly through test labs, pilot deployments, and data-driven proof points. In practice, the standard model is a fixed subscription priced at a level aligned with expected value, which escalates on annual renewal as the customer accrues evidence of impact. Alternative models include explicit performance-based pricing wherein customers pay only upon realizing certain business outcomes. These can be even more enticing for customers, but require companies to directly measure the value created.
Establish the technical sales motion.
Sales of digital and AI solutions require a meaningfully different skillset than does selling equipment or traditional services. Leading companies deliberately hire for technical sales capabilities and build a rigorous sales motion with demos, customer-specific ROI models, value proofs, and customer success follow-ups. They also establish a sales pipeline and internal incentives to drive desired sales behavior. The “Quarterback” role—a technical sales professional who orchestrates the account team, customer success, and product teams through each moment of truth—is central to this motion.
Plan for a longer sales cycle and customer budget cycles.
Companies consistently underestimate the length of an enterprise software sales cycle, which is what AI solutions fundamentally represent. Customers typically need to see their own data in a working prototype before committing; they will often want to pilot at a subset of locations; and they will need setup support before realizing value at scale. Additionally, customers are constrained by annual planning cycles: If the goal is inclusion in next year’s software budget, proof points must be in place before that planning process begins. Companies should build these dynamics explicitly into their sales planning.
Pursue a land-and-expand strategy.
Rather than seeking large enterprise-wide commitments upfront, leading companies focus on landing a paid foothold, such as a structured pilot or limited subscription, while expanding coverage, functionality, and price over time. Software leaders like Salesforce and Adobe have mastered this; most industrials have not. Free trials can support the “land” motion, but they must be explicitly positioned as temporary, with clear pricing, defined durations, and opt-out mechanics that prevent customers from anchoring on free. The key is having an explicit roadmap for moving from installation to monetization and from initial price to expanded value.
Build customer success to drive renewal.
The lifetime value of these offerings comes from long, sticky revenue streams, not the first-year sale. This makes early customer success critical. Dedicated customer success resources are essential for driving on-site usage, proving value, and securing renewal, particularly in the resourcing-heavy model. Companies that invest in the customer success function observe higher renewal rates and continuously improved economics.
Deploy generative engine optimization (GEO).
With the rise of AI, standard search engine optimization (SEO) is insufficient. Companies must structure content, FAQs, and technical documentation so potential customers’ AI assistants can surface their offering accurately.
An off-road equipment and technology provider put this level of commercial discipline into action. The company introduced a pay-per-use, AI-enabled solution that could significantly reduce customers’ input costs. The product worked, but adoption stalled. The barrier was the business model: Customers were accustomed to owning equipment outright, and a consumption-based pricing structure felt unfamiliar and risky.
The company responded with a systematic overhaul of its commercialization approach. It invested in internal test site deployments to generate credible proof points for the technology across diverse real-world conditions. It built customer-specific operational models to quantify value impact and used them to anchor pricing, deliberately capping fees at no more than 25% of the value created for the customer. It pursued an aggressive land-and-expand strategy, offering risk-free trials and retrofit options to lower barriers to adoption for customers. It explicitly chose a resourcing-heavy model, creating a dedicated customer success function and training its network of dealers on AI sales and support. Throughout the overhaul, the company took a longer-term view on value capture rather than optimizing for early revenue. More recently, the company has begun piloting pay-per-outcome pricing where customers only pay if the promised value is realized, further reducing adoption risk for skeptical buyers.
The results were clear. Adoption, customer satisfaction, and revenue all accelerated following these changes, validating the thesis that the bottleneck was never the technology, but rather the sales process.
Industrial companies are continuing to improve internal operations with AI, with still more headroom to reshape core functions and processes. The next, and more difficult, step is monetizing these new capabilities externally to customers. The barrier is rarely with the digital or AI solutions themselves; it is commercial, behavioral, and organizational.
Companies that succeed do so by making deliberate choices across all four elements of the go-to-market framework:
- defining a sharp value proposition grounded in the physics of the customer’s business
- co-developing the product with customers to maximize actionability
- building commercial capabilities including pricing, packaging, technical sales, and customer success initiatives designed to drive conversion and renewal
- making explicit decisions about how to resource and organize for an SaaS-like operating model
Capital goods and engineered products companies that treat their AI journey as a full commercial transformation and not just a product launch will ultimately unlock new business models, deepen customer relationships, and create sustainable economic value for both themselves and their customers.
