Software companies integrating artificial intelligence into their products face significant challenges in monetization, largely because they cannot demonstrate clear, measurable benefits to their customers, according to a new report by McKinsey & Company. The analysis highlights a growing disconnect between the high cost of AI features and their unproven impact on business productivity and cost savings.
The report, titled "Upgrading software business models to thrive in the AI era," identifies critical hurdles that are slowing the widespread adoption and profitability of AI-enabled software. Many businesses report that while AI tools increase their IT expenses, the promised reductions in labor costs or boosts in efficiency have not yet materialized, leading to customer hesitation and skepticism.
Key Takeaways
- A McKinsey report finds software vendors struggle to sell AI features due to a lack of demonstrable return on investment (ROI).
- Only 30% of software firms have published quantifiable results from real-world customer AI deployments.
- Integrating AI could increase software prices by 60% to 80% for some business functions, without corresponding cost savings.
- The report identifies three main challenges: unproven value, poor adoption scaling, and unpredictable pricing models.
- Businesses are reluctant to reduce headcount based on AI promises, a key factor undermining the technology's value proposition.
The Core Problem: A Lack of Measurable Value
The primary obstacle for software-as-a-service (SaaS) vendors is the difficulty in proving that their AI tools provide a tangible return on investment. While companies often promote potential AI use cases, McKinsey found that very few can back up these claims with hard data. According to the report, only 30 percent of software firms have published quantifiable results from actual customer deployments.
This lack of evidence creates a significant problem for customers. Many see their IT budgets swell to accommodate expensive AI features, but they are not seeing a corresponding decrease in other operational costs, such as labor. The report suggests that fully AI-enabling the customer service stack for a typical business could result in a price increase of 60 to 80 percent.
By the Numbers
An MIT study cited in related analyses found that many enterprise organizations have so far seen zero return from their investments in artificial intelligence, reinforcing the challenges highlighted by McKinsey.
This sentiment was captured in a quote from a human resources executive at a Fortune 100 company, who expressed frustration with the current state of AI tools.
"All of these copilots are supposed to make work more efficient with fewer people, but my business leaders are also saying they can't reduce head count yet."
This perspective underscores the gap between the theoretical promise of AI efficiency and the practical realities of business operations. Without the ability to reduce staff or significantly improve output, the high cost of AI becomes difficult to justify.
Adoption and Pricing Hurdles Compound the Issue
Beyond the value proposition, McKinsey identifies two other major challenges that hinder AI monetization: scaling adoption and unpredictable pricing structures.
Underinvestment in Change Management
The report argues that many companies fail to scale AI adoption successfully because they underinvest in change management. New tools require more than just technical implementation; they need comprehensive user training and performance monitoring to be effective.
McKinsey suggests a specific investment ratio to overcome this. For every $1 spent on developing an AI model, companies should expect to spend $3 on change management. This includes training employees on how to use the new tools effectively and establishing metrics to track their impact on performance.
Complex and Opaque Pricing
The third major issue is the lack of predictable pricing. Customers often find it difficult to forecast how their AI-related costs will scale with usage. The pricing models are frequently complex, opaque, and tied to consumption metrics like data processing or token usage, which are hard for businesses to estimate in advance.
Shifting Purchase Decisions
McKinsey notes that purchasing decisions for software are increasingly moving away from central IT departments to individual line-of-business leaders. These managers evaluate AI tools based on their direct impact on departmental budgets and outcomes, often making trade-offs between investing in technology and hiring more staff. They demand conversations centered on value, not just features.
Navigating the Path to AI Monetization
To address these challenges, McKinsey advises software vendors to rethink their business models, with a primary focus on pricing and value communication. The report suggests that while the traditional per-user monthly subscription model is unlikely to disappear, it will need to evolve.
Vendors are expected to incorporate consumption-based elements into their pricing. Many are already experimenting with hybrid models, which include a base subscription with a capacity cap. Usage beyond this cap is then metered in various ways:
- Metered Throughput: Limiting the number of tokens processed daily, weekly, or monthly.
- Per-Task Pricing: Charging for each specific action or task completed by the AI.
- Outcome-Based Pricing: Tying costs to a specific business result, such as the number of qualified leads generated for a sales tool.
However, the report warns that the rapid evolution of AI means that today's premium features can quickly become standard expectations. Furthermore, the cost of AI model delivery is dropping significantly. According to McKinsey, the cost of large language model (LLM) delivery has declined by more than 80 percent per year over the past two years. This trend requires vendors to carefully balance their pricing to encourage adoption without eroding their own margins.
The Reality of AI Productivity
The challenges outlined by McKinsey are reflected in real-world studies. For example, a trial of Microsoft's Copilot AI tool by a UK government department found no discernible boost in productivity among employees. This type of result reinforces customer skepticism and makes it harder for vendors to sell expensive AI upgrades.
Despite the current hurdles, the massive investments poured into AI development mean that companies will continue to push for monetization. The key, as the McKinsey report suggests, will be shifting the conversation from technological capabilities to proven business value. Until vendors can consistently demonstrate how their AI tools cut costs, increase revenue, or boost productivity in a measurable way, broad market acceptance and profitability will remain challenging.