The traditional time-and-materials (hourly) model has been the backbone of IT and consulting services for decades, but that may be changing. Modern AI tools – from code-generating assistants and automated testing frameworks to data analytics and generative documentation – are slashing the hours required for routine tasks. In effect, AI is “turning hours into minutes”: work that once took days of human effort can now be done in a fraction of the time. For example, AI-driven analysis and reporting tools can draft insights far faster than manual effort. While this boosts productivity, it creates a paradox for hourly billing: if each task takes half as long, providers must bill twice as many tasks to maintain revenue. As one analyst notes, the “traditional time-and-materials billing model may reduce the revenues of firms for providing the same value to clients” under rapid automation. In short, AI’s efficiency gains can feel like a double-edged sword to firms charging by the hour.
AI’s impact on billing has even been dubbed the “AI efficiency paradox”: as software and algorithms do the heavy lifting, justifying time-based fees becomes harder. Many clients already expect their vendors to leverage AI-driven cost savings – and to share them. For instance, if an AI tool cuts research or coding time by half, customers may demand a commensurate drop in hours billed. Conversely, simply raising hourly rates is risky: clients can (and will) push back if competitors don’t follow suit. Moreover, the hourly model inherently rewards longer engagements. As RSM advises, “the hourly billing model fails to recognize the unique value” of services and “creates a key misalignment between the client and the firm”. In other words, billing by the clock gives providers no incentive to optimize or innovate, a flaw now painfully exposed by AI’s speed.
That said, AI can also sharpen the old billable-hour system. Proponents point out that automation isn’t just cutting tasks – it can streamline billing itself. AI-powered time-tracking and invoice tools can automatically capture work and expenses, reducing disputes and errors. In fact, one legal-industry analysis found that AI-enhanced billing “makes [hourly billing] more accurate, defensible, and client-friendly” by flagging forgotten time and generating detailed audits. In practice, firms may use AI to drive every remaining hour to the maximum value, partially offsetting lost time. Thus, hourly billing faces pressure and opportunity: it may survive by becoming far more efficient and transparent, even as its fundamental logic is challenged.
Beyond Hours: Exploring New Pricing Models
To adapt, IT service firms and their clients are experimenting with alternatives to pure hourly billing. A variety of models are gaining traction: fixed-price contracts, subscription or retainer fees, outcome-based arrangements, and hybrids of these. In many cases the choice depends on the work’s nature. For well-defined projects, a fixed-price (output-based) model is appealing: the provider quotes a single fee for the agreed deliverables. This gives buyers predictability and caps their budget. The flip side is risk: if an AI breakthrough lets the team finish in half the estimated time, the vendor’s profits can evaporate. As one consultancy notes, a project “that used to take six weeks… might now take three weeks… if you haven’t updated how you scope, staff, and price that work, your margin disappears fast”. Fixed-price deals demand precise scoping and often guard against scope creep, so they work best when requirements are stable.
Other firms turn to subscription, retainer or credit models for ongoing services. Clients pay a recurring fee (or buy blocks of service “credits”) for access to support, tools, or AI platforms. This aligns revenue with continuous value delivery and can include access to costly AI infrastructure. For example, a company might subscribe to managed analytics services (covering AI licenses and updates) rather than paying per hour of analysis. The advantage is steady, predictable income and a closer partnership: the vendor has a stake in the client’s long-term success. However, subscriptions risk charging for unused capacity and can be tricky to price fairly. Determining the right “credit” value for each service component requires data and trust.
A growing trend is outcome- or value-based pricing. In this model, fees are tied to agreed results or impact rather than time spent. For instance, an IT team might get paid a bonus only if they meet specified performance metrics (system uptime, speed of deployment, customer satisfaction targets, etc.). The idea is simple – “align your fee with the client’s result. If the project hits the agreed outcome, you earn more. If it doesn’t, you earn less”. Outcome pricing can build trust and drive innovation, since both sides share a goal. Industry experts report that top consultancies have long used gain-share deals in digital transformation or cost-reduction projects. In the AI era, such models are gaining momentum: advanced analytics make it feasible to guarantee things like “threats detected in 15 minutes or less” and charge accordingly. The challenge is defining clear, measurable outcomes and agreeing on how to track them. Without precise metrics (and sometimes a dash of trust), outcome deals can lead to disputes over whether goals were truly met.
Hybrid models combine elements of time, subscription, and performance pricing. For example, “risk collars” on an hourly contract might give the client a discount if delivery takes longer than planned, or a bonus to the firm if it finishes early. Another approach is to charge a base T&M fee plus a technology surcharge: keep the familiar day rate, but add a line item for AI licensing or software costs. Some providers move to flat-rate bundles – think managed IT packages at a set monthly fee – often with tiered levels of service. Others (especially software-centric outfits) may license their own AI-driven tools or templates to clients, effectively monetizing intellectual property. In all cases, the common theme is shifting away from “hours = value” toward models that account for efficiency gains and shared rewards.
Here are some of the emerging model types:
- Fixed-Price/Output-Based: One lump-sum fee for defined deliverables. Provides clients cost certainty, but puts risk on the provider if the scope is larger or the work ends up harder (for example, if an AI shortcut fails to cover a task, the vendor absorbs the extra cost).
- Subscription/Credits/Retainers: Recurring payments (monthly/annual) or purchased credits give clients ongoing access to services (often including AI tools). This smooths revenue and covers continuous support or licensing, but providers must ensure clients use what they pay for, and clients may balk at paying during slow periods.
- Outcome/Gain-Share: Fees contingent on meeting targets or creating value. Aligns incentives tightly and can command higher upside for providers. However, it requires clear, agreed-upon metrics. Without them, defining “success” can become contentious.
- Usage-Based/AI-As-A-Service: Clients pay for actual consumption (e.g. hours of AI processing, number of users, or transactions). This is more common in software but can extend to services (think “pay-per-incident” SLAs or credits for API calls). It offers flexibility—clients only pay for what they use—but revenues can fluctuate widely.
- Hybrid Packages: Any combination of the above. For example, an agile development engagement might be billed T&M up to a point, with bonus payments for hitting milestones. Or a multi-year engagement might start as fixed-price and transition to a retainer. RSM suggests creative hybrids like bundling multiple services under a single fee or adding “risk collars” on top of hourly work. These hybrids try to balance predictability and incentive but add contractual complexity.
Pros and Pitfalls of AI-Era Billing
Each new model comes with its own advantages and drawbacks in the AI context. Predictability vs. Flexibility: Fixed and subscription models give clients budget certainty and steady revenue for vendors. But they assume the work scope is well-defined. As noted above, if an AI jump cuts the needed effort in half, a fixed-price contract can suddenly become unprofitable. In contrast, T&M remains flexible (you only pay for effort expended), but costs can be hard to forecast and some clients dislike open-ended bills.
Incentives: Value-based and performance pricing align provider incentives with client outcomes. When done right, this can spur providers to innovate (for example, investing in better AI or automation because they share in the payoff). On the other hand, it places more risk on the vendor if targets are missed, and it demands reliable measurement systems. Not all work lends itself to clear metrics: how do you quantify the value of improved user satisfaction or reduced risk? Getting the math right can be tricky.
Complexity: Subscription and usage models often require new billing systems and analytics. Ironically, one benefit of AI is that it can power these billing tools. Automated trackers can log work and resource use in real time, but designing fair tiers or credit values takes effort. Hybrid contracts with collars, bonuses, and licenses are even more complex to negotiate and manage.
Despite these challenges, many firms are already embracing change. Experts advise that companies update their approach proactively. For instance, Futurice recommends refreshing project scopes, bundling tools with services, licensing AI-driven accelerators, and being transparent with clients about what AI is doing. They warn: “If you want to grow profitably in the age of AI, you’ll need to evolve both how you deliver and how you price”. Firms that simply cling to the old day-rate risk having “their margins eroding, and their value harder to defend”.
Meanwhile, some see upsides in the new models. Clients often gain greater transparency: outcome-based and subscription fees force vendors to articulate what they’re really delivering. From the provider’s view, subscription or retainer income can finance the AI tools that boost productivity. And hybrid tools like AI time-tracking make billing processes faster and reduce disputes. (Indeed, one analysis of legal billing noted that AI “addresses the issues of the hourly billing regime, providing much-needed relief from its pressures and uncertainties”.)
Crystal Ball: The Next 3–5 Years of IT Service Billing
So what will IT billing look like by 2028? Nearly all signs point to a mixed landscape. Industry observers predict that the classic billable hour will still exist, but only as one option among many. Day rates and pure T&M projects will decline in market share as subscriptions, packages, and outcome deals grow. One commentator quips that consultants should carry a “golf bag” of pricing tools rather than a single racket – using a different approach for each shot. Indeed, experts expect leading firms to offer a portfolio of models (in golf terms, choosing the right club for the situation).
In practice, we’ll likely see more hybrid agreements: for routine, AI-accelerated work, fixed or subscription pricing may become the norm, while strategic projects incorporate performance bonuses. Large enterprises (with sophisticated procurement) will push for gain-share or clear SLAs, and may demand refunds or credits if promised efficiencies don’t materialize. At the same time, many mid-market clients will stick with retainers or outcome-linked contracts that ensure continuous service.
Technologically, billing and project management platforms will become smarter. Automated tracking of AI vs. human effort will inform pricing and even dynamic adjustments. Firms may implement tiered subscription plans for AI services (e.g. standard vs. premium support), similar to how software is sold today. On the provider side, expect an increase in value-driven pricing pilots: selective projects where providers agree to “eat some risk” in exchange for a bigger share of upside.
Finally, the underlying economics of AI itself may influence pricing. If AI commoditizes a service (say, basic code review or translation), that service may shift entirely to fixed or per-word/ per-item pricing, with more skilled work commanding custom fees. Conversely, novel AI capabilities could command premium, bespoke contracts (for example, developing a custom AI model might be a fixed-price engagement).
In summary, the next 3–5 years will likely see no one-size-fits-all model. Hourly billing won’t vanish overnight, but its logic will be questioned in every contract. Providers that adapt by blending models – and transparently sharing AI-driven efficiencies – will be best positioned. As one leading firm warns, firms must act now “before [change] is forced on you”. Pricing will increasingly be viewed as a strategic lever: one that must evolve from “charging for time” to “charging for value” in an AI-powered world.
