Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Across the consulting firms, agencies, and their clients, a consensus is forming that is more important than any individual firm’s AI strategy. It can be summarized in a single shift: the services industry is moving from selling time to selling outcomes. BCG’s research found that the “future-built” companies generating the most value from AI were spending 26 percent more on IT. They also dedicated up to 64 percent more of their IT budgets to AI than their peers. These companies expected twice the revenue increase and 40 percent greater cost reductions by 2028. Fifteen percent of their AI budgets went to agents specifically, compared to almost none for laggard companies. The implication for services firms is that their best clients, the ones with the biggest budgets and the highest expectations, are the ones most aggressively building their own AI capabilities and will be the hardest to retain on traditional T&M engagements.
WPP’s Pretorius declared 2026 “the year of agentic marketing” and described the commercial model shift explicitly: clients are buying outputs and outcomes, not people’s time. It is the shift that drives most of our conversations at Monetizely now.
Even Deloitte, typically the most measured of the Big Four in its public statements, acknowledged in its 2026 report that the gap between AI leaders and laggards is widening fast and that agentic AI is emerging as a powerful force shaping future-built companies. The firm found that improving productivity and efficiency topped the list of benefits from enterprise AI adoption, with 66 percent of organizations reporting gains. But only 34 percent were truly reimagining their businesses. The rest were using AI for incremental efficiency, not structural transformation.
This gap between efficiency and transformation is the central tension in the services industry today. Most firms are using AI to do the same work faster and cheaper. A small number are using AI to fundamentally restructure what they sell, how they deliver it, and how they price it. The firms in the first category will see margins compressed as clients demand lower prices for AI-accelerated work. The firms in the second category will see margins expand as they shift to outcome-based models where the value of the work, not the cost of producing it, determines the price.
The venture capital world has noticed this gap and is betting aggressively on the second category. General Catalyst Managing Partner Marc Bhargava has been executing what the firm calls the AI-enabled roll-up: after scanning 70 service industries, General Catalyst identified a shortlist of 10 with enough automation potential to meaningfully transform their margin profiles, and made investments in companies like Eudia (law), Crescendo (call centers), and Titan (IT services). The thesis is that established services businesses with deep domain expertise can be acquired and re-engineered around AI-native delivery, capturing the margin expansion that the transformation makes possible.
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