When teams embed AI tools and outcome-based commercial models, they activate strategic levers for adoption, retention and expansion.
Key Highlights
- AI-first services catalogs must evolve in lockstep with product, surfacing new offerings like “data validation” or “accelerated diagnostics.”
- Moving from time-and-materials to outcome-based and tiered subscription packages aligns consultant incentives with customer ROI.
- AI-powered health scoring and next-best-action engines transform customer success (CS) into a proactive expansion engine.
In today’s software market, Professional Services (PS) teams must evolve into strategic growth engines that drive adoption, retention and expansion. Companies that treat PS as a lever for software outcomes consistently outperform those focused solely on margin.
We consistently see PS positioned along a spectrum: at one extreme, firms are chasing stand-alone revenue and margin, judging success by the services P&L; at the other, companies are deploying PS strategically to drive adoption, retention and expansion, measuring success by customer satisfaction and renewal metrics. Of course, many operate between these poles, aiming to balance short-term margin goals with long-term software outcomes.
A recent Teneo survey across 300+ firms highlights the variation in services margins achieved by software organizations today. While this is partly a result of differing strategic objectives for the role services should play, it also reflects varying levels of sophistication in services offerings, monetization and delivery efficiency.
Teneo's POV
- Services as growth engine: AI can support the role of PS and CS as levers for adoption, retention and expansion.
- Evolve the catalog in lockstep with the product: As new AI products are released, evolution in the services catalog will almost certainly be required to maximize value realization. In today’s software market, Professional Services (PS) teams must evolve into strategic growth engines that drive adoption, retention and expansion.
- Price for outcomes: Moving from time-and-materials to outcome-linked or tiered subscription packages aligns incentives with customer ROI and generates predictable ARR for organizations.
- Proactive CS with AI: Leading organizations are leveraging next-best-action engines to convert usage data into churn prevention and expansion plays, strengthening net retention.
Evolving the Services Catalog for AI-first Solutions
Best Practices:
- Respond to new product capabilities: As each AI feature goes live, determine whether to create, adapt or retire service offerings so the catalog directly supports that functionality (e.g., adding a “data pipeline and validation” engagement when rolling out anomaly detection).
- Leverage AI within services delivery: Ensure effective packaging of internally developed AI enhancements. If the capability provides a true step-change in value for the customer, consider placing it in a premium services tier. If it provides only incremental value, customer expectations
While most surveyed software firms indicate some coordination between their service catalog and product roadmap. Relatively few (29%) report that the two are truly developed concurrently – a critical shortfall in a world of rapid AI-driven product evolution.
Reinventing Services Commercial Models
As AI accelerates service delivery and embeds value, time and materials (T&M) approaches not only become misaligned with the value customers receive but also risk revenue loss for vendors. By adopting models that reflect the strategic nature of AI-enabled engagement – rewarding value delivered, not just hours worked –services teams can align incentives with customer outcomes, unlock new revenue streams and ensure PS remains a strategic partner rather than a commoditized add-on.
Best Practices:
- Shift to outcome-based pricing: Price services based on impact delivered rather than delivery cost. Consider tying fees to specific customer outcomes – such as defined uplifts in adoption metrics or a reduction in onboarding time – sharing both risk and reward.
- Develop recurring, tiered service packages: Bundle services into tiered, subscription-based offerings. Align service levels (basic, premium, enterprise) with customer needs and willingness-to-pay, driving ARR growth and enhancing enterprise value through predictable, scalable revenue streams.
Transitioning to alternative commercial models can represent a material undertaking for organizations. Not all services will be appropriate for outcome or subscription-based pricing (e.g. implementation services with variable delivery timelines and cost). A phased approach – prioritizing certain services (such as consultative and advisory engagements) – is typically a pragmatic route.
Rethinking the Role of Customer Success
Companies that have implemented AI-driven churn prediction and next-best-action engines report marked improvements in preventing churn and downsell, as well as increased pipeline and execution of expansion opportunities.
Best Practices:
- AI-Powered health scoring and churn prediction: Whether building in-house or using platforms like Gainsight or Totango, deploy multi-dimensional health models – combining usage trends, support-ticket sentiment and billing anomalies – to generate early-warning scores for at-risk accounts.
- Next-best-action engines for expansion: Use AI to analyze each customer’s feature adoption profile and usage trends, then recommend personalized upsell and cross-sell plays – ensuring the CS team knows precisely when and how to approach each account.
For more guidance on how CS teams are evolving to incorporate AI capabilities into their workflows, visit Teneo’s article: How AI Offerings are Reshaping Sales and Marketing Models.
Only just over half (56%) of the surveyed firms are using Generative AI or machine learning to monitor account health and churn risk. These tools are mostly being viewed as augmentative rather than replacements for traditional approaches – most firms still rely on existing data tracking and analytics (70%) and scheduled customer feedback sessions (60%).
Common Pitfalls
- Margin mindset only: Over-emphasis on PS as a revenue line dilutes its impact on software adoption, renewals and expansion outcomes.
- Static catalog: Failing to update service offerings in line with product releases and to incorporate AI capabilities in delivery can cause service organizations to fall behind competitors and miss value capture opportunities.
- Sticking to T&M: Billing by the hour misaligns incentives once AI accelerates delivery.
- Reactive CS: Relying on manual checkpoints instead of AI-driven health insights leads to late churn interventions and missed upsell opportunities.
Conclusion
AI is transforming every stage of the customer lifecycle, from how PS offerings are defined and delivered to how CS is executed and how fees are structured. Teneo’s recent research and experience show that firms embedding AI across the Services lifecycle enjoy higher adoption rates, stronger renewal metrics and healthier overall margins.
If your service model hasn’t evolved in step with your AI and product strategy, now is the time to act. Teneo can help evaluate your current setup and chart a path toward AI-led services evolution.