From SaaS to Success: The Progency Proposition

Published May 19, 2025

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The recent SaaSBoomi Annual 2025 was a watershed moment that crystallised a fundamental truth: the traditional SaaS model is reaching its evolutionary endpoint. While conversations buzzed about “SaaS AI” and the opportunity to capture “salary TAM” through Agentic AI (alongside India’s domestic software market), these discussions merely scratched the surface of a more profound shift required in how software delivers value in the AI era

I asked Perplexity to summarise the key themes discussed:

  1. Transition from SaaS to AI: The event emphasised the pivotal shift from traditional SaaS to AI-powered solutions. Sessions explored how companies can integrate AI into their products and processes, reinvent business models, and address challenges posed by this transition.
  2. AI Product and Technology: A significant focus was placed on moving beyond AI hype to delve into practical engineering and product design efforts. This included frameworks for scalable architectures, data mastery, compliance, security, and personalized user experiences.
  3. Vertical AI Opportunities: The event highlighted sector-specific opportunities in industries such as manufacturing, energy, and life sciences. Founders were encouraged to collaborate with industry leaders to solve real-world problems using AI solutions/
  4. India’s SaaS Advantage: The event spotlighted India’s potential to become a global hub for SaaS and AI innovation, leveraging its talent pool and market opportunities. Discussions also covered India’s software market outlook for 2035.
  5. New World Order: A recurring theme was the emergence of a “New World Order,” where mastering AI integration with industry expertise would define successful companies of the future. This theme underscored India’s role in leading this transformation.

A post-event Rothschild report added more colour to the impact of AI on SaaS:

  • AI is fundamentally changing SaaS, making conventional subscription-based models irrelevant as AI-driven automation takes over
  • Future software will not just offer tools but will act as autonomous agents executing business processes without human intervention
  • Companies relying on feature-based SaaS offerings will struggle as AI-driven automation reshapes the landscape
  • AI-powered business applications will move from static workflows to self-learning systems capable of real-time decision-making
  • The age of static, subscription-based SaaS pricing is coming to an end as
  • AI disrupts how software is sold and monetized
  • Future AI-first solutions will shift towards usage-based, outcome-driven pricing where businesses pay for automation, intelligence, and efficiency, not just access to software

Yet these observations, while insightful, don’t address the most critical evolutionary leap SaaS must make: transforming from software vendors to success partners. This is the essence of Progency—the fusion of product, agents, and agency that reimagines how software creates value. The harsh reality is that SaaS has largely been software without service, leaving customers with powerful tools but inadequate expertise to leverage them effectively.

This fundamental disconnect has created an execution gap where brands typically utilise only 30-40% of their platform capabilities despite significant investments. SaaS companies must now add a thin service layer—initially human-led but increasingly powered by AI agents—that ensures customer outcomes rather than merely providing access to features.

By embracing performance-based pricing tied directly to business results, SaaS companies create perfect alignment with their customers: they win only when their customers win. This shift from selling inputs (features, seats, API calls) to guaranteeing outputs (revenue growth, cost savings, measurable business improvements) transforms the SaaS business model from a capped subscription revenue stream to an unlimited upside opportunity with truly “infinite” TAM.

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Previous Writings

NeoSaaS: From India First to Global Leader in Big Martech (Dec 2024): “As the world’s foremost hub of software engineering talent still awaits its first homegrown software products multinational, this series explores how a powerful convergence of Indian innovation, US market access, AI capabilities, and strategic acquisitions could finally produce that elusive global colossus – one that dominates at home while becoming a formidable player in the US and beyond. Let’s call this company “NeoSaaS”. The name signifies more than just another software venture – it represents a transformational business model built on the foundation of four interconnected pillars: strategy, software, services, and profit sharing (the 4S framework). This unique approach transcends traditional SaaS boundaries, creating a blueprint for a world-class software entity emerging from India.” I focused on the AdWaste opportunity in marketing. “While most SaaS companies focus heavily on their software offerings, merely providing great software is insufficient for creating a game-changing solution. NeoSaaS must transcend traditional SaaS limitations by embracing a comprehensive 4S framework that combines product and agency (Progency) and transforms it from a vendor into a true partner in customer success: Strategy, Software (Stack), Service (Kaizen Progency), Sharing (Profit)… This 4S framework ensures that NeoSaaS delivers comprehensive solutions rather than just tools. By combining strategic guidance, powerful software, continuous service improvement, and aligned incentives, NeoSaaS creates an ecosystem that truly enables customer success. The result is not just a product deployment but a transformative partnership that drives measurable business impact.”

SaaS Futures: Exploring New Revenues Streams (Aug 2024): In this series, I focused on new products, new markets, new geos, services, and M&A. On services, I wrote: “SaaS and Services have traditionally been viewed as fundamentally different business models, akin to chalk and cheese for most companies. The mindsets driving these two approaches are indeed quite distinct. SaaS companies typically focus on scalability, product development, and recurring revenue, while service-oriented businesses emphasise customisation, client relationships, and project-based work. The economic metrics for these models also diverge significantly. SaaS companies are often valued based on their high gross margins and the predictability of their recurring revenue, resulting in higher valuation multiples. In contrast, services businesses generally have lower gross margins due to the labour-intensive nature of their work and are typically valued at lower multiples. However, in today’s challenging business landscape, where competition is fierce and customer expectations are ever-increasing, software companies must be open to exploring new avenues for growth and customer satisfaction. This is where a thin layer of services as an add-on capability can prove invaluable.”

New SaaS: Services, AI Agents, Sharing (May 2024): I wrote about the new SaaS: Services, AI Agents, Sharing. “Services…bring in people into the product proposition to ensure continuous monitoring and improvement. This component integrates human expertise and intervention into the digital offering, enhancing the adaptability and personalisation of the software…AI Agents help automate conversations, tasks, and ‘next best action’ predictions. These autonomous, intelligent systems empower the platform by automating interactions, streamlining tasks, and providing predictive insights…Sharing (a “progency” business model) combines product and agency, to price based on performance and outcomes. It redefines the economic relationship between service providers and their customers. By adopting a performance-based pricing strategy, the focus shifts towards shared success and outcomes…The “New SaaS” can be defined as an integrated, outcome-driven ecosystem that leverages the synergistic potential of services, AI agents, and performance-based collaboration.”

Additional writings:

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3D Evolution

As I see it, SaaS needs to evolve along three axes: a thin layer of services, AI Agents, and success-based pricing. This three-dimensional transformation will fundamentally redefine the relationship between software providers and their customers.

  1. Thin Layer of Services: From Tool Provider to Success Partner

Traditional SaaS deploys powerful platforms but leaves implementation largely to customers, creating an execution gap where capabilities exceed utilisation. A thin layer of specialised services—strategically added without transforming into a full-service consultancy—bridges this critical divide.

This services layer includes:

  • Implementation specialists who understand industry-specific workflows
  • Vertical experts who translate software capabilities into business outcomes
  • Ongoing optimisation consultants focused on continuous improvement

The goal isn’t providing extensive professional services but ensuring customers extract maximum value from their software investment. This approach transforms the vendor-client relationship from “here’s your license, good luck” to “we’re invested in your operational success.”

  1. AI Agents: From Passive Tools to Active Participants

The second evolutionary axis introduces AI agents that transform software from tools that await human instructions into systems that proactively execute business processes. These agents fundamentally alter how organisations interact with software:

  • Autonomous Operations: Agents handle routine tasks without human intervention, from data analysis to workflow orchestration
  • Predictive Intelligence: Systems that anticipate needs before users articulate them
  • Continuous Learning: Capabilities that improve automatically through usage patterns
  • Cross-Functional Coordination: Multiple specialised agents working in concert to accomplish complex business objectives

This shift goes beyond automating existing processes—it fundamentally reimagines how work gets done. AI agents don’t just augment human capabilities; they create entirely new operational paradigms where systems take initiative rather than merely responding to commands.

  1. Success-Based Pricing: From Access to Outcomes

Perhaps the most revolutionary axis is the shift from subscription-based pricing (paying for access) to success-based models (paying for outcomes):

  • Performance Metrics: Compensation tied to specific KPIs relevant to the customer’s business
  • Risk-Sharing: Vendors assume partial responsibility for implementation success
  • Unlimited Upside: Both parties benefit proportionally from exceptional results
  • Value Quantification: Rigorous measurement of software’s business impact

This approach requires vendors to develop sophisticated measurement frameworks and prediction capabilities. More importantly, it demands the courage to stand behind one’s product with financial commitments.

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The combination of these three axes—services, AI agents, and success-based pricing—creates a new entity that transcends traditional SaaS – Progency. This isn’t incremental improvement but fundamental reinvention. Companies making this transition aren’t merely selling better software; they’re guaranteeing better business outcomes through an integrated approach that combines human expertise, autonomous intelligence, and aligned economic incentives.

For enterprise buyers, this evolution eliminates the frustration of underutilised software investments. For vendors, it transforms the addressable market from software budgets to business outcomes—exponentially increasing potential revenue while creating deeper, more strategic client relationships.

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What’s Changed

For several years, I’ve been exploring the Progency concept within martech—the powerful fusion of product and agency capabilities. Initially, my conversations with marketers revealed a fundamental misunderstanding: many interpreted the “agency” component as merely outsourced labour performing the same tasks as their internal teams. This approach would simply shift costs without addressing the core inefficiencies that prevent outcome-based pricing models.

My perspective transformed fundamentally a few months ago during a demonstration of Netcore’s Agentic AI system. Unlike isolated agents performing discrete tasks, I witnessed a sophisticated multi-agent ecosystem working collaboratively toward complex business objectives. This revelation crystallised a crucial insight: the “gen” in Progency shouldn’t represent generic services but rather AI agents—autonomous systems that could transform execution capabilities while dramatically reducing operational costs.

Yet this insight revealed another challenge. While organisations could theoretically deploy these AI agents themselves, the reality of effective AI utilisation remains stubbornly difficult. Even after two and a half years of widespread ChatGPT adoption, most professionals still struggle with effective prompting. As I frequently remind colleagues, crafting the right instructions to generate optimal AI outputs remains an underappreciated skill—one that prevents organisations from fully leveraging even the most sophisticated AI systems.

The integration of AI agents into the Progency model transcends the original vision of a thin services layer with performance-based pricing. While that approach would have offered incremental improvements and modest upside potential, it wouldn’t have created the scale necessary to make Progency economically transformative across diverse client portfolios. AI agents fundamentally alter this equation by enabling a previously unimaginable economic model: zero upfront platform costs coupled with compensation tied exclusively to measurable business outcomes.

This revolutionary approach draws inspiration from adtech’s remarkable trajectory over the past two decades. The advertising technology sector has grown into a $700 billion industry boasting some of the highest gross margins and profitability in the technology sector. This extraordinary success didn’t occur through traditional software licensing or agency service models—it emerged when the industry decisively shifted from input-based metrics (cost per thousand impressions, or CPM) to outcome-based models (cost per click, or CPC).

By directly tying costs to measurable business results, adtech created perfect alignment between platform capabilities and client objectives. The platforms that delivered superior outcomes thrived; those that couldn’t deliver results quickly perished. This ruthlessly efficient model drove unprecedented innovation and value creation for both providers and their clients.

For established SaaS companies, the Progency model represents both an extraordinary opportunity and an existential imperative: disrupt your own business model before newcomers inevitably do. As AI capabilities accelerate, the window for this transformation is rapidly narrowing. The first-movers who successfully implement the Progency approach—integrating specialised services, AI agent orchestration, and outcome-based economics—will establish competitive advantages that laggards simply cannot overcome.

This is more than just a pricing shift or a service enhancement; it’s a fundamental reimagining of the relationship between software providers and their customers. In the Progency future, vendors don’t sell access to capabilities—they guarantee business results, creating a virtuous cycle of continuous improvement where both parties’ interests align perfectly around measurable outcomes.

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Startup!

What should SaaS companies do to embrace this revolutionary model? This question has occupied my thoughts particularly in relation to Netcore’s Martech SaaS platform, though the principles apply broadly across the enterprise software landscape. My recommendation: create an internal startup dedicated to building a Progency offering, focusing initially on prospects where it is a struggle to gain traction. In essence, carve out a blue ocean opportunity within the increasingly commoditised red ocean of SaaS.

Incumbent SaaS companies possess three critical advantages: established clients, recurring contracts, and predictable cashflows. These should continue undisturbed while a separate, dedicated team launches a “Progency Startup” within the organisation. This new venture leverages the full capabilities of the existing platform alongside the thin managed services layer that the SaaS business has already developed. Over time, the human elements within these services will be systematically augmented and eventually replaced by AI agents, creating unprecedented economies of scale.

The fundamental differentiators for this internal startup lie in its go-to-market strategy and pricing model.

Every business exhibits a power law distribution across its customer base: approximately 20% of customers (the “Best” segment) typically generate 60-80% of revenue. The Progency Startup should strategically target the next 40%—the “Rest” customers who account for the remainder of revenue but often receive disproportionately less attention. (The final category, “Test,” encompasses dormant and churned customers requiring separate reactivation/reacquisition strategies.)

The Progency Startup’s revolutionary proposition to prospective clients is straightforward yet compelling: “Let us take ownership of your ‘Rest’ customer segment with zero platform fees. This allows your team to focus exclusively on your highest-value customers while we apply our PEAK framework—Platform, Experts, AI Agents, and Kaizen methodology—to maximise value from your underserved middle segment.”

This approach creates a win-win scenario. Clients eliminate costs associated with servicing these customers while maintaining revenue streams. Meanwhile, the Progency demonstrates its superior capabilities through a genuine skin-in-the-game approach—earning money only when it delivers revenues exceeding established baselines. The underlying thesis is powerful: Progency’s orchestrated AI agents will consistently outperform the client’s human teams in addressing the personalised needs of this long-tail customer segment.

While the “Best” customers ultimately represent the more lucrative prize, successfully demonstrating measurable results with the “Rest” segment accomplishes something far more valuable than gaining initial market entry—it earns the Progency a strategic seat at the decision-making table. Success creates an irrefutable case for expanding the model to encompass the client’s entire customer base.

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The path forward for SaaS companies is clear: reinvent themselves through the Progency model before market forces make this transition inevitable. Begin at the periphery where resistance is lowest, demonstrate compelling results through outcome-based economics, and methodically expand toward the core business. The performance-based paradigm of Progency it the destined evolution for enterprise software that will separate tomorrow’s winners from legacy providers clinging to yesterday’s business models.

The question isn’t whether this transformation will occur, but rather: which visionary companies will lead this revolution, and which will follow belatedly—if they survive at all?

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Other Industries

I asked the AIs to provide examples of success-based models from other industries.

While the evolution from SaaS to Progency draws significant inspiration from adtech’s transformation, numerous other industries have successfully implemented outcome-based economic models. These precedents not only validate the viability of success-based approaches but offer valuable blueprints for implementation within the enterprise software sector.

Healthcare’s Value-Based Revolution

Perhaps the most ambitious shift toward outcome-based economics is occurring in healthcare, where fee-for-service models are gradually giving way to value-based care arrangements. Under these frameworks, providers receive compensation based on patient outcomes rather than the volume of procedures performed.

Accountable Care Organisations (ACOs) in the US exemplify this approach—receiving financial rewards when they improve patient health metrics while simultaneously reducing overall treatment costs. This transformation fundamentally realigns healthcare provider incentives from maximising billable procedures to optimising patient health outcomes—a perfect parallel to Progency’s shift from selling software access to delivering measurable business results.

Energy Sector’s Performance Contracting

Energy Service Companies (ESCOs) have pioneered performance-based models through Energy Savings Performance Contracts. These innovative arrangements allow organisations to implement energy efficiency upgrades with zero upfront capital investment. The ESCO finances and implements the improvements, then receives compensation exclusively from the documented energy savings achieved over time.

This model has transformed energy efficiency from a capital expenditure decision to an operational improvement with guaranteed positive ROI. The parallel for SaaS is compelling: rather than requiring customers to invest in software licenses hoping for eventual returns, Progency enables organisations to implement solutions with compensation tied directly to measurable efficiency gains or revenue improvements.

Legal Services’ Contingency Approach

The legal industry has long employed outcome-based economics through contingency fee arrangements, where law firms receive payment only upon successful case outcomes. This approach fundamentally transforms the attorney-client relationship by creating perfect alignment around a common objective—winning the case.

What makes this model particularly relevant to Progency is its risk-shifting mechanism. The service provider (law firm) assumes significant upfront investment with compensation contingent on delivering specific results. This arrangement has democratised access to legal representation while ensuring lawyers are incentivised to maximise client outcomes rather than billable hours.

Manufacturing’s “Power-by-the-Hour”

Traditional industrial manufacturers have revolutionised their business models through outcome-based approaches like Rolls-Royce’s pioneering “Power-by-the-Hour” concept. Rather than selling jet engines outright, Rolls-Royce charges airlines based on engine uptime and performance. This transforms the relationship from a transactional hardware purchase to an ongoing partnership focused on operational reliability.

This approach mirrors Progency’s potential in enterprise software—shifting from selling products to guaranteeing operational performance. The customer no longer bears all the risk of implementation success; instead, the provider is directly incentivised to ensure continuous optimal performance.

Education’s Income Share Agreements

Perhaps the most innovative recent application of outcome-based economics appears in education, where coding bootcamps and alternative educational providers implement Income Share Agreements (ISAs). Students pay no upfront tuition, instead committing to share a percentage of their post-graduation income for a defined period.

This model creates extraordinary alignment between educational providers and students—schools succeed financially only when their graduates secure well-paying positions, driving relentless focus on employable skills and job placement. Similarly, Progency succeeds only when its clients achieve measurable business improvements, creating an educational incentive to continuously enhance capabilities and outcomes.

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The Common Thread: Risk Shift and Incentive Alignment

What unites these diverse examples is a fundamental reallocation of risk and realignment of incentives. In each case, service providers assume greater upfront risk in exchange for participation in the value they create. This arrangement naturally drives continuous improvement, as providers constantly seek to enhance outcomes that directly impact their compensation.

For SaaS companies considering the Progency model, these precedents demonstrate that success-based approaches can create thriving economic ecosystems across widely varying industries. More importantly, they illustrate how such models can transform client relationships from transactional vendor interactions to true strategic partnerships—precisely the evolution enterprise software needs in the age of AI and growing customer expectations.

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Critique

I then asked the AIs for a critical assessment of the Progency ideas.

Implementation Hurdles

Measurement Complexity
The success-based model hinges on accurately measuring outcomes attributable to the software. In complex business environments, establishing clear causality between software usage and business results can be exceptionally difficult. Factors beyond the software’s control—market conditions, competitor actions, internal execution—significantly impact outcomes, creating potential attribution disputes that could undermine the entire model.

Scope Definition and Baseline Establishment
Defining what constitutes “success” and establishing appropriate performance baselines presents considerable challenges. Without rigorous, mutually agreed frameworks, companies risk either setting targets too low (creating windfall profits for Progency providers) or too high (making success impossible). The initial negotiation period could become protracted and contentious.

Economic Viability Concerns

Cash Flow Challenges
The zero-upfront cost model, while attractive to clients, creates significant cash flow challenges for providers. Traditional SaaS businesses rely on predictable subscription revenue to fund ongoing operations, development, and growth. Progency providers must secure substantial capital to sustain operations during the potentially lengthy period before success metrics generate revenue—particularly problematic for smaller SaaS companies or startups.

Risk Allocation Imbalance
While sharing risk theoretically creates alignment, the Progency provider potentially assumes disproportionate risk. Factors outside their control (executive decisions, organisational changes, market shifts) can undermine performance, creating financial exposure without corresponding control. This imbalance may necessitate complex contract provisions that reintroduce the very complexity Progency aims to eliminate.

Practical Business Constraints

Scalability Limitations
The thin services layer, even augmented by AI agents, may face scalability challenges across diverse client requirements. Each client implementation requires customisation and contextual understanding that may resist full automation. As the client base grows, maintaining quality while expanding AI agent capabilities across different industries and use cases presents substantial technical and operational challenges.

Vertical Expertise Requirements
Effective implementation demands deep industry-specific knowledge that most horizontal SaaS providers lack. Building this expertise across multiple verticals requires significant investment and time, potentially limiting Progency’s applicability to specific industries or use cases in the near term.

Strategic Risks

Competitive Positioning Challenges
Progency represents a hybrid model between pure SaaS and consulting services. This creates potential competitive disadvantages against both specialised SaaS providers (with lower operational costs) and established consultancies (with deeper domain expertise). Finding the optimal positioning in this landscape may prove challenging.

Client Dependency Concerns
The success-based model could inadvertently create unhealthy dependencies on specific clients. If a disproportionate share of revenue comes from a few high-performing implementations, the Progency provider becomes vulnerable to client leverage or loss.

Organisational Resistance
Established SaaS companies with traditional subscription-based models may face significant internal resistance to adopting such a radical shift. The transition requires not just technological changes but fundamental cultural and mindset shifts across the organisation.

Technological Limitations

AI Capability Reality Check
Your model heavily relies on AI agents reaching a sophistication level that may be further away than anticipated. While AI capabilities are advancing rapidly, fully autonomous agent systems capable of replacing human expertise across complex business functions remain largely theoretical. Over-promising and under-delivering on AI capabilities could undermine client trust in the entire model.

Integration Complexity
The seamless operation of AI agents requires robust integration with client systems. Legacy infrastructure, data quality issues, and API limitations could significantly constrain effectiveness, particularly in enterprises with complex technology landscapes.

Ethical and Regulatory Considerations

AI Governance and Oversight
As autonomous AI agents take on more critical workflows, questions of accountability and liability become increasingly important. Who bears responsibility for AI errors? A comprehensive governance framework would be essential to maintain trust and transparency.

Compliance Challenges
Industry-specific regulations (GDPR, HIPAA, financial services requirements) add layers of complexity to AI agent deployment. Each vertical may require specialised compliance approaches, further complicating scalability.

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Conclusion: Promising but Nuanced

The Progency model presents a genuinely innovative vision for SaaS evolution, addressing fundamental limitations in the current subscription paradigm. However, its successful implementation will require careful navigation of the challenges outlined above.

A more measured approach might involve:

  1. Hybrid Models: Combining baseline subscription fees with performance-based upside to balance cash flow needs while maintaining outcome alignment
  2. Controlled Scaling: Starting with specific vertical markets where outcomes are more easily measured and attributed
  3. Phased AI Integration: Gradually introducing AI agents alongside human expertise rather than assuming immediate wholesale replacement
  4. Selective Client Partnerships: Initially targeting clients with modern tech stacks and collaborative mindsets to establish proof points

Published by

Rajesh Jain

An Entrepreneur based in Mumbai, India.