When scarcity becomes abundance - value models everywhere
Steven Forth is a principle at Ibbaka and valueIQ. Connect on LinkedIn
TL ; DR (Too Long ; Didn’t Read)
Generative and agentic AI make expert value modeling accessible, fast, and cheap, eliminating old constraints and shifting value modeling from a rare, expensive task to an organization-wide, continual practice.
AI enables comprehensive competitive modeling, real-time dynamic pricing, and hyper-personalized value propositions down to individual customers.
Scenario planning and predictive modeling are dramatically expanded, allowing rapid exploration of strategic pathways and market responses.
AI-powered value models drive outcomes in product design, sales enablement, investment prioritization, and pricing strategy innovation at previously impossible scale.
New constraints emerge around data quality, AI hallucinations, transparency, skills gaps, trust and adoption, ethical fairness, governance, information overload, vendor lock-in, and regulatory compliance.
Each emergent constraint generates business opportunities for data, validation, explainability, training, change management, ethics auditing, governance, curation, portability, and compliance solutions.
Pricing consulting is disrupted: billing shifts from time-based to outcome-based; advisory services evolve toward hands-on implementation, validation, and governance roles.
Pricing optimization moves from periodic reviews to continuous, real-time adjustment and de-averaging, with intense personalization and faster decision cycles.
AI transforms SaaS and professional services, pushing vendors up the value chain and redefining platform and ecosystem business models.
Human judgment and ethical oversight remain vital, even as organizations gain strategic agility and advantage through rapid AI adoption and alignment of business strategy with proprietary data and model sophistication.
Competitive advantage centers on adaptability, vision, and trust, favoring those who rethink pricing, product, and value for a landscape of perpetually evolving constraints.
One of the main impacts of generative AI is that what was rare and expensive becomes common and easily available, dissolving some constraints while uncovering new ones.
Pricing experts have long known that pricing should begin with a value model. This is one of the key points one learns over and over again across the seven editions of The Strategy and Tactic of Pricing by Tom Nagle and many collaborators over the years. And one does not need just one value model. One also needs value models for the competitive alternatives, and different use cases may need different value models. A single solution could need a half dozen value models interacting with each other. This almost never happens.
It is too expensive
It takes too much time
It is too hard to maintain
Practically, value models have been too hard to apply. They require a lot of data and expertise to be practical.
Expertise.
That is the key word. Generative AI makes expertise widely available. One can now build an AI that makes it easy to generate a value model for any combination of use case x customer x competitor, and then apply it to practical business problems in product design, product marketing, sales and pricing.
So what happens when what was scarce becomes abundant, and we can apply a value model any time we need one?
We investigated this through a series of naive prompts that we ran through Perplexity Research. We used naive prompts as we wanted to keep this open and exploratory. The alternative would have been to develop a system of structured prompt sequences with feedback loops and quality rubrics.
The approach we are taking here is informed by the ideas of Sangeet Paul Choudary in his book Reshuffle. In this book, he proposes that AI will reshuffle work, organizations, and value creation by first eliminating existing constraints and surfacing emerging constraints (we applied the same approach in How will AI change pricing roles).
Prompt 1: What happens when constraints on value model building, maintenance, and application are removed?
Value models of the EVE (Economic Value Estimation) style are used to structure the design of pricing models, to inform value-based selling, and to guide product design and product investment decisions.
Building, maintaining, and applying value models has been expensive, as a great deal of human labour is involved. This has limited their use.
For example, a best practice is to build a value model for each competitive alternative and to compare them for different customers and use cases in order to understand the competitive positioning of each solution. In practice, this is seldom done. This is just one of many examples.
Generative AI makes it much faster and cheaper to build, maintain, and apply value models. (Substantiate this claim.)
What constraints does this remove, and what new possibilities does this open up for the use of value models?
New Possibilities Opened by AI-Enabled Value Models
1. Comprehensive Competitive Modeling
Organizations can now realistically implement the best practice of building value models for every competitive alternative:
Automated tracking of all major competitors' offerings, pricing, and positioning
Continuous updates to competitive reference values as market prices shift
Multi-dimensional competitor profiling beyond just price—including technology, partnerships, and strategic direction
Predictive modeling of competitor responses to pricing or product changes
2. Dynamic, Real-Time Value Models
Moving beyond static quarterly models to responsive systems:
Prices and value calculations that adjust in real-time based on market conditions
"Generative pricing" models that adapt as customer configurations change
Continuous value quantification as customer usage patterns evolve
Integration with CRM and product systems for live value tracking
3. Hyper-Personalized Value Propositions
AI enables value modeling at unprecedented granularity:
Individual customer-level value models rather than segment averages
Personalized value stories generated automatically for each prospect
Customized pricing based on specific customer operations and value drivers
Predictive modeling of which value drivers matter most to each customer
4. Expanded Scenario Planning
Organizations can explore many more strategic possibilities:
Generating dozens of market scenarios with varied assumptions
Testing product positioning across multiple customer segments simultaneously
Simulating competitive dynamics and market responses
Year-by-year narrative development for different strategic pathways
5. Enhanced Value Communication and Sales Enablement
AI transforms how value models support sales processes:
Automated generation of value-based sales collateral customized for each opportunity
Real-time value calculators that sales can configure during customer conversations
AI-powered chatbots that can articulate value propositions in natural language
Dynamic ROI presentations that update based on customer-provided inputs
6. Product Development and Investment Optimization
Value models can now actively guide product strategy:
Identifying "white space" opportunities where customer needs are unmet
Quantifying the value potential of proposed features before development
Prioritizing product investments based on value creation potential across segments
Continuous feedback loops between market value realization and product roadmaps
7. Pricing Strategy Innovation
New pricing approaches become feasible:
Outcome-based pricing tied to realized customer value
Hybrid models combining consumption metrics with value delivery
Automated price optimization across product portfolios
Dynamic promotional strategies optimized for segment-specific value perception
8. Cross-Functional Value Management
Breaking down organizational silos:
Shared value models accessible across pricing, sales, product, and marketing teams
Consistent value narratives from product development through customer success
Real-time dashboards showing value creation and capture across the business
Integration of value models into strategic planning and resource allocation
9. Predictive Value Insights
Moving from reactive to anticipatory value management:
Forecasting how value perceptions will evolve as markets mature
Predicting which customer segments will derive the greatest value from new capabilities
Identifying early signals that value propositions need refreshing
Anticipating competitive value erosion before it impacts win rates
10. Democratization of Value-Based Strategy
Making sophisticated value modeling accessible to more organizations:
Small and mid-size businesses can leverage AI tools previously available only to enterprises
Business units can develop their own value models without central analytics teams
Faster iteration cycles enable experimentation and learning
Lower barriers to entry for value-based pricing and selling adoption
Strategic Implications
The transformation of EVE model development through generative AI represents a shift from value modeling as a specialized, periodic exercise to value management as a continuous, organization-wide capability. This removes the fundamental trade-off between comprehensiveness and practicality that has limited EVE adoption.
Organizations can now pursue strategies that were previously impractical:
Building value models for every competitive scenario rather than just primary competitors[query]
Refreshing models continuously rather than quarterly or annually
Personalizing value propositions at scale rather than using segment averages
Testing multiple pricing strategies in simulation before market implementation
Aligning product development closely with value creation opportunities
The key insight is that AI doesn't just make existing processes faster—it enables qualitatively different approaches to value modeling that change what's strategically possible. The constraint removal is not incremental but transformational, opening possibilities for value-based strategy execution that simply weren't feasible in the pre-AI era.
Human expertise remains essential for defining strategic objectives, validating AI outputs, interpreting nuanced market dynamics, and making judgment calls on value positioning. However, AI handles the heavy analytical lifting, allowing professionals to focus on strategic value creation rather than model maintenance. This represents an evolution from pricing as art informed by limited analysis to pricing as science enabled by comprehensive, real-time value intelligence.
Prompt 2: Emergent Constraints
Each time one set of constraints is removed a new set of constraints emerges.
Solving for these emergent constraints can be where the most innovative solutions emerge and where the most value is created.
Given the constraints around value models that AI removes, what new constraints are likely to emerge?
How will these new constraints create new business opportunities?
What impact will this have on how pricing consulting, pricing design and optimization, value communication and sales and product design are carried out?
What wider changes could result from the emerging constraints?
See the full response here.
1. Data Quality and Integrity Constraints
The Nature of the Constraint
AI value models are only as reliable as the data feeding them. While AI removes time constraints, it introduces acute data quality dependencies. According to research, 85% of AI initiatives may fail due to poor data quality and inadequate volume. The "garbage in, garbage out" principle becomes amplified when AI processes data at scale.
Data quality issues include accuracy problems, inconsistencies across systems, completeness gaps, timeliness challenges, and relevance mismatches. Additionally, data poisoning—where malicious or misleading information enters datasets—and synthetic data feedback loops that degrade model quality over time present new risks.
Business Opportunities Created
This constraint opens substantial opportunities for data infrastructure and governance services. Organizations need data cleansing platforms that prepare value model inputs, data provenance systems that track lineage and quality across the organization, integration services connecting siloed data sources for unified value models, continuous data quality monitoring and validation tools, and specialized data enrichment services that augment internal data with verified external sources.
2. AI Hallucination and Accuracy Constraints
The Nature of the Constraint
AI hallucinations—when models generate plausible but false information—pose severe risks for value models that inform pricing decisions. OpenAI's own tests show their newest models hallucinate 30-50% of the time. In financial contexts, AI chatbots frequently hallucinate when handling financial tasks like explaining concepts or retrieving data.
For value models, hallucinations could manifest as fabricated competitive pricing data, invented market trends, non-existent customer segments, or fictional value driver relationships. These errors can appear highly convincing, making detection difficult.
Business Opportunities Created
This constraint creates demand for AI validation and verification services, including human-in-the-loop validation systems ensuring critical value model outputs receive expert review before use, retrieval-augmented generation (RAG) implementations that ground AI outputs in verified business data, hallucination detection tools using specialized algorithms to flag suspicious outputs, small language models trained on domain-specific financial and pricing data rather than general web content, and audit trail systems that track AI decision provenance for accountability.
3. Interpretability and Transparency Constraints
The Nature of the Constraint
As value models become AI-driven, they risk becoming "black boxes" where stakeholders cannot understand how pricing recommendations are generated. This opacity creates problems for building stakeholder trust, regulatory compliance requirements, business justification of pricing decisions to customers and sales teams, and debugging when models produce unexpected results.
The complexity of AI models makes accountability and transparency hard to enforce, complicating governance efforts. When customers or internal teams cannot understand pricing logic, trust erodes.
Business Opportunities Created
This constraint drives demand for explainability solutions, creating opportunities for explainable AI (XAI) platforms that provide clear explanations for pricing decisions, visualization tools that make AI decision-making processes accessible to non-technical stakeholders, post-hoc interpretation methods like LIME and SHAP that reveal model reasoning, governance frameworks establishing clear accountability for AI-driven pricing, and training programs teaching pricing teams to interpret and validate AI recommendations.
4. Skills Gap and Organizational Capability Constraints
The Nature of the Constraint
AI democratizes value modeling capabilities but simultaneously creates acute skills gaps. Organizations face a "half-life of AI skills" of approximately three to four months—knowledge becomes outdated rapidly. According to research, 67% of executives identify lack of AI expertise as a top challenge to implementation. Within the technology sector itself, 95% of leaders struggle to find skilled talent with AI capabilities.
The skills required span technical AI literacy, data interpretation abilities, strategic business judgment, change management capabilities, and ethical AI governance understanding. Traditional pricing professionals must evolve from manual operators to "decision leaders" who validate AI suggestions.
Business Opportunities Created
This constraint creates vast opportunities in the learning and capability-building space. Organizations need AI literacy training programs tailored for pricing professionals, certification programs in AI-enabled value modeling, AI change management consulting helping organizations navigate adoption, role-based learning pathways (business versus technical), mentorship programs pairing AI-experienced professionals with traditional pricing experts, and continuous learning platforms keeping skills current as AI evolves.
5. Trust and Adoption Constraints
The Nature of the Constraint
Strategic automation faces significant organizational resistance from pricing teams concerned about job security or skeptical of AI-driven recommendations. Research indicates that AI disclosure can actually harm social perceptions and erode trust in some contexts. Employees may fear automation, experience uncertainty about future roles, and resist changes to established workflows.
Successful AI adoption requires addressing the human factors—awareness, desire, knowledge, ability, and reinforcement—through structured change management. Without this, AI implementations face delays, underutilization, and eventual abandonment.
Business Opportunities Created
This constraint creates opportunities for AI adoption enablement services, including change management consulting focused specifically on AI pricing tool adoption, stakeholder engagement frameworks ensuring pricing teams are involved from project inception, communication strategies articulating how AI augments rather than replaces human expertise, pilot program design demonstrating quick wins to build organizational confidence, and resistance mitigation services addressing fears transparently.
6. Ethical and Fairness Constraints
The Nature of the Constraint
AI-driven pricing can inadvertently lead to price discrimination based on protected characteristics or perpetuate historical biases embedded in training data. Concerns include unfair targeting of vulnerable populations, algorithmic bias amplifying social inequalities, opaque decision-making that customers perceive as unfair, privacy violations through extensive data collection, and regulatory compliance challenges under evolving fairness standards.
The FTC and ACCC have signaled increased scrutiny of potentially discriminatory algorithmic pricing. Australia's consumer protection framework is being reviewed specifically to address unfair practices like dynamic pricing.
Business Opportunities Created
This constraint opens opportunities for ethical AI services, creating demand for bias detection and mitigation tools conducting regular algorithm audits, fairness testing frameworks using standardized metrics to assess discriminatory outcomes, diverse dataset curation services ensuring training data represents all customer segments fairly, ethics advisory boards providing external perspectives on pricing model design, regulatory compliance consulting navigating evolving AI pricing regulations, and transparent pricing communication strategies that explain algorithmic pricing to customers.
7. Governance and Oversight Constraints
The Nature of the Constraint
AI agents capable of autonomous pricing decisions introduce new governance challenges—heightened cybersecurity threats, potential for unauthorized pricing changes, legal liability when AI makes binding commitments, and control challenges as systems act independently. The rapid pace of AI development often outpaces regulatory frameworks, creating governance gaps.
Organizations struggle with implementing data governance frameworks that effectively address AI-specific quality issues, managing siloed data that AI needs integrated, maintaining consistency across AI-driven processes, and establishing accountability when AI makes errors.
Business Opportunities Created
This constraint drives demand for AI governance platforms and services, creating opportunities for centralized AI governance dashboards providing visibility into all pricing models and their performance, policy development services establishing clear rules for AI pricing model deployment, risk monitoring systems tracking model drift and silent failures in real-time, compliance automation tools ensuring AI pricing adheres to regulatory requirements, and governance frameworks defining clear roles, responsibilities, and escalation paths for AI decisions.
8. Information Overload and Decision Paralysis Constraints
The Nature of the Constraint
Paradoxically, AI's ability to generate unlimited scenarios and analyses can overwhelm decision-makers. Research shows that 40% of executives report feeling highly burdened by information, making them 7.4 times more likely to experience decision regret and 2.6 times more likely to avoid making decisions altogether.
In pricing contexts, the ability to model thousands of scenarios can lead to analysis paralysis where teams continuously analyze rather than act. Real-time data streams can create "data overload" where filtering signal from noise becomes the challenge.
Business Opportunities Created
This constraint creates demand for decision support and information curation services, including decision intelligence platforms that highlight the most critical insights from AI outputs, scenario filtering tools that identify the most strategically relevant modeling options, executive dashboards distilling complex AI analysis into actionable recommendations, automated prioritization systems that rank pricing opportunities by impact, and decision workflow tools that guide teams through structured decision processes despite information abundance.
9. Vendor Lock-In and Technology Dependence Constraints
The Nature of the Constraint
As organizations adopt AI-powered value modeling platforms, they risk becoming dependent on specific vendors whose systems use proprietary technologies. AI vendor lock-in manifests through pricing volatility (43% of vendors mixing subscription with usage-based pricing, changing rates more than once every 30 days), egress fees that make data migration expensive, integration complexity slowing team productivity, and limited portability across platforms.
This dependence can divert resources away from innovation toward infrastructure management. Organizations find their AI roadmaps tied to vendor agendas rather than their own strategic priorities.
Business Opportunities Created
This constraint creates opportunities for vendor-agnostic solutions and services, including platform integration services that connect multiple AI pricing tools into unified workflows, data portability solutions enabling organizations to switch providers without losing historical value models, vendor evaluation frameworks helping organizations assess lock-in risks before adoption, multi-cloud strategies reducing dependence on single hyperscalers, and open-source value modeling frameworks providing alternatives to proprietary platforms.
10. Regulatory and Compliance Constraints
The Nature of the Constraint
Existing competition law and consumer protection frameworks struggle to address novel issues raised by algorithmic pricing. Regulators question whether AI-driven pricing enables tacit collusion between competitors, even without explicit human agreements. The "black box" nature of AI algorithms makes regulatory oversight difficult.
Transparency mandates are emerging—the proposed U.S. Preventing Algorithmic Collusion Act creates presumption of illegal agreement when competitors share pricing algorithm data. The EU's AI Act grants competition authorities new powers to access company information relating to AI systems. Australia's ACCC has made algorithmic transparency a 2025 priority.
Business Opportunities Created
This constraint opens opportunities for regulatory compliance services, including algorithmic transparency tools enabling regulators to test AI pricing in "regulatory sandboxes", compliance automation platforms ensuring pricing models meet evolving regulatory standards, regulatory monitoring services tracking new AI pricing regulations across jurisdictions, disclosure management systems helping organizations explain AI pricing to regulators, and policy advocacy services helping industry shape reasonable AI pricing regulations.
Transformation of Pricing Consulting
From Hours to Outcomes
The consulting industry faces fundamental pricing model disruption. AI cuts the time required for billable tasks while adding depth and accuracy, making traditional hourly billing untenable. According to Simon-Kucher, at least 20% of professional services firms will fundamentally change their revenue model in the next five years due to AI impact.
The shift moves toward deliverable-based pricing focused on workshops, training programs, measurable productivity enhancements, and knowledge transfer—not hours spent. Value-based pricing tied to measurable business outcomes (10-40% of cost savings or revenue increases) is becoming dominant, with 73% of consulting clients preferring outcome-tied models.
New Advisory Service Models
Consulting is evolving from strategy advisor to hands-on implementer. AI facilitates advisory roles focused on building client AI capabilities, validating AI outputs through expert judgment, establishing governance frameworks, navigating ethical considerations, and managing organizational change.
The most valuable consultants now bridge technical understanding with business transformation experience—a combination commanding 40-60% premiums over purely technical or strategic consultants. Executives plan to increase overall consulting spend, with 75% expecting AI to have positive impact on their use of consulting.
Pricing Design and Optimization Transformation
From Periodic to Continuous
Pricing design shifts from periodic strategic reviews (once or twice annually) to continuous optimization. AI enables "read and react" processes that monitor performance against strategy frequently, identify deviations, and implement corrective changes in near real-time.
The pricing team's role evolves from manual operators to strategic decision leaders. They set strategy collaboratively with merchants, use AI tools to optimize within that strategy, and provide final review before execution. Human oversight remains essential—particularly when brand perception or long-term goals are at stake.
De-Averaging and Hyper-Personalization
AI enables optimization at unprecedented granularity. Rather than average prices across assortments, retailers can de-average to invest in categories driving store traffic and key value items (KVIs) influencing perception. One large U.S. retailer made targeted KVI price reductions that contributed to 10% improvement in customer value perception, driving long-term market share.
Personalization extends to individual customer level, with AI analyzing usage patterns and behaviors to create value-aligned pricing structures. This moves beyond segment-level pricing to truly individualized value models.
Platform and Ecosystem Models
AI enables platform business models where companies facilitate transactions between buyers and sellers rather than selling products directly. Dynamic matching algorithms pair parties based on value preferences in real-time. This fundamentally changes pricing from product-based to service-based or outcome-based models.
Value Communication and Sales Transformation
From Static to Dynamic Value Stories
Sales teams gain access to real-time value calculators configured during customer conversations. AI generates customized value-based sales collateral for each opportunity automatically. Chatbots can articulate value propositions in natural language, answering customer questions about ROI interactively.
This shifts value communication from static presentations to dynamic, personalized dialogues. Sales professionals become value story architects rather than presenters, guiding AI to generate relevant scenarios for each stakeholder.
Predictive Value Insights
AI enables sales teams to forecast which value drivers matter most to specific customers before conversations begin. Predictive modeling identifies customers most likely to appreciate particular value propositions, enabling targeted outreach. This transforms sales from reactive (presenting value when asked) to proactive (leading with value insights).
Trust-Building Through Transparency
The black-box problem creates opportunities for sales professionals who can explain AI-driven pricing transparently. Those who help customers understand how AI determines value recommendations build differentiated trust. Sales becomes partially about educating customers on AI pricing methodologies.
Product Design and Investment Transformation
Value-Driven Product Roadmaps
AI value models identify "white space" opportunities where customer needs remain unmet. By continuously tracking value realization versus expectations across customer segments, product teams get real-time feedback on feature performance.
Quantifying the value potential of proposed features before development becomes standard practice. Product investments are prioritized based on value creation potential rather than engineering feasibility or competitive feature parity.
Continuous Value Validation
Product teams establish feedback loops between market value realization and development priorities. As customers use products, AI tracks which features deliver expected value and which disappoint. This informs both pricing (repricing underperforming features) and development (enhancing or sunsetting features).
The cadence accelerates from annual product planning to continuous adjustment based on value signals.
Outcome-Based Product Models
AI enables product-as-a-service models with continuous updates based on value insights. Rather than selling one-time products, companies offer subscriptions where AI ensures customers always access latest features and optimizations.
Pricing shifts from upfront purchases to consumption-based or outcome-based models where customers pay based on value realized rather than features accessed.
Organizational Structure Transformation
Centralized Pricing Centers of Excellence
Best-in-class organizations establish centralized pricing teams or centers of excellence spanning categories, regions, and channels. These teams possess strategic insight and data science capabilities to manage AI-powered pricing engines. They typically sit within merchandising to align with commercial levers, though some organizations place them in marketing, IT, or data science.
This centralization consolidates expertise, governance, and technology—addressing the skills gap and governance constraints simultaneously.
Competitive Dynamics Shift
Speed as Competitive Advantage
Competition increasingly centers on decision velocity rather than analytical depth. Organizations using dynamic AI decisioning grow faster and more profitably than those reacting to historical trends. The strategic win is being consistently faster than the competition—turning pricing intelligence into true competitive advantage.
This creates "winner-take-most" dynamics where early AI adopters pull ahead while laggards struggle to catch up.
Commoditization of Standard Value Models
As AI democratizes basic value modeling, standard approaches become commoditized. Competitive advantage shifts to proprietary data, unique AI model architectures, specialized domain expertise, and superior change management capabilities.
Organizations differentiate through six areas: data advantage, algorithmic sophistication, implementation speed, organizational adaptability, ethical AI practices, and ecosystem partnerships.
Market Structure Evolution
Unbundling and Rebundling
AI enables unbundling of previously integrated offerings, with granular pricing for individual components. Simultaneously, AI facilitates dynamic rebundling—creating customized bundles for each customer based on predicted value.
This creates more fluid market structures where product boundaries blur and reconfigure continuously based on value signals.
New Intermediaries and Platforms
Emergent constraints create space for new intermediaries addressing data quality, interpretability, ethical auditing, and governance gaps. Platform businesses emerge connecting value model builders, data providers, validation services, and end-users in ecosystems.
These platforms reduce friction from multiple constraints simultaneously—providing vetted data, pre-built interpretability, compliance automation, and change management support.
Industry Convergence and Disruption
SaaS and Agentic AI Disruption
Generative and agentic AI disrupts software-as-a-service by automating tasks that SaaS products currently perform. This forces SaaS companies to move up the value chain to more strategic, judgment-intensive services that AI cannot easily replicate.
Value modeling itself may become embedded in broader AI agent platforms rather than standalone software categories.
Professional Services Transformation
Consulting, legal, accounting, and financial advisory services face disruption as AI handles analytical components. These industries must reinvent around judgment, relationship management, regulatory navigation, and implementation support—areas where human expertise remains superior.
The emergent constraints (trust, ethics, governance) actually protect human professional services from full automation while changing what services clients purchase.
Regulatory and Policy Evolution
Anticipatory vs. Reactive Governance
The pacing problem—rapid technological development outpacing legislative action—drives calls for anticipatory rather than reactive regulation. Regulatory sandboxes where AI pricing algorithms can be tested under controlled conditions emerge as governance mechanisms.
International cooperation becomes essential as AI pricing systems operate globally, requiring coherent cross-border regulatory approaches.
Stakeholder Participation Expansion
The limited public engagement in AI development drives demands for democratic governance involving affected communities beyond technical experts. Ethical advisory boards, diverse testing panels, and public comment periods become standard in AI pricing model development.
This addresses both the fairness constraint and builds legitimacy for AI-driven pricing.
Conclusions
The transition from scarcity to abundance in value modeling, driven by generative and agentic AI, marks a profound transformation for B2B SaaS, business AI, and pricing professionals. What was previously expensive, time-consuming, and the domain of experts is now accessible, dynamic, and democratized—unleashing a wave of innovation and strategic possibility. Yet, this abundance does not erase constraints; it reshuffles them, introducing new demands for data quality, model accuracy, interpretability, skills development, adoption management, ethical oversight, governance, and regulatory compliance.
For executives and pricing experts, the imperative is clear: embrace AI-enabled value models to move beyond periodic analysis towards real-time, organization-wide value management. Success will depend not just on technology adoption but on building robust data infrastructure, fostering continuous learning, ensuring transparent and fair pricing practices, and developing agile governance systems. Competitive advantage will increasingly favor organizations that align strategic objectives with AI capabilities—leveraging proprietary data, model sophistication, rapid implementation, and partnerships across the emerging value modeling ecosystem.
In this new landscape, human judgment, creativity, and ethical stewardship remain indispensable, guiding AI’s analytical power toward sustainable, customer-centric outcomes. The winners will not be those who automate the past, but those who reimagine pricing, product, and value creation for a world where constraints are continuously evolving and where adaptability, trust, and vision set the pace for success.
Navigating the new pricing environment brought by AI agents? Contact us @ info@ibbaka.com
