Every year leading analyst firm Gartner produces hype cycle reports about emerging technologies across different industries. In the Gartner Hype Cycle™ for Emerging Technologies in the Communications Industry, 2026* Qvantel is named as a Sample Vendor in the CSP Algorithmic Pricing category.
While algorithmic pricing is viewed as an emerging technology in telecoms it has been used in other industries for years. Airlines have used dynamic fare management for decades but now AI has improved it. Algorithms set fares across thousands of route/date combinations based on booking pace, remaining seat inventory, and competitor pricing. AI models continuously recalibrate the demand curves and elasticity assumptions the algorithms operate on, improving yield per flight over time. Also ride-hailing services use algorithmic pricing and continuously adjust ride prices based on real-time supply and demand signals. AI refines the underlying model over time by learning how demand patterns vary by location, time, weather, and local events, so the algorithm becomes more accurate at predicting when and where to adjust prices, and by how much.
What Does AI-Driven Algorithmic Pricing Mean for CSPs?
Pricing in telecoms has traditionally been a deliberate, rules-based process. Tariffs are designed in advance, encoded into product catalogs, and applied systematically according to defined logic such as segment, usage tier, contract term, and so on. Algorithmic pricing formalises and automates that logic. It executes pricing decisions based on rules that humans define upfront. The algorithm applies those rules consistently and at scale, but the underlying logic is static until someone changes it. This works well when the relevant variables are stable and well understood but becomes a constraint in more complex or fast-moving environments.
What is changing now is that AI is being applied to make the algorithms themselves smarter. It is refining the rules, adjusting parameters, and adapting pricing logic in response to data rather than requiring human intervention each time conditions change.
AI-driven algorithmic pricing introduces machine learning into the algorithm design and maintenance process. Rather than requiring pricing teams to manually identify the right rules and thresholds, AI models analyse historical and real-time data to surface patterns, test pricing hypotheses, and update algorithm parameters over time. The algorithm still executes pricing decisions in a structured, auditable way but the logic it operates on is continuously refined by AI rather than remaining fixed between review cycles.
In a telecoms context, this might mean an AI model identifying that a particular customer segment responds differently to bundle pricing than previously assumed and adjusting the relevant discount thresholds accordingly. Or a model detecting that demand elasticity for roaming add-ons varies significantly by destination and travel frequency, and updating the pricing rules to reflect that granularity. The algorithm executes the decision, and the AI keeps the algorithm calibrated.
Why This Matters for CSPs
The commercial environment CSPs operate in creates conditions where static pricing algorithms degrade in effectiveness over time. Customer behaviour shifts, competitive positioning changes, and new product configurations introduce variables that weren't present when the original pricing logic was designed. Without a mechanism to update the algorithms systematically, pricing teams either over-engineer rules upfront in an attempt to anticipate every scenario or accept that the logic will drift out of alignment with market reality.
AI-driven algorithmic pricing addresses this by treating the algorithm as something that evolves rather than something that is set and monitored. The practical benefits for CSPs include more accurate targeting of retention and acquisition offers, better calibration of bundle and add-on pricing across customer segments, and a reduced operational burden on pricing teams who would otherwise manage that calibration manually.
Customer expectations are also driving this shift. Personalisation, powered by AI-generated insight into customer behaviour and preferences, is increasingly something customers expect and CSPs that cannot offer it risk losing ground to competitors that can. This pressure is not confined to the direct channel. Resellers and distribution partners face the same competitive dynamics, which makes AI-driven algorithmic pricing equally relevant across indirect channels. Channel-specific pricing is becoming a higher priority as CSPs look to tailor offers by partner and route to market, and this increasingly extends to bundling telecom services with non-telecom goods, where the combination is priced and resolved as a single bundle rather than as separate line items.
As with any AI application, the quality of outputs depends on the quality of inputs. Data consistency, feature engineering, and model governance are all prerequisites for this approach to function reliably. The AI component also introduces a layer of explainability that needs to be managed and this can help build customer trust.
What BSS Needs to Support AI-Driven Algorithmic Pricing
For AI-driven algorithmic pricing to work in practice, the BSS needs to do two things well. First, it needs to support flexible, granular pricing logic that can be updated frequently as AI models refine algorithm parameters. Second, it needs to integrate with the AI and data platforms where that refinement happens, so that updated logic flows through to execution without manual rework.
The product catalog and rating engine need to support condition-based pricing rules at a fine-grained level, allowing algorithm parameters, such as discount thresholds, eligibility conditions, bundle configurations, intent based price logic etc to be adjusted without structural changes to the underlying catalog. This makes it practical to update pricing logic at the frequency that AI-driven refinement produces, rather than being constrained by the cost of catalog changes.
Real-time AI-driven monetization with advanced algorithmic pricing automation capabilities provide the execution engine needed to act on intent-driven refined pricing logic at the customer level. This can involve adjusting an in-flight session, modifying an event / segment-level discount, or updating the parameters of advanced bundles (cross discount, multi benefit rewards, intent-driven loyalty, adjustments on personalised trends, etc). For CSPs evaluating AI-driven algorithmic pricing, the BSS architecture is a foundational consideration as the intelligence applied to the algorithm is only as useful as the system's ability to operationalise it.
Open APIs and MCP enables integration with external AI and machine learning tools, allowing model outputs to feed directly into pricing configuration and offer management workflows. This closes the loop between the AI layer that refines the algorithms and the BSS layer that executes them.
Having these systems in place can turn AI-driven algorithmic pricing from an emerging technology into a capability that CSPs use every day to deliver a more personalised experience and increase revenues.
*Gartner Report, Hype Cycle for Emerging Technologies in the Communications Industry, 2026, By Peter Kjeldsen, June 2026.
Bernhard Kraft
Head of Product Management, Qvantel