Bridging employee and customer personalization into a shared data-model-governance substrate creates mutually reinforcing gains — improving employee experience raises customer engagement and vice versa — and algorithmic transparency increases this payoff while privacy constraints reduce it.

Main Finding

AI personalization for employees and for customers is converging onto a common substrate of data, models and governance. This convergence — not merely having strong personalization capability on each side — is the stronger proximal driver of dual‑sided value: improved employee experience and increased customer engagement reinforce one another (reciprocal spillover) and accumulate into a firm‑level property the paper terms personalization convergence. Algorithmic transparency amplifies these positive spillovers while privacy concerns attenuate them.

Key Points

• Thesis: The historical separation of employee‑side and customer‑side personalization is organizational, not technological; both estates are moving toward a unified substrate.
• Convergence Model: AI personalization capability on the work side and market side jointly shape employee experience and customer engagement; these feed into personalization convergence and then into dual‑sided value.
• Reciprocal spillover: Improvements on one side (e.g., better employee personalization) positively affect the other side (e.g., customer engagement), and vice versa.
• Boundary conditions: Algorithmic transparency strengthens the convergence → value pathway; privacy concerns weaken it.
• Convergence > Capability: Aggregate convergence (integrated data, models, governance) is a stronger immediate predictor of firm value than raw, siloed personalization capability.
• Practical contributions: the paper provides (a) a formal definition and measurement scheme for personalization convergence, (b) a three‑layer reference architecture (data, models, governance), (c) a five‑stage maturity model (progression from siloed estates to full convergence), and (d) a governance agenda treating employees and customers as a single population of data subjects.

Data & Methods

• Literature synthesis: Structured review of 118 peer‑reviewed studies published 2015–2025, integrating theories including the service–profit chain, service‑dominant logic, sociotechnical systems theory, and technology affordance perspective.
• Empirical illustration: An illustrative demonstration using a calibrated simulated dataset (n = 612) to evaluate the Convergence Model and boundary conditions.
• Measurement: The paper develops a formal measurement scheme for personalization convergence (operationalizing how employee‑side and customer‑side personalization coalesce).
• Limitations noted: the empirical demonstration is illustrative/simulated rather than large‑scale field evidence; synthesis may reflect publication bias and heterogeneity in study designs.

Implications for AI Economics

• Investment strategy: Firms should prioritize integrating data, models and governance across employee and customer personalization (convergence) rather than investing only in isolated capability upgrades.
• Value measurement: Personalization convergence is a measurable firm‑level construct that can be used to estimate returns to AI investments beyond conventional single‑side metrics (productivity, sales).
• Labor and product markets: Employee experience improvements driven by integrated personalization can raise customer engagement and thus firm revenues — linking AI investments in HR/operations to market outcomes.
• Governance and regulation: Treating employees and customers as one population of data subjects calls for unified privacy, consent and transparency policies; algorithmic transparency is economically valuable because it strengthens convergence effects.
• Policy tradeoffs: Privacy protections reduce spillover magnitude; regulators and firms face a tradeoff between consumer/worker privacy and the aggregate economic gains from convergence — suggesting demand for privacy‑preserving integration techniques (e.g., differential privacy, federated learning).
• Research agenda: Empirical validation with firm‑level panel data, quantification of the convergence metric’s contribution to profits, modeling of equilibrium effects on labor demand and pricing, and cost‑benefit analyses of governance choices (transparency vs. privacy) are high‑priority next steps.