AI Decisioning

AI Decisioning is Hightouch’s term for learning-based marketing personalisation where AI agents decide which experience to deliver to each customer, optimising toward defined business goals within explicit guardrails.

Key points

• Hightouch defines AI Decisioning as a new class of marketing technology for 1:1 personalisation, replacing static segments and journeys with agents that continuously learn which experiences drive the best outcomes for each customer ^[src-023].
• The marketer’s role shifts from drawing every journey branch to defining goals, decision dimensions, inputs, and guardrails ^[src-023].
• Decision dimensions can include subject line, content modules, channel, send time, day of week, communication frequency, and other experience variables ^[src-023].
• The technical foundation combines reinforcement learning, Multi-Armed Bandits, and Contextual Bandits. Reinforcement learning connects actions to outcomes, bandits balance exploration and exploitation, and contextual bandits incorporate customer data into the choice ^[src-023].
• The key operating claim is that “right” content/channel/timing cannot be known in advance; it must be learned from customer engagement and non-engagement over time ^[src-023].
• Hightouch’s second article expands the reinforcement-learning loop: an agent selects an action, interacts with the customer environment, receives a positive or negative response, then chooses the next action based on experience and desired outcomes ^[src-024].
• In marketing terms, actions can include message/no message, channel, timing, cross-sell offer, message type, and frequency; rewards can include opens, visits, cart additions, purchases, retention, or lifetime value ^[src-024].
• The third article identifies multi-armed bandits as the automated experimentation engine inside AI Decisioning: they shift resources to winning actions while still exploring new possibilities across marketing dimensions ^[src-025].
• The fourth article positions contextual bandits as the final 1:1 personalisation layer: they combine bandit exploration/exploitation with individual customer data so AI Decisioning can choose what works for this person, right now ^[src-026].
• Hightouch says its production version uses multiple contextual bandits optimised for different business outcomes and coordinates messages across campaigns ^[src-026].
• Braze uses the phrase AI Decisioning Studio for a broader optimisation architecture where Intelligent Selection and multi-armed bandit logic handle live allocation, while contextual bandits and predictive scoring support personalisation and next-best-action decisions ^[src-027].
• The Braze article also makes the reward loop explicit for campaign decisioning: customer interactions such as clicks or purchases update confidence across variations and allow the system to adapt quickly ^[src-027].

Related entities

• Hightouch
• Braze

Related concepts

• Personalisation Gap
• Treatment Personalisation
• Contextual Bandits
• Multi-Armed Bandits
• Exploration-Exploitation Trade-off
• Reinforcement Learning for Marketing
• Agentic Marketing
• Marketing Bandit Optimisation
• Customer Feature Matrix
• Intelligent Selection

Source references

• ^[src-023] Hightouch — “Under the hood of AI Decisioning, part one: Overcoming the personalization gap”
• ^[src-024] Hightouch — “Under the hood of AI Decisioning, part two: Reinforcement learning”
• ^[src-025] Hightouch — “Under the hood of AI Decisioning, part three: Multi-armed bandits”
• ^[src-026] Hightouch — “Under the hood of AI Decisioning, part four: Contextual bandits”
• ^[src-027] Team Braze — “What is a multi-armed bandit? Smarter experimentation for real-time marketing”