AI in Practice · Your reference for all three sessions
Prepared by Deep Hospitalityfor the IHG Owners Association Emerging Leaders Network

AI in
Practice

A working guide to use across all three sessions, from getting a process ready, to using generative AI well, to designing an agent that can carry the work forward.

Emerging Leaders Network · AI Learning Series · October 2026

Keep this guide with you. We will come back to the same pages as the hiring example develops.

The example is recruitment, but the point is broader: learn how to define the work, give AI the right context, decide where it can act, and connect the result to business value.

Three sessions. One hiring journey.

The series
9 OCTOBER · SESSION 1

Are You Ready?

Start before the tool. Clarify the need, map the process, find the data and name the owner. We also set up the difference between AI that responds and AI that acts.

23 OCTOBER · SESSION 2

Solve a Pressing Operational Problem

Use generative AI to refresh the job description and build the internal job spec. The AI produces; the leader still judges, refines and moves the work forward.

6 NOVEMBER · SESSION 3

Agents That Move Money, Not Just Minutes

Hand the end-to-end chain to an agent, keep people in the decisions that matter, and work out where the commercial value sits.

Before AI: can the process run?

Take one real hire. If any of these four are unclear, the technology is not the first problem to solve.

01

Intent

Do we know what we are actually trying to achieve: role, property, timing, banding and constraints?

02

Process

Can we describe what really happens from “we need to hire” to interview, including the variations?

03

Data

Is the information needed to run the process available, usable and in a place we can access?

04

Owner

Is there a named person accountable for the process and the decisions inside it?

My biggest readiness gap: ________________________________________________

When AI responds. When AI acts.

Generative AI

Useful when you want an output and a person will decide and execute what happens next.

One interactionRefresh the Sous Chef job description.
Produces contentCreate interview questions and a scorecard.
WaitsIt stops until you ask for the next thing.
The person moves the workYou post, review, shortlist and schedule.

Agentic AI

Useful when a defined workflow can continue across several steps inside agreed permissions.

Works across stepsAdvertise, collect, shortlist and coordinate interviews.
Completes tasksUses authorised tools and data to move the process forward.
Works towards a goalThe outcome stays fixed while the next action can change.
The system moves the workIt continues, checks progress and escalates exceptions.

Build the role pack with the right context

A polished answer can still be wrong. The quality of the role pack depends on what the leader knows to provide, and what the leader knows to challenge.

Job description

External. Written for a candidate. It should explain the role clearly and represent the property and brand well.

  • Purpose of the role
  • Key responsibilities
  • Experience and capability required
  • Property and market context
  • Brand tone of voice

Job specification

Internal. It carries information needed to hire and manage the role that does not belong in the external advert.

  • Banding / grading
  • Compensation parameters
  • Reporting line
  • Approval route
  • Internal constraints and decision criteria
What did the AI miss? _________________________________________________

Hand the workflow to an agent

An agent is not “AI with a better prompt”. It needs a goal, a process, authorised tools, boundaries and a way to know when to stop or ask for help.

1

Goal

Define the result, timing, constraints and approvals.

2

Plan

Turn the process into steps, owners, data and decisions.

3

Execute

Use authorised systems and data to carry out the work.

4

Evaluate

Check quality, gaps, delays and progress against the goal.

5

Adjust

Change the next action when the result is weak or conditions change.

6

Complete

Return the outcome, evidence, exceptions and items needing a person.

Hiring example

Fill this approved role in 21 days without rejecting a candidate or making an offer without human review.

Move money, not just minutes

Current time to fill________ days
Target time to fill________ days
External agency / recruitment spend________
Management capacity released________
Other measurable effect________
Ask a better question:

What changes financially or operationally if this process becomes faster, more consistent or less dependent on external support?

Keep the calculation defensible. Avoid converting every minute into a fictional headcount saving.

Keep people in the decisions that matter

Set before you automate

  • A clear business outcome
  • The real process, including exceptions
  • What data the agent may use
  • What systems it may access
  • Where human approval is mandatory
  • A named person accountable for the result

Do not delegate blindly

  • Do not give unrestricted access to candidate or employee data
  • Do not assume a shortlist or recommendation is correct
  • Do not let a machine reject a candidate without the agreed review path
  • Do not automate an unclear process and expect a clear outcome
  • Do not deploy without an escalation route
  • Do not measure success only in minutes saved

Use the same thinking on your own process

Choose one repeated process from your own area. You will use this in Session 3, but you can start filling it in at any point in the series.

1. What outcome are you trying to move?
2. What is the process today?
3. What data does it need, and where is it?
4. Which decisions must remain with a person?
5. Where could AI act rather than only advise?
6. What would make this commercially worthwhile?

Terms we will use

Goal: the outcome the agent is asked to pursue.
Tool use: authorised access to software, data or applications.
Trigger: the event that starts the workflow.
Human-in-the-loop: a defined point where a person reviews or approves.
Guardrails: rules and permissions that limit what the system may do.
Agent: an AI system that can pursue a goal and take actions within boundaries.
Deep Hospitality
www.deep-hospitality.com
Caroline Hardman · Caroline.hardman@deep-hospitality.com
Nitin Thariyan · Nitin.thariyan@deep-hospitality.com