Sustaining AI Project Performance Training Course

Name: Sustaining AI Project Performance Training Course
Availability: InStock

Maintain AI project outcomes after launch through linked benefit, model, service, risk, governance, and improvement controls.

At a glance

Duration: 5 days
Format: Classroom
Cities: Bali, Vienna, Dubai, Nice, Paris, Madrid and more
Next session: 11 – 15 October 2026, Bali
Average fee: 5,800 €

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Sustaining AI Project Performance Training Course

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Overview

The Sustaining AI Project Performance Training Course is a five-day intermediate course for project managers, AI product owners, program managers, PMO teams, delivery leaders, and performance analysts who leave with an AI Project Performance Sustainability Plan. The course links delivery indicators, business benefits, live model and service measures, operational risk, decision ownership, and improvement cycles after launch. Agile Leaders Training Center delivers this course on sustaining AI project performance.

Who Should Attend

Project functions responsible for delivery outcomes, dependencies, and corrective action
Product functions responsible for AI service value, adoption, and operating decisions
PMO functions responsible for portfolio reporting, assurance, and benefit tracking
Technical operations functions responsible for model and service monitoring
Risk functions responsible for controls, escalation, and performance evidence
Business functions responsible for benefit ownership and outcome review

The course assumes participants manage projects or operational performance and leaves out model coding, data science algorithms, and infrastructure configuration.

Departments and Industries

The course supports AI project performance decisions across service and asset-based organizations.

Project management offices and transformation teams
Technology, data, and digital product functions
Financial services and professional services
Healthcare operations and education services
Manufacturing, logistics, and energy operations

Learning Objectives

By the end of this course, participants will be able to:

Analyze AI project outcomes across delivery, service, model, and benefit measures
Build performance baselines, thresholds, ownership, and escalation paths
Apply benefit realization reviews to post-launch AI decisions
Evaluate operational AI risks using monitoring and control evidence
Prioritize corrective actions through structured improvement cycles
Create an AI Project Performance Sustainability Plan

Course Agenda

Day 1: Performance Context and Outcomes

AI Project Lifecycle and Operating Transition Map
Delivery Outcome and Business Benefit Logic Model
Stakeholder Value and Benefit Ownership Matrix
Post-Launch Performance Baseline Register
Success Criteria and Evidence Alignment Checklist

Day 2: Measures and Monitoring

Project Service and Model Indicator Architecture
Leading and Lagging Performance Measure Canvas
Model Quality and Service Reliability Monitoring Plan
Data Drift and Performance Degradation Trigger Matrix
Executive AI Performance Dashboard Design

Day 3: Benefits and Decision Control

Benefits Realization Review Cycle
Benefit Variance and Root Cause Analysis Method
Decision Rights and Performance Accountability Matrix
Threshold Breach and Escalation Protocol
Value Risk and Tradeoff Decision Record

Day 4: Risk and Continuous Improvement

NIST AI RMF Govern Map Measure Manage Application
Operational AI Risk and Control Register
Corrective and Preventive Action Prioritization Grid
Change Control and Model Update Assessment
Performance Learning and Improvement Backlog

Day 5: AI Performance Sustainability Practice

Suggested Exercise: Outcome and Benefit Baseline Review
Suggested Exercise: Monitoring Threshold and Alert Design
Suggested Exercise: Performance Incident Decision Simulation
Suggested Exercise: Improvement Backlog Prioritization
Capstone Exercise: AI Project Performance Sustainability Plan

Practical Exercises

The course includes suggested activities for converting live evidence into project and operating decisions.

Suggested activity: map delivery results to business benefits and accountable owners.
Suggested activity: design model, service, and adoption thresholds with escalation actions.
Suggested activity: review a performance variance and select a controlled response.
Suggested activity: present an AI Project Performance Sustainability Plan with measures, owners, risks, and review cycles.

FAQs

Who suits the Sustaining AI Project Performance Training Course?

Project managers, product owners, PMO teams, delivery leaders, and performance analysts suit the course; it assumes responsibility for project outcomes or operational performance.

How does sustaining AI project performance differ from general project monitoring?

Sustaining AI project performance combines delivery and benefit measures with live model, data, service, adoption, risk, and human oversight indicators after launch.

Which measures support sustained AI project performance?

Sustained performance uses linked measures for project delivery, business benefits, service reliability, model behavior, data changes, adoption, incidents, risks, and corrective actions.

How should AI performance thresholds guide decisions?

AI performance thresholds should identify acceptable ranges, accountable owners, review frequency, evidence sources, escalation paths, and predefined response options.

What belongs in an AI Project Performance Sustainability Plan?

An AI Project Performance Sustainability Plan connects outcomes, measures, baselines, thresholds, benefit owners, operational risks, decision rights, escalation, improvement actions, and review cycles.

Conclusion

Participants leave with an AI Project Performance Sustainability Plan connecting project outcomes, operating indicators, benefits, risks, owners, and decisions. The work product changes performance review from separate reports into a linked management cycle. It gives teams a reusable structure for monitoring, escalation, corrective action, and benefit review after launch.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 61-76 of 76 events

Location	Dates	Duration	Mode	Price
Paris	Week 32, 2027 9 – 13 August 2027	5 Days	Onsite	€5,700
Frankfurt	Week 32, 2027 9 – 13 August 2027	5 Days	Onsite	€5,700
Lisbon	Week 33, 2027 16 – 20 August 2027	5 Days	Onsite	€5,700
Al Jubail	Week 33, 2027 22 – 26 August 2027	5 Days	Onsite	€5,700
Milan	Week 34, 2027 23 – 27 August 2027	5 Days	Onsite	€5,700
Riyadh	Week 34, 2027 29 August – 2 September 2027	5 Days	Onsite	€5,700
Amsterdam	Week 35, 2027 30 August – 3 September 2027	5 Days	Onsite	€5,700
Kuala Lumpur	Week 36, 2027 6 – 10 September 2027	5 Days	Onsite	€5,200
Accra	Week 36, 2027 12 – 16 September 2027	5 Days	Onsite	€4,100
Rome	Week 37, 2027 13 – 17 September 2027	5 Days	Onsite	€5,700
Johannesburg	Week 37, 2027 19 – 23 September 2027	5 Days	Onsite	€4,500
Bangkok	Week 38, 2027 26 – 30 September 2027	5 Days	Onsite	€6,000
Prague	Week 39, 2027 27 September – 1 October 2027	5 Days	Onsite	€6,000
Trabzon	Week 39, 2027 3 – 7 October 2027	5 Days	Onsite	€6,800
London	Week 40, 2027 4 – 8 October 2027	5 Days	Onsite	€5,700
Casablanca	Week 41, 2027 11 – 15 October 2027	5 Days	Onsite	€4,100

Frequently asked questions

What does this course cover?

OverviewThe Sustaining AI Project Performance Training Course is a five-day intermediate course for project managers, AI product owners, program managers, PMO teams, delivery leaders, and performance analysts who leave with an AI Project Performance Sustainability Plan. The course links delivery indicators, business benefits, live model and service measur…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

Sustaining AI Project Performance Training Course