1. Accelerating intelligent automation for competitive advantage
January 15, 2018

2. Evolving from a human-first to a hybrid-human-robot approach
Human-first approach
Focus on recruitment & training
Offshoring, labor arbitrage
Cost model per human hour
Standardization, repetitive processes, reporting
Break-fix
Traditional business operations
Robotic-first approach
Focus on extreme automation
Machine learning, cognitive
Knowledge arbitrage
Cost model per transaction, per robot, per machine
Preventive, self-heal, self- serve
Digital business operations
Value to the digital enterprise
Reduce process cost
Boost employee productivity & satisfaction
Improve service quality & customer satisfaction
Increase revenue
Competitive advantage

3. Intelligent automation’s maturity continuum
Automation focus
Artificial
intelligence
Algorithmic
automation
Marketing
Enhanced process
automation
Customer interactions
Robotic process
automation
Virtual agent, fully autonomous, sentiment / empathy, avatar
Basic
automation
Transactions
Scripted chat bots, text & speech, hybrid human handoff
Cognitive, emotion, reasoning, deep neural nets
Processes
FAQ text chat bots, human back-up
Predictive, machine learning, NLP, narrow AI
Humans supervise
Decision support analytics
Factory
User experience
Analytics
Data classes
Use case
Video, image, gesture, speech, social, IoT
Rules-driven
Unstructured + big data, IoT
Structured + unstructured
Supply chain
Structured
Process
Transaction
Operation
Interaction
Angela

4. Intelligent automation’s maturity continuum
Automation focus
Artificial
intelligence
Algorithmic
automation
Marketing
Enhanced process
automation
Customer interactions
Robotic process
automation
Basic
automation
Transactions
Chat bot
Cloud-based machine learning
IoT machine learning
Processes
Factory
User experience
Analytics
Data classes
Use case
Supply chain
Angela

5. CGI clients around the world are executing Robotic Process Automation and experimenting in Algorithmic Automation and AI
63% of clients are executing on RPA.
17% are executing on Enhanced Automation; 52% are investigating; 32% have not begun.
61% are investigating and experimenting with Algorithmic and AI applications; 35% have not begun.
All industries – globally
N=1327
Source: CGI Client’s Global Insight 2017

6. Industries shift relative ranking across automation categories
KEY INSIGHTS
Communications and Financial Services lead across all categories
Retail lags across all categories
Government is in mid-field in RPA but last in advanced categories
Manufacturing more focused on advanced automation than RPA
RPA scores are relatively closer across industries – everybody is doing it
Automation Maturity Score*
Communications
Financial Services
Transport / Logistics
Utilities
Government
Manufacturing
Oil and gas
Retail
RPA
0.73
0.64
0.63
0.61
0.60
0.58
0.54
0.53
Enhanced Automation
0.34
0.41
0.28
0.29
0.22
0.40
0.27
Algorithmic / AI Automation
0.30
0.26
0.24
0.16
0.25
*Weighted average of 4 responses (investigating, experimenting, transforming, complete) with scoring ranging from 0.0 “no maturity” to 1.0 “fully mature”
Source: CGI Client’s Global Insight 2017

7. Canada more cautious than the rest of the world
CGI’s Canadian clients are following the rest of the world with a 6-9 month lag
KEY INSIGHT
Canada more cautious than the rest of the world
57% of clients are executing on RPA
13% are executing on Enhanced Automation; 49% are investigating; 38% have not begun
55% are investigating and experimenting with Algorithmic and AI applications; 42% have not begun
All industries - Canada
N=200
Source: CGI Client’s Global Insight 2017

8. Montreal’s AI ecosystem - integrated for success
Business intelligence
Deep learning
Applied mathematics
Data mining
Cybersecurity
Intelligent automation via applied AI
Bridging the gap across industries
Industrial research & technology transfer AI
Academic research
AI commercialization
Natural language understanding
Neural networks
Machine intelligence
Deep learning
Machine learning
Reinforcement learning

9. Montreal’s AI capability
Key takeaways
Evangelize
Accelerate
adoption and encourage business to experiment to more advanced levels of the AI continuum
Montreal’s AI capability
to global clients
Assess your maturity – execute on RPA – experiment up the spectrum
Focus on processes with high volume, manually intensive, multi-system, error prone
Perform comprehensive RPA process assessment, identify your prioritized process back log, quantify the RPA business case
Consider accelerating RPA through a factory approach
Move from back office to middle office to front office processes
Develop the full spectrum of IA as a core competence and centre of excellence in the organization
Focus next on machine learning, automating advanced decisioning (fraud, claims, risk, credit decisions)
Work with regulators on ‘true’ AI (deep learning) challenges (e.g., explainability, XAI)
Plan for the HR and organizational change implications
Re-skill humans to be problem-solving SMEs and Automation supervisors, define new roles
Understand your talent gap