Accelerating intelligent automation for competitive advantage January 15, 2018

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

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

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

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

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

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

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

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