Improve Customer Care and Increase Your Bottom Line with AI

What if you had the idea that could save the company millions and help your firm create a competitive advantage. What if you could take this idea and in a matter of a few weeks test your hypothesis with real data and develop the proof you need to present to your C-Suite. What if you could then execute and deliver the idea within a matter of a few months. What if you could have the agility of a startup while operating within the constraints of a large organization.

This presentation will help you answer those questions and become your company’s hero.

Transcript

2. Big Data & AI Conference Dallas, Texas June 27 – 29, 2019 www.BigDataAIconference.com
3. 1 1 Improve Customer Care and Increase Your Bottom Line with AI Customer- Driven IBM 1 © 2019 IBM Corporation 26 June 2019 IBM Institute for Business Value IBM Improve Customer Care and Increase Your Bottom Line with AI Customer- Driven Digitization © 2019 IBM Corporation 28 June 2019 IBM Institute for Business Value 1
4. Saurabh Shah Partner Global Practice Leader, AI and Analytics Mail: Saurabh.Shah@us.ibm.com LinkedIn: www.linkedin.com/in/ai-leader- saurabh-shah Agenda • The AI Opportunity • AI Level Setting • Practical steps for starting your AI journey
5. Manufacturing It is predicted in manufacturing, AI can improve demand forecasting accuracy by 10-20% AI is yielding significant economic benefits Financial services AI is expected to reduce operating expenses by at least 22%, especially around client engagement activities Retail industry AI through personalization on customer data, is expected to drive atleast 1-2% increase in new sales for retailers Marketing AI is predicted to create $1.4-$2.6 trillion of value in marketing across industries through personalization and customer service management Supply chain management AI is predicted to lead to value creation of $1.2-$2 trillion in supply chain management across industries through better supply and demand forecasting
6. Many organizations are moving towards their cognitive journey, with greater proportion of outperformers in more mature phases Enterprise grade AI Not considering 21% 9% 7% 47% 30% Consid ering 19% 18% 16% Stages of adoption of cognitive computing Outperforming organizations are in more mature phases of 6 their cognitive journey vs. 28% 6% EvaluatingPiloting Phase in cognitive journey All others Outperformers Out of all organizations that are in operating/ optimizing phases, 73% are 9% 4% Impl eme nting 14% 5% 1% 1% Operating Optimizing
7. Quick level setting on AI 7
8. The evolution of AI General AI Revolutionary Narrow AI Emergin g Broad AI Disruptive and pervasive We are here 2050 and beyond
9. Artificial Intelligence is transformational – machines that approximate humans Engage s Interacts and assists by understanding and reasoning around both content and context Decides Posits unexpected decisions based on a breadth of data that a human would be unable to process Discovers Identifies hitherto unknown connections, draws new insights and creates new value 7 7
10. 8 8 Intelligent Workflows A better approach to unlock value Intelligent workflows use real-time insights to change how work gets done. With data as the foundation, we configure processes and orchestrate emerging technologies to achieve business outcomes. Agile at the Core Innovation at Scale Speed to Value Custome r Centric
11. Practical Steps for successful AI 11
12. Four success factors for starting your AI journey Change Change heads, hearts and hands Execution Avoid AI tourism Data There is no AI without data Strategy Don’t forget the “why” 12 Your AI strategy needs to be driven by the desired business values and outcomes. AI is a means to an end, not an end in itself. Start with the end goal in mind. When enterprise data is coupled with external data and made accessible via a platform, you can unlock endless AI opportunities. Without accessible data in the right format, there is no AI. Understand the need, build the solution, and execute with scale in mind. Building the necessary skills required to enable technological and process changes requires significant change management focus to successfully re-train your talent pool, maintain the culture, and achieve scale.
13. 1. Strategy Don’t forget the “why” 11  Create a business-value- driven AI strategy  Improving a business through digital transformation requires an enterprise-wide approach
14. Enterprises have also become clearer and more discriminating in what is important to their enterprise AI strategy 12 Companies are prioritizing driving top line growth with their investments in AI above cost considerations 36% 15% 47% 48% 40% 44% 49% 70% 58% 77% Customer satisfaction Customer retention improvement Customer acquisition cost reduction Other operational cost reduction Revenue growth from new market entry Top 5 value drivers for adopting AI for outperformers 2018 2016 Source: 2018 AI Survey : AI2. What are the important value drivers for artificial intelligence/cognitive computing?; N=5001
15. 2. Data 15 There is no AI without Data  Make data the cornerstone of your delivery strategy  Don’t avoid AI because you think you have no usable data  Internal, proprietary data is not your only source to power AI initiatives  Data privacy has to be a core part of the overall data management strategy
16. A trucking company in a low- margin transportation industry niche wanted to build new revenue streams. It leveraged internal location data from its trucks and combined it with licensed data sources to launch a new truck-based smart advertising business that cognitively optimizes ads based on location, weather, and other information. A US federal government agency was losing millions of dollars a month and putting its employees at risk due to adverse weather conditions impacting transit to work. It leveraged a combination of public, licensed, and proprietary data to build a cognitive decision engine and employee dashboard to help automate and communicate critical transportation decisions. A quick-service restaurant leveraged hyperlocal data from IBM MetroPulse in combination with its own footfall data to improve location targeting, resulting in notable visit lift gains. 16
17. 3. Execution 17 Avoid AI tourism  Scale or fail  Nothing has bold impact in isolation  There is no singular starting point for automation and AI journeys  Focus on re-imagining your processes rather than applying AI as patchwork
18.  AI systems augmenting and assisting 20,000 advisors across 5,000 branches to identify frequent requests, determine level of urgency, and respond more quickly and accurately • Saves 200,000 working days annually which gets reassigned towards training, upgrading advisors' skills and expanding sales activities • Focused program to re- train advisors and drive adoption  Supports 100,000+ conversations per month, easing the burden on call center staff • Recognizes 40+ distinct use cases to quickly resolve easy requests so that agents can focus on helping customers with complex issues • Increases speed of customer response times by 99 percent and significantly improves customer satisfaction  Makes expert knowledge accessible to junior employees • Time spent on researching for possible solutions or hazards has been reduced by 75% • Saved US$10 million- worth of time and kept employees safe • Created a bridge for knowledge transfer 18 Crédit Mutuel Autodesk Woodside
19. 4. Change 19 Change heads, hearts and hands  You need talent that understands the intersection of data and algorithms, as well as their impact on process chains and workflows  Change management is key for the fundamental shifts in the workplace as humans and machines work side by side