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External Document © 2017 Infosys Consulting CONSULTING Achieving Repeatable Success with AI
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CONSULTINGAchieving Repeatable Success with AI
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Overcoming AI´s Barriers
Case Studies
Build a Winning Team
Create a World-Class Ecosystem
Our Approach
CONSULTING
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Tableof Contents
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Overcoming AI’s Barriers Keys To Success
Overcoming AI’s barriers are not easy, but it is certainly achievable. The starting point to a successful journey is identifying the correct problem to solve. Too often, firms do this by looking internally at their own operations. Instead, companies should employ the powerful tenets of design thinking, which focus on customer empathy and outside-in thinking to iteratively test prototypes.
Key Tenets of Design Thinking
Following this approach is wise for any customer-facing solution a company designs. And because AI still lacks general awareness, and therefore tends to produce very problem-specific solutions, one could argue it is even more important to deploy in these settings.
Failing to Examine the Cause of Customers’ EmotionsTo give an example, imagine a grocer has noticed customers are unhappy with the selection of products. If they failed to use design thinking and thoroughly analyze the problem from the customer perspective, they might come to the logical conclusion they aren’t sourcing the right products. This could trigger the development of intelligent algorithms to diversify product selection. This would be an ambitious effort, but would certainly provide actionable recommendations.
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EMPATHIZE
DEFINE
IDEATE
PROTOTYPE
TEST
Source: http://dschool.stanford.edu/dgift/
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Overcoming AI’s Barriers
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Finding the Right ProblemWhat if, however, the real problem wasn’t that customers didn’t like the products stores carried, but that the products they wanted were too often not on the shelves due to out of stock? This could produce similar customer feedback, but is caused by a very different problem. To solve the real underlying problem of procurement levels, the store should instead develop an entirely different set of algorithms focused on op-timized replenishment.
Leveraging the Data to Solve itOnce a company has leveraged Design Thinking to identify the solution that addresses the core issue, focus can shift to finding the data required to power the solution. Given the massive volumes of data AI thrives on, this can be intimidating. The good news for companies, however, is that to create significant value, not all algo-rithms require millions of data points. While it is certainly true that voice and image recognition were areas of early AI progress because of the massive availability of data to train these algorithms, even data sets of a few thousand can, in some circumstances, yield vastly superior outcomes to the status quo.
Building a Successful AI SolutionAfter doing a full mapping of the data inputs, teams can then shift to execution mode. Like many technolo-gies, AI solutions are best implemented using agile methodologies in which algorithms are trained, tested and tweaked in an iterative fashion. If your solution involves the shifting of decisions from humans to machines, this testing phase gives you the opportunity to leverage the experience of the people who have traditionally been doing these roles and work with them to find higher value-add activities within the organization.
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Case StudiesInfosys Clients Using AI to Drive ROIAs we mentioned earlier, AI has been navigating its way through a maturity curve. While early work focused on core building blocks – first laying the foundations for neural networks used to identify non-linear patterns and then layering in context-specific machine learning plug-ins – we are now moving into what can be referred to as Applied AI, where software companies are providing solutions to ever more specific use-cases.
In our work with Fortune 500 clients, we are seeing a diverse set of business applications:
• Business process automation: advances on narrow, industry- and company-specific tasks
• Conversational commerce: Chatbots leveraging voice recognition, customer churn prediction
and prospect prioritization
• Customer service: progressively increasing levels of automation and reduction in the number
of tickets requiring human intervention provide superior levels of customer insight and service
• Industry operations: predictive maintenance of equipment, reducing costly operational disruptions
• Energy: learning algorithms to automate buying and selling of resources to optimize prices and yields
• Intelligent hiring: machine learning to predict the cultural fit between teams and potential recruits
• Project management: proactive prediction of delays and more intelligent resource planning
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AI powers some of the leading disrupters in the market – Amazon, Netflix, Airbnb, Google and Uber.
Individual clients’ expectations are shift-ing and AI can serve them at a time when predictions, recommendations and tailored, personalized experiences are key. AI also enables new user interfaces where “Zero UI is the new UI” leveraging technologies like voice recognition or facial recognition.
External Salesand Support Org
Internal Salesand Support Org
Front OfficeSystems
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I. Efficiency
Business ProcessAutomation
Complexity (Feasibility)
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L3 IT-SupportService Requests
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Prognostics and Diagnostics
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Case Studies CONSULTING
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Two areas where we have recently had success are in the fields of deterministic automation and predictive insights.
Deterministic AutomationWe recently had a client that processes massive volumes of EDI transactions. Like any EDI shop, this team spent a disproportionate amount of time handling exceptions, which had handling times of 8 minutes instead of the near real-time performance compliant files experienced.
After working with the client to identify appropriate machine learning tools and collecting / mapping the data fields required to power it, we were able to leverage E2E robotics to significantly reduce the number of mis-matches between invoiced and received items. This project resulted in a reduction of average handling time from 8 minutes to 100 – 120 seconds, resulting in a massive 75% improvement that translated into increased customer service and a reduced cost-to-serve.
Predictive Insights and Cognitive AutomationShifting to the financial services sector, we recently worked with a client who owns and operates a large net-work of ATMs. Like any remotely placed kiosk, ATMs only generate revenue when they’re online, making long-term outages very costly. Historically, all ATMs received the same scheduled maintenance. While this approach was better than simply waiting for machines to break, it inevitably triggered some maintenance that was not needed and still failed to identify failures outside basic norms.
Additionally, because of highly variable transportation patterns, service personnel were often stitching to-gether inefficient service routes. By integrating learning algorithms with machine diagnostic data arriving via the company’s network, we were able to optimize both the servicing that was done and the service routes taken by personnel. This solution resulted in an 18 % cost reduction and a 14 % increase in efficiency.
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Purposeful AI is at the Intersection of Technology, Domain Expertise and StrategyAs more firms arrive at the decision to launch AI initiatives, they are immediately confronted with the question of how best to construct a team capable of success. In our experience, to successfully craft AI-centric solutions, a specific compliment of skills is required. At a high-level, three core elements are required:
Domain: Throughout the AI process, it is vital to deeply integrate domain-specific expertise into the team. At the onset, these resources will play an active role in design thinking exercises geared towards finding the right problem to solve. As the team moves into execution mode, these resources will identify additional data elements that might be relevant to the end solution and help ensure the finished product is tethered to the realities of the market.
Strategy & Execution: It is tempting to characterize this function as simply a good PMO, but this sells this role short. Instead, these resources need to think about the high-level business impacts of the solution, both in terms of financial KPIs as well as the operational impacts to the broad base of constituents affected (customers, employees, partners, etc.). From an implementation perspective, this group additionally needs to construct the long-term architecture required to support the end solution and ensure the work the de-velopers are doing is both scalable and sustainable.
AI (Data & Computer Science): Despite the proliferation of AI building blocks, it is often necessary to lever-age developers and data experts to bring algorithms to life and surround them with the scaffolding to enable the algorithms to make an enduring contribution. These resources will do various tasks such as implement APIs, structure and access the data required by algorithms and, in some cases, extend the machine learning capabilities to incorporate nuances of the problem being solved.
Build a Winning Team
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DOMAIN STRATEGY &EXECUTION
DATA & COMPUTER
SCIENCE
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Dedicated Resources and Access to Situational ExpertsAs shown by the graphic below, AI applications today are by their nature very narrow. Therefore, there has been a massive proliferation of AI solutions, technologies and tools.
As this list continues to grow, building an effective ecosystem of partners and vendors with the knowledge of which AI solutions are relevant to a given problem becomes a fundamental part of a successful AI journey.
Create a World-Class Ecosystem
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Source: http://www.shivonzilis.com
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Scale Purposeful AI with a CoEBy combining the right team with the right ecosystem, any organization can successfully implement an AI solution capable of adding outsized value. We have also found, however, that to achieve enduring traction – and to replicate the success enjoyed by a stand-alone project across an entire organization – a more perma-nent structure is required.
Drawing from our experience, we have found that assembling core AI capabilities into an agile, lightweight team, backed by strong executive leadership, provides the robust, flexible foundation capable of accelerating and amplifying the benefits of AI.
An effective Center of Excellence (CoE) will ensure that learnings are captured and shared across each initia-tive, mistakes are seldom (if ever) repeated, and the most effective approaches reused.
AI Center of Excellence
CONSULTING
Through this structure, companies can build prototypes with a long-term view and contin-uously enhance ecosystems and partnerships to facilitate purposeful AI.
Along the way, a strong CoE can help address important questions such as:
• How can AI support our corporate strategy?
• How are we consistently addressing the moral questions raised by AI solutions?
• How can we shape our future workforce to thrive in a “native-AI” environment?
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Create a World-Class Ecosystem
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Our Approach CONSULTING
How We Can Help You Achieve Successful AIInfosys Consulting’s priority is to help companies navigate the transition to AI. Working with firms from the initial phases of design thinking / data analysis to the full implementation of an AI Center of Excellence, we help companies implement and sustain AI-driven competitive advantage.
Our team of consultants is bolstered by a deep roster of over 500 data scientists who collectively maintain relationships with many of the world’s most innovative AI providers. Taken together, this gives us the ability to provide domain-specific recommendations in areas that drive your business.
While it took companies 10 – 15 years to go digital, the AI wave is arriving more quickly, creating what can sometimes be an uncomfortable situation.
As MIT professor and data expert Dr. Andrew McAfee recently opined, “AI will not replace managers. But managers who use AI will replace those who don’t.” Fortunately, we are here to help you make the successful transition to AI and position it for enduring, long-term success.
An Approach to Drive Immediate Value
Making enterprise data visible end to end
Setting up a Data Lake and Dashboards
Improving productivity, reducing cost, disrupting business models
Robotic Process Automation and improved workflow
Getting insights from Enterprise data
Using algorithms to go fishing in the Data lake
Advanced Analytics
Machine Learning
Data Aggregation &
Visualisation
Intelligent Automation
Using data and insights to drive rules for diagnosis, prediction and reaction
Supervised Machine Learning
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Jonathan Ebsworth PartnerInfosys Consulting
Jonathan is an automation and artificial intelligence partner at In-fosys Consulting. He has spent over 30 years developing strategies and programs to help large clients transform their operations. He is an experienced program manager, enterprise architect and soft-ware engineer. Jonathan is also one of the firm’s leading design thinking practitioners.
Alex BlountPartnerInfosys Consulting
[email protected] Alex is a veteran partner at the firm and leads key technology and
advisory services for clients across Switzerland. He has an industry expertise in manufacturing and has spent much of his career ad-vising top global organizations on their growth and operational strategies – with a focus on how innovative technology can enable competitive advantage for them.
Mark Danaher PartnerInfosys Consulting
Mark is a partner at Infosys Consulting and the leader of the firm’s disruptive technologies practice – which combines some of the brightest minds around digital, big data, artificial intelligence and automation. In his 25 years of consulting experience, Mark has ad-vised and delivered strategic solutions to clients globally, with a focus on the retail, manufacturing, transport and logistics sectors.
Tom Lurtz PartnerInfosys Consulting
[email protected] Tom is a member of Infosys Consulting’s disruptive technologies
practice in Europe and also leads the organization in Germany that focuses on digital transformation and AI. His mission is to help transform companies into digitally-centric organizations, with a focus on customer interactions, new business models and product portfolio optimization.
About Infosys Consulting We are a global advisor enabling organizations to reimagine their future and create sustainable value leverag-ing disruptive technologies. And as part of technology leader Infosys, we have access to a global network and delivery capability of 200,000 professionals that help our consultants implement at scale. To see our ideas in action, please visit InfosysConsultingInsights.com.
About the authors
© 2017 Infosys Limited, Bangalore, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.
For more information, contact [email protected]
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