Beyond RPA: Impact of AI for End- User Application & Device - - PowerPoint PPT Presentation

Beyond RPA: Impact of AI for End- User Application & Device Support

Sam Gross, Founder & CEO, ChoiceWORX

2 SIMPLIFYING THE INTELLIGENT AUTOMATION OF IT Confidential

Definitions: RP RPA Au Autom

• mation
• n v
• vs. In

Intel elligen ent Au Autom

• mation
• n

RPA Automation - Smart software programmed to do high-volume, repeatable tasks

2

…gives any work process that is definable, repeatable and rules-based the ability to map out a business process and assign a software robot to manage the execution of that process

Designed to be a proxy for a human worker

…ability to analyze vast amounts of dynamic and unstructured input and execute processes that are highly dynamic and non-rules based.

Learning systems that are adaptive and can make adjustments optimized for a new situation Intelligent Automation - Systems that Think, Systems that Learn, Systems that Adapt

3 CONFIDENTIAL

Cost? Time? Expertise? Risk?

Software Platform Fit for Purpose General Purpose

Definitions: Fi Fit for Purpose se Platforms s vs.

• s. Ge

General Purpose se So Software

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Digital Disruption: Requires Legacy y IT Paradigms to Change Dramatically

THE GAP

FUNCTION-BASED TOOLS/CONSOLES

OPEN, API ENABLED, DIGITAL PLATFORM SITUATIONAL, SELECTABLE, & ALGORITHMIC FLOWS

Policy Preference Cost Expedience First

COIN-SORTER FIFO STACK

BOTS, OMNI-Channel, DIGITAL L1

ARBITRAGE, SPECIALIZED, REPETITIVE

Service Desk Deskside NOC/Ops

TRADITIONAL OPERATIONS PARADIGM

IT as Integrator/Sourcing Specialist

DIGITAL OPERATIONS PARADIGM

IT as Digital Enabler

LABOR DYNAMICS PROCESS DYNAMICS TOOLS DYNAMICS

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Digital Disruption: Legacy Analog Support Processes Will Will NO NOT suppo upport t your ur Dig Digit ital al Trans ansforma matio tion

Current State Journey Intelligent Automation Journey

How it starts Digital data from device is transformed into analog message by the End-user and relayed to the call center Analog message from End- user is transformed into digital data by the call center agent and entered into a case management system HUMAN Speed Effort: 15 Min – 4 Hrs. Digital fault data from device is delivered directly to the AI Automation A BOT and AI in the cloud pinpoint the causal fault,

• rchestrate remediation and validate an operational

state . Symptomatic faults are ignored because the context of the application is fully evaluated MACHINE Speed Effort: 30 Sec to 5 Min End-user application or device fails and generates digital data on End-user device

15 Min

Expedience

30 Sec

Expedience

4 Hrs 5 Min

Effectiveness Effectiveness

SME diagnoses the issue by asking question of the End-user or initiating a manual remote control session on the device

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Digital Disruption: Impacts your IT Infrastructure At At the Same Time as Your Applications

DATA CENTER AUTOMATION

ORIGIN

▪ JCL ▪ Scheduling ▪ Scripting ▪ Runbook Automation ▪ OEM Consoles

DESTINATION

ARTIFICIAL INTELLIGENCE (AI)

ENABLED & DRIVEN BY: NEXUS OF FORCES EXPERIENCE ECONOMY DIGITAL TRANSFORMATION FOUR INTERDEPENDENT PATHS OF EVOLUTION

BUSINESS PROCESS AUTOMATION

Trigger-based, Stored Procedures

INFRASTRUCTURE IMPACTS AND EVOLUTION TECHNOLOGY AUTOMATION PROGRESSION DATA REPRESENTATION CONTINUUM

Rules-based, Structured Workflows Robotic Processes, Natural Language Orchestration Robotic Process Automation Narrow AI Self-learning Structured & Managed Master Data Management Big Data, Unstructured Graph Data, Self-defining Flow-based, Policy enabled Cognitive Computing

Adapted from HfS, Gartner, IRPA

Ad Hoc ITIL COBIT Lean IT Bi-Modal DevOps Modern Service Management

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Digital Disruption: Industrialize RPA with an AI platform Whe When n your ur RPA BOT Fails ails…… HOW W WI WILL LL IT GET ET FIXED? ED?

End-point Device Operating Environment Database Server Application Server Network

Browser / Client Software

Web Server

Browser / Client Software

Database Server Application Server Web Server Application 1 Application 2 RPA BOT

What Failed?

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Digital Disruption: Industrialize RPA with an AI platform

„ MACHINE LEARNING

─ identifies patterns within data sets and tries to make predictions based on existing data ─ Needs a large volume of data to learn and be able to find valuable information in patterns ─ Plausibility and correctness of results is important, it is usually possible to find something in large sets of data. ─ Not useful in situations where there’s no data, other than some initial conditions, and constraints

„ COGNITIVE COMPUTING

─ Handles conceptual/symbolic data rather than just pure data or sensor streams ─ Computing that comprehends at an advanced level ─ Facilitates human intelligence ─ Humans are firmly in charge of decision making

„ MACHINE REASONING

─ Generate conclusions from available knowledge by using logical techniques such as deduction and induction. ─ Reasoning systems interact with Semantic Graphs providing expressive power and predictive abilities ─ Algebraically manipulates previously acquired knowledge in order to answer a new question ─ Uses algorithms to replicate human actions and self-determines the Next Best Action

Pl Platforms for Mach chine learning, Cognitive computing, Mach chine reasoning, Sematic c analysis, De Deep learning & Na Natural language proce cessing are important to understand

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OBJECTIVE Reduce the time to get what is wanted or required. MEASUREMENT Total time to complete the ask with active & wait values

EXPEDIENCE EFFICIENCY

OBJECTIVE Reduce the total cost of

• perations or
• wnership.

MEASUREMENT Total cost/flow to complete by skill mix & choice. OBJECTIVE Increase successful

• utcomes & process

consistency. MEASUREMENT Track satisfaction, successful outcomes & process consistency.

EFFECTIVENESS

OBJECTIVE Reduce the number of touches, redirects & resupplies of information. MEASUREMENT Count total touches, redirects & restarts per issue.

EMPOWERMENT

OBJECTIVE Anticipate & supply guidance for next steps & actions to success. MEASUREMENT Click-through percent

• n recommendations

& automation success.

ENRICHMENT

Digital Disruption: Boost Customer and Employee Satisfaction Re Reimagine Me Metri rics Whe When n Powered by y an AI Platform

Traditional IT Service Level Measurements Have Become Obsolete

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Digital Disruption: Ap Apptinuum Re Reimagine End-us user Applic pplicatio tion n Suppo upport t & De Devic vice Suppo upport

Solve Recurring IT problems Before You're Even Aware of Them

Proactive & Instantaneous Remediation and ChatBOT Powered End-user Support Improve Application Availability Boost End-user Experience AND Reduce Cost Complicated Scripting and Agents Are No Longer Necessary Full Service Automation as a Service

Benefit from AI without Hiring or Re-skilling

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Digital Disruption: Ap Apptinuum and Intel vPro AMT

Provision Manage Remediate Secure Break/Fix

Firmware Update Agent Presence Discovery Remote Command System Defense Platforms equipped with Intel vPro AMT can be managed remotely, regardless of whether they are powered up or whether they have a functioning OS Hardware Event Manager Fast Call for Help KVM

Ex Extended function

• nality to
• Rob
• bot
• tically En

Enable an En End-po point Device across the he lif lifecy ecycle is cle is d deliv eliver ered ed t to t the ch e chip ip b by In Intel v el vPro A Act ctiv ive M e Man anag agem emen ent T Tech echnolo logy

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Enjoy Significant Labor Cost Red eduction… AN AND Boos

• ost En

End-user er Exper erien ence

Th The fir first specializ ialized SaaS aaS Automatio ion as as a a Servic vice pla latform co combining AI and Intel vPro Active Management Technology (A (AMT) T) for au automa mated ed End-use user Appl Applicati tion n and nd Device Suppo upport t

13 CONFIDENTIAL

To To Lear arn Mor More Vis isit it OU OUR Exhib ibit it

Sam Gross 914-548-9865 sam@choiceworx.com www.ChoiceWORX.com Thank you for joining Beyond RPA: Impact of AI for End-User Application & Device Support