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— Peter Schooff (@PSchooff) September 20, 2016
Some really nice thoughts and comments/links.
MyPoV : How Do You See the Relationship and Interplay Between AI and BPM in the next Few Years?
The state we are in the BPM (Business Process Management) roadmap, was not realized overnight. It is getting enriched day-by-day by rattling with BPM in the enterprise playground with experience, learning, implementations, failures, scenarios and tagging along mushrooming disruptive technology trends. BPM with its inception as a process modeler/development /integration/orchestration/BAM etc. has matured as an enterprise wide platform to meet [more or less] the enterprise demands of transformation/mobility/decisioning/ACM/next best actions/mobility/social/analytics etc.
The same holds good for AI/Cognitive Computing. AI (“Artificial Intelligence”) is undoubtedly the “Nirvana” state which every enterprise/organization is trying to achieve as the final destination. But it does not happen over a blink of an eye, instead it is an evolving journey.
The journey to AI can be broadly classified as:
→ BASIC COMPUTING [scripts + repetitive steps in a single application]
→ ENHANCED COMPUTING [rpa + monotonous repititive job across applications]
→ COGNITIVE COMPUTING [machine learning + analytics]
- Basic Automation :
- Human with tools | structured data sets | Goal: Labor Efficiency
- Robotic Process Automation [RPA] :
- Human augmented with Robots | unstructured + patterned data sets | No Decisioning (targeted for Head Down Workers) | Goal: Labor Efficiency
- Autonomics :
- Robots augmented with humans | unstructured + patterned data sets | Goal: Labor Elimination
- Cognitive Computing :
- End to end robots with human oversight | unstructured + NO patterned data sets (targeted for Knowledge Workers) | Goal: Labor Elimination
- Artificial Intelligence – AI :
- Fully automated with NO human involvement | unstructured + NO patterned data sets (targeted for Knowledge Workers) | Goal: Labor Elimination
When we talk about the relationship and interplay between BPM & AI in next few years, the targeted enterprise audience is presumed to have matured over “enhanced computing” and addressed/identified all the underlying challenges w.r.t. monotonous manual intensive repetitive jobs/activities.
Some of the interesting use cases that enterprises can adopt based on the BPM + AI/Cognitive Computing duo are:
- Developing Self-Healing Processes [process optimization, exceptions and anomalies that are resurfaced during execution being auto corrected based on the past resolution steps → analytics & machine learning driven]
- Evidence based learning – data driven
- Adaptive and Predictive analytics + Past scenario based Actions [Machine Learning] for decisioning
- Virtual workforce controlled by Business Operation Teams
- Handwriting to Text or Speech (Identification & Learning Graphology Techniques)
- Debugging, Troubleshooting and Solution Wizard
- Text & Mail categorization/recommendation
- Support Issues and enriching KeDBs (Knowledge Error Databases)
- Self-Driving Cars – by building artificial intelligence and algorithms
- Image Processing
- Banking / Retail / Telecommunication
- Identifying Prospective Customers and Partners
- Satisfactory index of the Customer (based on relationship, transaction, marketing campaign etc.)
- Fraud, Waste and Abuse of Claims
- Forecasted Credit Risk and credibility of the customer
- Effectiveness of a Marketing Campaign
- Eg: How many accepted the offer and how many rejected it. Any decisive factors leading to acceptance.
- Cross Selling and Recommendations
- Eg: Ecommerce sites : People who purchased this product also purchased this
- Contact Center (helps the CSR to engage the customer during the call with the relevant data)
- Eg: We have seen that you have ordered the cheque books to a separate address (different from the registered address) – would you like to change your Address Details
- Healthcare & Life sciences
- Scanning & Screening – Biometrics
- Drug Discovery based on the component mix
- Diagnosis and remediation based on the Symptoms, Patient Record and Lab Reports
- AECP – Adverse Event Case Processing Scenarios based on drug, patient, geo, climatic conditions, past history, food intake etc.
- Handwriting / Signature / Fingerprint / Iris / Retina Verification
- Face Recognition
- DNA Pattern Matching
- ….and many more
Definitely, there are concerns in adopting the Artificial Intelligence on the Day-1 [it may not be a cake walk]. There are lot of factors that are getting disturbed from our traditional paths, and it is only a matter of time for the thoughts to sink , the dust around the buzz to settle down and most importantly GETTING the CONFIDENCE of RELYING BLIND-FOLDED on a MACHINE/ROBOT.
To cite a few scenarios: Are we comfortable and confident enough to sit in a driverless car or airplane? Or are we fine as a financial institution to manage all our high-profile privileged customers based on an algorithm? May be / May be NOT always.
- it becomes crucial from organizations to define the a bucket or business scenarios which can be an eligible candidate for BPM/Process & AI – avoiding RISKS that can have catastrophic effect on the customers/organization. To be precise, the RISK of Loss should not be more than the investment on AI.
- time and exhaustive scenarios are the key factors that will enrich the intelligence of the system based on real time transactions good[favorable] or bad[un-favorable] – is the business prepared for the same?
- it is important to have a balance between the enhanced customer experience and the operational cost reduction with virtual workforce
The business benefits that Enterprises can reap by augmenting BPM with Artificial Intelligence are:
- Reduced Operation Cost
- Elevated Customer Satisfaction / Experience
- Right Product to the Right Customer at the Right Time via the Right Preferred Channel
- Reduce Customer Churn
- Personalization through Prediction and Intelligence
- Increased Workforce Productivity
- Help organizations unlock the value of the wealth of data they have in-house
- Close the gap with the new online providers by offering real-time decisions
New terminologies like SPA [‘Smart Process Automation‘] are also coined, considering the process and cognitive computing/AI duo. Enabling the Process to select the best performing algorithm on the fly.
What is your take – How Do You See the Relationship and Interplay Between AI and BPM in the next Few Years?
Happy Learning 🙂
The Banking & the Financial services industry is the widely and wildly conquered sector where digitization and automation has been enabled to the max by IT. But in recent times, with the advancement of technology and innovations, it is important for the Banking industry to explore, pace up, transform, innovate, re-think and live in the present. The technology adoption and re-incarnation of business strategy/priorities to serve the customers will definitely play a crucial role in the Bank’s journey in making it stand tall n strong in the crowd (as a differentiator).
A nice read article on “Top Retail Banking Trends & Predictions“.
Key Highlights :
- The ‘Platformification’ of Banking
- Removing Friction from the Customer Journey
- Making Big Data Actionable
- Introduction of ‘Optichannel’ Delivery
- Expansion of Digital Payments
- Executing on Innovation
- Exploring Advanced Technologies
- Emergence of a New Breeds of Banks
- Mining New Talent
- Responding to Regulatory and Rate Changes
few additions …
- Next-Gen Customer Experience by including
- wearable device adoption
- analysis driven Next Best Actions
- ..and many more
- Developing Customer Centric Applications (primarily self service enabled)
- Welcome & Leverage Open Source Stacks (OSS)
- Crowd-Sourcing Initiatives (loan, mortgage, risk management etc)
- Blockchain technology making the financial system more decentralized
- Emergence of Banking Market Apps
- Robot Advisers that stop you from making unsound financial choices, in real time – Customer Assist
- Establishment of Banking Innovation Labs
- Beacon Technology Adoption
- Heat-Map Technology – Identifying right customer for the right product at the right time
- Automated Appointment/Scheduling/Token Management – helps avoiding unexpected crowd and long queues
- Battling with mushrooming Mobile Wallets
- DevOps driven CI/CD in IT – for streamlining Release Management
- Streamlining Operations in IT with Robotic Automation / Machine Learning techniques
Interesting Read Articles:
- 10 Branch Banking Innovation Strategies for 2016
- The Uberization of Banking
- What Makes a Great Mobile Banking App
- Digital Disruption Forces Financial Institutions to Rethink Priorities
- Selfies Transforming Mobile Banking
- With Digital Banking, Consumers Must Come First
- Internet of Things: Opportunity for Financial Services?
- Banks Need to Make Mobile Apps An Experience, Not An Add-On
- Article : The derivative effect: How financial services can make IoT technology pay off (link : pdf)
- 7 Habits Of A Highly Successful Digital Bank
- Differentiation in Banking Requires Better Data Insights
- New Customer Onboarding Goes Beyond Slick Marketing (link: pdf)
- Mobile Banking Satisfaction Drops As Digital Expectations Rise
- Banks Expand Innovation Investments to Battle Fintech
- How Banking Can Survive Digital Disruption
- Three Barriers to Banking Innovation
- Digital Thinking for Bank 1.0
- Becoming a Smarter Bank
What are your top trends & predictions in the retail banking space?
Happy Learning!! 🙂
Here are a few bookmarks for gearing up with Bigdata and hadoop from a newbies standpoint :
- Big Data University:It has a very rich repository of course materials (targeted for a beginner)
- Have completed the Hadoop and Bigdata course and certification – its nice and crisp
- Some of my previous Blogs
- Top reads and views in big data
- What is Hadoop?
- IBM Big Data Videos
- O’Reilly Webcast: An Introduction to Hadoop
- Hadoop & Bigdata
- Intro to Hadoop (#nice)
- Big Data in Real Time
- What is Hadoop ? by Cloudera CEO
- Apache Hadoop– Petabytes and Terawatts
- Hadoop Summit 2011– one of the videos
- eBook– Understanding Big Data: Analytics for Enterprise Class Hadoop n Streaming Data
- Realtime Analytics for Big Data: A Facebook Case Study
Some Useful Links :
Happy Learning!! 🙂