Companies looking to adopt AI today are bombarded
with technology companies and start-ups selling advanced
machine learning based solutions built on exciting use
cases. However, before kickstarting newer pilots and
investing in these advanced solutions it is useful to step
back and reflect on the overall intent of using AI for
the organization and the traditional suite of analytical
techniques and resources available.Oneway, CIOs can assess
the suitability of an AI solution is it to break it down into
simpler elements and ask five basic questions.
Accenture's report explains how natural language processing and machine learning makes extracting valuable insights from unstructured data fast. Read more. https://www.accenture.com/us-en/insights/digital/unlocking-value-unstructured-data
Accenture's report explains how creating effortless experiences are so simple and easy with our data-driven strategy framework to drive growth. Read more.
Best Practices in Implementing Social and Mobile CX for UtilitiesCapgemini
Are you having difficulties in implementing a modern customer experience solution strategy that meets your customers’ needs across all interaction channels, including mobile and social?
This presentation highlights best practices for the design and implementation of effective CX strategies adapted to the utilities industry.
Presented at Oracle OpenWorld 2014 by Bruna Gapo, Oracle's Utilities Industry Director, Ajay Verma, Capgemini's Global Utility Practice Leader, and Victor Jimenez, Capgemini Utilities Executive.
http://www.capgemini.com/oracle
Learn how AWS is being used to help industrial companies unleash the value of operational data, drive cultural change, augment plant technology with artificial intelligence an build an ecosystem of innovators an entrepreneaurs to revitalise heavy industry.
Accenture's report explains how natural language processing and machine learning makes extracting valuable insights from unstructured data fast. Read more. https://www.accenture.com/us-en/insights/digital/unlocking-value-unstructured-data
Accenture's report explains how creating effortless experiences are so simple and easy with our data-driven strategy framework to drive growth. Read more.
Best Practices in Implementing Social and Mobile CX for UtilitiesCapgemini
Are you having difficulties in implementing a modern customer experience solution strategy that meets your customers’ needs across all interaction channels, including mobile and social?
This presentation highlights best practices for the design and implementation of effective CX strategies adapted to the utilities industry.
Presented at Oracle OpenWorld 2014 by Bruna Gapo, Oracle's Utilities Industry Director, Ajay Verma, Capgemini's Global Utility Practice Leader, and Victor Jimenez, Capgemini Utilities Executive.
http://www.capgemini.com/oracle
Learn how AWS is being used to help industrial companies unleash the value of operational data, drive cultural change, augment plant technology with artificial intelligence an build an ecosystem of innovators an entrepreneaurs to revitalise heavy industry.
Treparel's introduction with content, text analytics and visualization examples about 'Turn Big Data into Business Insight'. Presented at LT Innovate Summit in Brussel (June 27, 2013)
Inside the mind of Generation D: What it means to be data-rich and analytica...Derek Franks
It’s one thing to buy into the current belief that data and analytics can transform business. It’s another thing to live it. In an IBM Center for Applied Insights global study of more than 1,000 enterprises, one particular segment – Generation D (for data) – stands out from peers. These enterprises not only employ more advanced data sources and more sophisticated analytics; they’re also adopting a more systematic, enterprise-wide approach to cloud computing, mobile and social engagement, and data and analytics. Interestingly, this GenD behavior pattern also correlates with stronger business performance across a range of key metrics.
Teaching organizations to fish in a data-rich future: Stories from data leadersAmanda Sirianni
Many organizations are still early in their journey to set up and optimize their analytics function and related capabilities. However, those that are investing in highly skilled data leaders are seeing the business benefits. To learn more, the IBM Center for Applied Insights spoke with some of these leaders.
Through their stories, we discovered the analytics challenges that businesses face across industries and sectors, and how today’s data leaders confront and eventually overcome those challenges. See how these leaders were able to deliver outcomes that far outweighed their early struggles. To learn more: www.ibm.com/ibmcai/cdostudy
The digital revolution is coming to procurement. While many businesses have embraced eProcurement systems and cloud-based tools, digital procurement demands more than that. Is your business ready? Learn three considerations before you move forward.
Implementing the smart factory: New perspectives for driving valueDeloitte United States
For manufacturers looking to implement a smart factory, considering lessons from those who have done it can provide direction forward and pave the way to greater value.
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
The rising collection and analysis of data has shifted the way companies do business. Four key ingredients to develop a data strategy, how to leverage next-generation technologies, and three essential steps for rolling out implementation are included. The Data Ecosystem will show you how to develop and implement the strategies that will meet the needs of your business.
The INTIENT Research informatics platform is designed to help scientific research-intensive organizations in the life sciences industry improve productivity, efficiency and innovation in the early stages of drug development. Visit https://accntu.re/2vPLwJl to learn more.
Accenture's six-country survey among 180 C-level health executives says adoption of AI is measured, but real.
The survey assessed beliefs about market maturity, practical and clinical challenges to the adoption of AI in healthcare.
Enthusiasm for AI (artificial intelligence) is high among health executives, with people skills the most important implementation success factor.
Sufficient staff training/ expertise is rated the most important success factors for AI implementation (ranked in top three by 73 percent of execs).
Visit https://accntu.re/2T4KuXb to learn more.
How to keep pace with changing technology and increase speed-to-value. In order to keep pace in a constantly evolving marketplace, organizations need a new model for sourcin IT services. Sourcing has become one of the most critical functions of the IT organization.
Technology change & the rise of new industriesJeffrey Funk
Using an analysis of many existing and emerging industries, this book (to be published by Stanford University Press) shows how one can analyze the timing of new industry formation. It does this by analyzing the improvements in cost and performance that have enabled new technologies to become economically feasible.
Breakthrough experiments in data science: Practical lessons for successAmanda Sirianni
Leading firms are integrating data science capabilities within their organizations to capture the untapped potential of data science as a source for competitive advantage. Yet, many enterprises are challenged to successfully integrate these capabilities for sustained value and to measure its worth for the organization. This analytics study conducted by the IBM Center for Applied Insights uses practical advice from those seeing the benefits to establish a proven success formula for integrating a data science capability within your organization.
To learn more: www.ibm.com/ibmcai/data-science
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
Treparel's introduction with content, text analytics and visualization examples about 'Turn Big Data into Business Insight'. Presented at LT Innovate Summit in Brussel (June 27, 2013)
Inside the mind of Generation D: What it means to be data-rich and analytica...Derek Franks
It’s one thing to buy into the current belief that data and analytics can transform business. It’s another thing to live it. In an IBM Center for Applied Insights global study of more than 1,000 enterprises, one particular segment – Generation D (for data) – stands out from peers. These enterprises not only employ more advanced data sources and more sophisticated analytics; they’re also adopting a more systematic, enterprise-wide approach to cloud computing, mobile and social engagement, and data and analytics. Interestingly, this GenD behavior pattern also correlates with stronger business performance across a range of key metrics.
Teaching organizations to fish in a data-rich future: Stories from data leadersAmanda Sirianni
Many organizations are still early in their journey to set up and optimize their analytics function and related capabilities. However, those that are investing in highly skilled data leaders are seeing the business benefits. To learn more, the IBM Center for Applied Insights spoke with some of these leaders.
Through their stories, we discovered the analytics challenges that businesses face across industries and sectors, and how today’s data leaders confront and eventually overcome those challenges. See how these leaders were able to deliver outcomes that far outweighed their early struggles. To learn more: www.ibm.com/ibmcai/cdostudy
The digital revolution is coming to procurement. While many businesses have embraced eProcurement systems and cloud-based tools, digital procurement demands more than that. Is your business ready? Learn three considerations before you move forward.
Implementing the smart factory: New perspectives for driving valueDeloitte United States
For manufacturers looking to implement a smart factory, considering lessons from those who have done it can provide direction forward and pave the way to greater value.
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
The rising collection and analysis of data has shifted the way companies do business. Four key ingredients to develop a data strategy, how to leverage next-generation technologies, and three essential steps for rolling out implementation are included. The Data Ecosystem will show you how to develop and implement the strategies that will meet the needs of your business.
The INTIENT Research informatics platform is designed to help scientific research-intensive organizations in the life sciences industry improve productivity, efficiency and innovation in the early stages of drug development. Visit https://accntu.re/2vPLwJl to learn more.
Accenture's six-country survey among 180 C-level health executives says adoption of AI is measured, but real.
The survey assessed beliefs about market maturity, practical and clinical challenges to the adoption of AI in healthcare.
Enthusiasm for AI (artificial intelligence) is high among health executives, with people skills the most important implementation success factor.
Sufficient staff training/ expertise is rated the most important success factors for AI implementation (ranked in top three by 73 percent of execs).
Visit https://accntu.re/2T4KuXb to learn more.
How to keep pace with changing technology and increase speed-to-value. In order to keep pace in a constantly evolving marketplace, organizations need a new model for sourcin IT services. Sourcing has become one of the most critical functions of the IT organization.
Technology change & the rise of new industriesJeffrey Funk
Using an analysis of many existing and emerging industries, this book (to be published by Stanford University Press) shows how one can analyze the timing of new industry formation. It does this by analyzing the improvements in cost and performance that have enabled new technologies to become economically feasible.
Breakthrough experiments in data science: Practical lessons for successAmanda Sirianni
Leading firms are integrating data science capabilities within their organizations to capture the untapped potential of data science as a source for competitive advantage. Yet, many enterprises are challenged to successfully integrate these capabilities for sustained value and to measure its worth for the organization. This analytics study conducted by the IBM Center for Applied Insights uses practical advice from those seeing the benefits to establish a proven success formula for integrating a data science capability within your organization.
To learn more: www.ibm.com/ibmcai/data-science
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
Business Intelligence, Data Analytics, and AIJohnny Jepp
Data is the new currency. In this session, best practices on data collection, management dashboards, and used cases will be shared using Azure Data Services.
Video accessible at bit.ly/APACSummitOnDemand
Shwetank Sheel
Chief Executive Officer
Just Analytics
Poonam Sampat
Cloud Solution Architect - Data & AI
Microsoft Asia Pacific
How to set up an artificial intelligence center of excellence in your organiz...Yogesh Malik
Setting up a COE ( Center of Excellence ) for AI ( Artificial Intelligence ) could be a daunting task. Lack of skills and quality data sets could hold you back. But still you should not wait any longer and start with what you have, build skills by training people, and move ahead in gettering executive approval for building an artificial intelligence center of excellence
Artificial Intelligence: Competitive Edge for Business Solutions & Applications9 series
The growth of Artificial Intelligence in recent years brought forth a major challenge for brands in deploying such AI solutions. Many brands lack the clarity regarding where to start the AI integration process and profitably deploy these solutions in the most effective manner.
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Inte...Kaleido Insights
This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
Dr Christoph Nieuwoudt- AI in Financial Servicesitnewsafrica
Dr. Christoff Nieuwoudt delivered a keynote on AI in Financial Services at Digital Finance Africa 2023 on the 2nd of August 2023 at Gallagher Convention Centre, Johannesburg, Midrand.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
AI solutions are the most important component of the digital transformation of many companies. AI Startups are racing ahead to address the needs of industries. In this paper, we present the broad strategies AI startups can employ to be successful.
How Analytics Has Changed in the Last 10 Years (and How It’s Staye.docxpooleavelina
How Analytics Has Changed in the Last 10 Years (and How It’s Stayed the Same)
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June 22, 2017
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Photo by Ferdinand Stöhr
Ten years ago, Jeanne Harris and I published the book Competing on Analytics, and we’ve just finished updating it for publication in September. One major reason for the update is that analytical technology has changed dramatically over the last decade; the sections we wrote on those topics have become woefully out of date. So revising our book offered us a chance to take stock of 10 years of change in analytics.
Of course, not everything is different. Some technologies from a decade ago are still in broad use, and I’ll describe them here too. There has been even more stability in analytical leadership, change management, and culture, and in many cases those remain the toughest problems to address. But we’re here to talk about technology. Here’s a brief summary of what’s changed in the past decade.
The last decade, of course, was the era of big data. New data sources such as online clickstreams required a variety of new hardware offerings on premise and in the cloud, primarily involving distributed computing — spreading analytical calculations across multiple commodity servers — or specialized data appliances. Such machines often analyze data “in memory,” which can dramatically accelerate times-to-answer. Cloud-based analytics made it possible for organizations to acquire massive amounts of computing power for short periods at low cost. Even small businesses could get in on the act, and big companies began using these tools not just for big data but also for traditional small, structured data.
Insight Center
· Putting Data to Work
Analytics are critical to companies’ performance.
Along with the hardware advances, the need to store and process big data in new ways led to a whole constellation of open source software, such as Hadoop and scripting languages. Hadoop is used to store and do basic processing on big data, and it’s typically more than an order of magnitude cheaper than a data warehouse for similar volumes of data. Today many organizations are employing Hadoop-based data lakes to store different types of data in their original formats until they need to be structured and analyzed.
Since much of big data is relatively unstructured, data scientists created ways to make it structured and ready for statistical analysis, with new (and old) scripting languages like Pig, Hive, and Python. More-specialized open source tools, such as Spark for streaming data and R for statistics, have also gained substantial popularity. The process of acquiring and using open source software is a major change in itself for established busines ...
How to choose the right modern bi and analytics tool for your business_.pdfAnil
We highlight Top 5 Business Intelligence Tools as suggested by Gartner and ask critical questions that can help organizations make better and informed decisions.
Similar to AI for RoI - How to choose the right AI solution? (20)
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Connector Corner: Automate dynamic content and events by pushing a button
AI for RoI - How to choose the right AI solution?
1. Connecting the Enterprise IT Community
in Asia Pacific Countries
APACCIOOUTLOOK.COM
DECEMBER - 24 - 2019
ISSN 2644-2876
$15
AIE D I T I O N
FRASIL
A DIGITAL
COMPANION
FOR PEOPLE
WITH
DISABILITIES
2. A
bhinavSinghalistheChiefStrategy&Innovation
Officer for all thyssenkrupp companies in the
Asia Pacific region. His technical expertise is
responsible for creating and shaping growth
opportunities for organizations. He is actively
involved in leading digitalization initiatives for the group
and is a board member for thyssenkrupp Innovations,
which is responsible for incubating new technology-
enabled solutions for customers in the Asia Pacific. Before
joining thyssenkrupp, Abhinav led the strategy team at
Dell Technologies, where he was involved in defining
Dell’s digitalization strategy. He has also worked with
McKinsey & Company for several years and in his last
role was an Associate Partner, where he advised energy
and industrial businesses on their strategy and operations,
in markets across the world.
Companies looking to adopt AI today are bombarded
with technology companies and start-ups selling advanced
machine learning based solutions built on exciting use
cases. However, before kickstarting newer pilots and
investing in these advanced solutions it is useful to step
back and reflect on the overall intent of using AI for
the organization and the traditional suite of analytical
techniques and resources available.
A recent study by McKinsey & Company compiling
hundreds of AI use cases and applications across multiple
industries found out that just in 16% of use cases advanced
machine learning based AI solutions were only applicable
and traditional analytical methods were not effective. In
69% of the use cases advanced AI methods such as deep
neural networks helped in improving performance of
already established methods and for remaining 15% could
provide only limited benefit.
This is also consistent with our experience that the
greatest potential for AI is to create value in use cases
where already established analytical techniques (such as
regression or classification) can be used, but where neural
network techniques can generate additional insights or
broaden the application base. It is a common pitfall for
companies to get caught up in all the hype surrounding
the advanced AI developments and miss the rationale of
adopting AI in the first place. Oneway, CIOs can assess
the suitability of an AI solution is it to break it down into
simpler elements and ask five basic questions.
1. What is the core business problem to
be solved?
Over two third of the expected value from using AI is
in either revenue generation use cases (e.g., product
recommendation, customer service management, pricing
& promotion) or operational improvement (e.g., predictive
maintenance, yield optimization, supply chain). While
consumer led industries such as retail and high tech tend
to see more potential from marketing and sales related AI
applications, manufacturing and other heavy industries
see more benefit in using AI for operational excellence.
The remaining value pool is distributed across the support
functions for example, task automation, people analytics,
CXO NSIGHTS
AI FOR ROI –
HOW TO
CHOOSE
THE
RIGHT AI
SOLUTION?
AN INTERVIEW WITH ABHINAV SINGHAL, CHIEF
STRATEGY OFFICER, ASIA PACIFIC, THYSSENKRUPP
3. risk assessment etc. Given the wide range of applicability
of AI techniques, it is important to pin down the source
of value creation for the company and then determine the
pain points or opportunities where it makes most sense to
invest in AI deployment.
2. Which analytical method or technique
is best applicable?
Most of the business problems can be classified into few
standard types and have a corresponding set of established
techniques to solve them. Some common examples (not
exhaustive) include:
• Classification (e.g., categorizing products of acceptable
quality)–Logistic regression, Discriminant analysis, Naïve
Bayes, Support Vector Machines, CNNs
• Estimation (e.g., forecasting sales demand or predictive
analytics)–Linear Regression, Feed Forward Neural
Networks
• Clustering (e.g., segmenting customers or employee)–K
means, Gaussian mixture, Affinity propagation
• Optimization (e.g., capacity or route optimization)–
Genetic Algorithms, Differential Evolution, Markov
decision process
•Recommendation(e.g.,nextproducttobuy)–Collaborative
filtering, Content filtering, Hybrid
The most prevalent problem types are classification,
estimation, and clustering, suggesting that developing the
capabilities in associated techniques could be a good starting
point. Also, in most cases, for a specific problem both the
traditional techniques or advanced deep neural network-
based techniques could be applicable and its important to
assess the trade-off between them before selecting.
3. What types of data-sets are needed?
Data sets also play an important role while choosing
between traditional and advanced techniques.They can vary
from being structured or time series based to text, audio,
image or video based. Typically, neural AI techniques excel
at analysing image, video, and audio data types because
of their complex, multi-dimensional nature compared to
traditional techniques. However, sometimes even more
value can be extracted from mining insights from traditional
structured and time series data types rather than going for
audio visual data sets. Building a deep understanding of use
cases and how they are associated with particular problem
types, analytical techniques, and data types can help guide
companies regarding where to invest in the technical
capabilities and the associated data that can provide the
greatest impact.
4. What are the ‘training’ requirements?
Large labelled training data sets are required in most
applications to make effective use of advanced techniques
such as neural networks, and in some cases, millions of them
to perform at human levels. As training data set increases,
performance of traditional techniques tends to flatten and
advanced AI techniques tends to increase. However, if a
threshold of data volume is not reached, AI may not add
value to traditional analytics techniques making it also
an important consideration. While promising new ML
techniques such as reinforcement learning, generative
adversarial networks, one-shot learning are trying to
overcome the requirements of supervised learning, which
requires humans to label and categorize the underlying
data. Companies still need to assess their ability to collect,
integrate, govern and process data at scale before deciding
on the right AI solution.
5. What is the optimum system (or hardware)
configuration?
Depending on the end use case and its requirements, data
can be generated from a variety of sources including sensors,
IoT devices & machinery or social networks. This could
result in massive amount of fast, structured, semi-structured
or unstructured data, time series or real-time which can be
stored in original granularity or aggregated or pre-analysed.
The choices are immense. It’s important to maintain focus
on the ultimate requirements of the end application and
prioritise accordingly. Similarly, the AI application can
reside in the cloud or at the edge to minimise latency.
Cloud typically offers a flexible and scalable environment
at relatively low-cost and without huge initial investments.
Many providers also offer APIs for computer vision, speech
recognition, and natural language processing (NLP) or other
cognitive domains on their cloud platforms which are pre-
trained and preconfigured for a certain task and serve as
gateways to AI applications. All these configuration options
need to be considered before finalising the optimum system
infrastructure to enable the preferred AI solution.
Ultimately, for CIOs to create value from their AI
initiatives, it is important to develop an understanding about
which business use cases have the most value creation
potential, as well as which AI or any other analytical
techniques is most suited to capture that value and can
be deployed at scale across the company. The decision of
choosing the right AI solution is often driven less by the
sophistication of the technique but more by the available
skills, capabilities, IT infrastructure and data at the end for
organization wide implementation.