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.

1. Connecting the Enterprise IT Community
in Asia Pacific Countries
APACCIOOUTLOOK.COM
DECEMBER - 24 - 2019
ISSN 2644-2876
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AIE D I T I O N
FRASIL
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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.