Holger Mueller - Infosys · PDF fileEnterprise BPO/ITO Infosys Nia Makes Enterprises More...

REPORT: OFFERING OVERVIEW

Infosys Nia Changes the Game for Enterprise BPO/ITOInfosys Nia Makes Enterprises More Efficient,

Powered by Machine Learning/Artificial Intelligence

Holger Mueller Vice President and Principal AnalystContent Editors: R “Ray” Wang Copy Editor: Maria ShaoLayout Editor: Aubrey Coggins

Produced exclusively for Constellation Research clients

June 6, 2017

© 2017 Constellation Research, Inc. All rights reserved. 2

TABLE OF CONTENTS

EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

REQUIREMENTS FOR AI SUCCESS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

THE INFOSYS NIA LINE AGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

KE Y DIFFERENTIATORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

ASSESSING INFOSYS NIA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

RECOMMENDATIONS: FOCUS ON BUSINESS OUTCOMES . . . . . . . . . . . . . . . . . . 19

ANALYST BIO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

ABOUT CONSTELL ATION RESE ARCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22


E X ECU TIV E SU MM ARY

This offering overview introduces the key capabilities

of Infosys Nia, a new Machine Learning- and Artificial

Intelligence-powered platform, which affects the way

enterprises operate.

This report describes the platform’s key capabilities and

evaluates Infosys Nia using Constellation’s four criteria

for Machine Learning (ML) and Artificial Intelligence

(AI) platforms: data management of a large corpus of

data, computing capacity, data science, and time. The

report includes an overview of how Infosys Nia stacks

up against the competition and closes with selection and

implementation criteria that CxOs should keep in mind

when implementing next-generation applications using ML

and AI.

REQUIREMENTS FOR AI SU CCE S S

Constellation views four core capabilities and assets as

being essential to developing powerful AI skills (see

Figure 1):

1. A large corpus of data is the first requirement. It’s not

the case that he who has the most data wins; the goal

is to build the largest graph that maps the connections

Infosys Ltd.

· Headquarters: Bangalore, India

· Founded: 1981

· Type: Public

· Revenue (FY 2016): $10.2 billion

· No. Employees (FY 2016):

200,364

· Website: www.infosys.com

· Twitter: @infosys

Business Themes

New C-Suite

Data to Decisions

Future of Work

Digital Safety and Privacy

Technology Optimization

Next-Gen Customer

Experience


to data. More data should improve the

precision of insights and allow for more

patterns to emerge. Data is used to test

and train algorithms and models, but the

data should be connected in some way so

that patterns and behaviors show up. The

patterns then should provide accurate

recommendations and suggested or

automated actions. The data exhaust of

these systems is also brought back into

the data store to support self-learning and

continuous learning (see Figure 2).

2. Massive computing capacity is the second

requirement and it’s closely tied to the

ability to ingest, store and quickly analyze

data at scale. Public clouds have changed the

scale and economics of computing, making

it possible to tap vast computing capacity on

demand. Winners will own or have access to

vast computing power.

3. Data science, the third requirement, refers

to intellectual property (IP), skills and

experience. The discovery of patterns,

creation of new algorithms and the ability to

apply human intuition to computing require

great math talent. The skills range from the

basics of data management, data cleansing,

integration and transformation to the

ability to mine data and apply advanced

statistical methods as well as machine and

deep learning to any amount of data. IP

includes algorithms, models and related

proprietary capabilities.

4. Time is the fourth requirement and it boils

down to the people-years that can be poured

into research and development. There is

no substitute for time. Early adopters gain

an advantage of time. Algorithms need

time to improve. Companies can try to buy

time by hiring more people or acquiring

Figure 1. Four Requirements for Developing Artificial Intelligence Capabilities

Source: Constellation Research


firms that have already sunk years into

research and development. But successful

delivery of capabilities depends on time

spent generating and learning from data,

understanding computing requirements

and iteratively advancing the math and

data science behind AI-based systems

and applications.

Other emerging and differentiating

requirements of AI include:

• Industry-specific expertise to improve the

relevance of specialized AI systems.

• Natural user interfaces to take advantage of

human voice, visual and gestural interaction.

• Robust recommendation engines that take

the output of AI and present choices that

accelerate decision making.

Figure 2. Continuous Learning Unlocks a Spectrum of Seven Outcomes for AI

Source: Constellation Research


THE INFOS YS NIA LINE AG E

On April 26, 2017, Infosys launched Nia, the

second generation of Infosys ML/AI platforms.

Nia means “purpose” in Swahili, which Infosys

saw as a good name for its purposeful AI

offering for the enterprise. The product is the

update to the Infosys Mana offering.

The Infosys Nia offering has six components:

1. Infosys Mana. Infosys Mana was the

first generation ML/AI platform introduced

in 2016.

2. Infosys AssistEdge. This is the Infosys

Robotic Process Automation (RPA)

product that automates formerly human-

operated processes and other processes

through software.

3. Skytree Algorithms. In the spring of 2017,

Infosys acquired technology and talent from

Skytree, enabling data scientists or non-data

scientists to create machine learning models.

4. Infosys Infrastructure Management

System. This is Infosys’ infrastructure

management system, which manages

physical servers, storage and

networking landscapes.

5. Optical Character Recognition (OCR)

capabilities. Infosys brings internally

developed OCR assets built on top of

requirements from customer engagements.

6. Natural Language Processing (NLP)

capabilities. NLP capabilities come from

organic intellectual property (IP) assets.

While Infosys first generation of ML/AI, Mana,

was mainly focused on IT, simplification,

efficiency and cost, Infosys Nia is expected

to allow Infosys to address new ML/AI use

cases in the areas of forecasting (revenue and

product demand), understanding customer

behavior, deeper understanding of contracts

and legal documents as well as compliance

and fraud. As of May 2017, more than 60

customers have gone live on these capabilities

and there are more than 160 ongoing


engagements with customers on these

specific capabilities.

Constellation Point of View (POV). Infosys

has shown continuous innovation in Machine

Learning and Artificial Intelligence over

the past 12 months. The new portfolio of

products and services should reduce the

cost of implementation, accelerate go-

lives and improve synergies of the offering.

Constellation sees the move as positive.

Constellation will evaluate early customer

references on how well the products and

services are working together in the suite.

The fact that each component has a positive

track record when working standalone is

encouraging. But CxOs making decisions about

ML/AI platforms need to assess each product

within a suite also as a standalone offering,

given the novelty of products in the ML/AI

area. For example, picking an inferior NLP

capability can substantially hamper the overall

success of an ML/AI platform suite.

In May 2017, it was too early to assess the

power of the overall Nia suite, given its novelty.

Based on conversations with clients at Infosys

Confluence, many of the implementations

are still in early stages and need more time

to mature. This is common to all ML/AI

deployments that are data- and knowledge

base-centric, as enterprises discover

challenges in their data quality as well as

algorithm selection. These are challenges that

are addressed over time.

KE Y DIFFERENTIATORS

Infosys Nia is a platform designed for

enterprises seeking to deploy Artificial

Intelligence for IT efficiencies, predominantly

in system management and system monitoring

(see Figure 3).

The Infosys Nia platform has several

capabilities that differentiate it in the

marketplace. Here are some of the key ones:


Knowledge Management (KM)-

Powered AI Platform

Infosys Nia addresses an organization’s

growing needs for more efficient processes

and knowledge-based approaches. The Infosys

first-generation ML/AI platform provided

a starting point for Infosys customers.

By amassing the digital exhaust from log

files, tickets, interactions, transactions,

conversations, images and other digital assets,

Infosys Nia amasses valuable data and signals

in volumes that humans cannot fathom nor

manage. Today, formalized actions for humans

are stored in knowledge bases, the incarnation

of Knowledge Management (KM). The

combination of digital assets, digital exhaust

and KM assets provides the starting point

from which Nia develops and expands upon its

AI capabilities:

• KM powers AI. Infosys Nia has a Knowledge

Management DNA built from its early ML/

AI platform lineage. The system starts

Figure 3. Differentiators for Infosys Nia – Services, IP and Business Model View

Source: Infosys


with knowledge accumulated from years

of working and supporting IT and business

processes with deep domain expertise.

That knowledge powers the Infosys Nia AI

capabilities, enabling the desired “brain-to-

machine” automation.

• Abstraction models processes.

Understanding and abstracting business and

IT processes are fundamental to collecting

the necessary and appropriate information

and outcomes to power an AI platform.

Infosys Nia models processes in a flexible

and adaptable way, allowing the capture of

the relevant information to build the best-

fitting processes and outcomes with the

help of AI.

• Cognitive capabilities deliver outcomes.

Infosys Nia operates a set of cognitive

capabilities on top of the ever-evolving

data fabric underlying business and IT

operations. The result - new insights,

automation and outcomes. Using multiple

cognitive mechanisms simultaneously is key

for superior insights and outcomes.

• AI adds self-healing ability. Infosys Nia

empowers highly desirable next-generation

AI capabilities, such as self-healing actions,

where the AI system acts automatically

when confronted with adverse events. For

example, Nia has the ability to throttle or

slow down an IT system that is operating

in an earlier phase of a supply chain to

compensate for a system further down the

chain not working as expected.

By combining these capabilities, enterprises

have already seen positive outcomes using

Infosys’ first-generation ML/AI platform.

For example, at an apparel manufacturer,

prediction models have reduced IT costs

up to 90 percent. In product development,

the Infosys first-generation ML/AI platform

was able to reduce product lifecycle times

by approximately 50 percent, cutting down

the number and length of iteration phases

between product design and manufacturing.

Constellation POV. Getting the underlying

architecture right for a new category of

software is never easy, especially for AI

platforms. Given the Infosys track record of


established implementations in both business

and IT deployments – the abstraction as well as

automation on the platform - and its delivery

of desired outcomes, Infosys has delivered

60 customers. What sets Infosys Nia apart

is the approach of Knowledge Management

by default. Infosys starts with a live, active

and vibrant underlying knowledge base that

in many cases is actively used by its teams

for information technology outsourcing and

business process outsourcing. This head start

beats starting with a data dump and then

regular data imports that consume valuable

resources and time.

The first-hand experience of its teams for

information technology outsourcing and

business process outsourcing gives Infosys an

advantage. When a product has to be proven

for internal operations as well as customer

use cases, it usually creates a higher validation

hurdle to prove itself. In the case of Infosys

Nia, it would be hard to convince enterprises

to use the platform if Infosys was not able to

show positive returns from its in-house use

cases. Infosys’ outsourcing scenarios provide a

mutual transparency between the enterprise

customer and Infosys. Enterprise CxOs making

decisions on AI platforms can see the benefits

of Infosys’ Nia usage.

Deep Domain Expertise that

Fosters Success

Infosys helps enterprises become more

efficient. In many engagements, the company

has become a partner to the client. The

many engagements over three decades

of operation has helped Infosys create a

deep understanding of successful business

processes, including what makes them

appropriate, compliant and successful. This

expertise has been captured in Infosys

Knowledge Bases for many years.

Infosys Nia aims to achieve automation of

business and IT processes based on this

repository. The focus on Knowledge Bases

gives most AI offerings by services vendors

like Infosys a jump start over AI offerings from

independent software vendors. Infosys Nia is

no exception. Nia can seamlessly operate with


little to no delay on top of the Knowledge Base

that Infosys has built for both customers and

its own internal users.

Constellation POV. Building a software

platform in an area of deep domain expertise

reduces the risks of overall product creation.

Combining that with Infosys’ more than

three decades of human expertise and rich

knowledge base helps to instantly validate

the Nia platform. As support professionals

monitor the system, they will validate

suggestions and actions offered by Infosys’

first generation ML/AI platform.

Infosys professionals working with enterprises

in outsourced situations will be able to quickly

discern if an early implementation of Nia is

working appropriately and creating intended

value for the client. The codified knowledge

and instant validation of Nia help enterprises

have trust in handing over partial or full control

of AI products.

For enterprises, this low-risk approach enables

a trusted handler to monitor Nia actions,

intervening, correcting, and stopping actions as

needed. CxOs who have to approve the go-live

of AI platforms and struggle with putting their

company’s operations (and their reputations)

on the line gain peace of mind.

More importantly, Infosys disrupts itself with

Nia. In this case, Infosys changes from being a

services provider to a combination technology

and services provider, which is a major market

positioning change. Adding capabilities beyond

services means that Infosys can gain a greater

share of clients’ wallets and more integration

synergies with clients. Infosys’ transition from

services to technology-based AI-enabled-

processes makes it easier for clients to shift

from human- to AI-powered services, all

supported by the same vendor.

Constellation remains concerned about

the relatively heavy reliance on offering

services rather than software in Infosys

implementations to date. Hopefully, Infosys

Nia will help shift more of Infosys’ business

toward offering software.


Flexible Licensing that Allows

Scalable Consumption

Licensing is always an area that enterprises

consider when looking for next-generation

applications, as it drives a key percentage of

Total Cost of Ownership (TCO). Infosys offers

three flexible licensing options for Infosys Nia:

1. Standalone license. A stand-alone Infosys

Nia license offers the freedom to deploy

the product on premises or on a compatible

cloud infrastructure.

2. Managed service. Infosys Nia can be set

up as a managed service, in which Infosys

manages the product for the customer

either at the customer site or in the

public cloud.

3. Prebuilt solutions. Customers may

consume Infosys Nia as part of a prebuilt

solution that is embedded into the clients’

existing software – the equivalent of a

runtime license.

Constellation POV. Flexible licensing and

deployment models give organizations

choice. Some customers seek on-premises

deployments because they prefer running

software locally, often motivated by data

residency regulations, data privacy rules,

performance factors and existing datacenter

and server capacity. Not surprisingly, Infosys

offers a managed services option - true to its

own organizational DNA but also matching

the demands and needs of large parts of its

client base.

Open Source DNA that

Targets Innovation

Infosys Nia is built on top of popular and

proven open source technologies like Apache

Spark. The platform could not have been made

available and extended so quickly if not for

open source technology. Open source-based

technology not only can lead to superior

innovation speed, but it also has the

advantage of abundant validation – both by

vendors creating solutions and by companies

using the technology in everyday next-

generation applications.


Open source innovation has allowed Infosys

Nia to “stand on the shoulders of giants”,

as Infosys CEO Vishal Sikka often pointed

out at the Infosys Confluence conference

in 2016. That allows the vendor to focus on

what matters most – domain expertise and

its realization in a modern architecture that

enables greater customer success through AI.

Moreover, the decision to make Infosys Nia

based on open source offers other possibilities

for customers:

• Extending with partners. Given the

popularity of open source, many potential

partner solutions run on the same

foundation, giving both customers and

vendor many partnering options.

• Innovating with academia. Much of the

leading innovation is happening in academia,

and Machine Learning and Artificial

Intelligence are no exceptions. The academic

world uses the same open source-based

technologies, thus making the transfer of

innovation easier to a platform such as

Infosys Nia.

Constellation POV. Open source has leveled

the playing field between traditional software

companies and all other players. By taking

the underlying architecture and operational

necessities out of the equation for building

a new product, open source has allowed an

innovation drive by existing and new market

players like no other technology improvement.

Infosys’ advantage is that it can build on a

modern platform and make years of knowledge

and experience available as a differentiator

without losing time and investment on the

basic blocking and tackling that ultimately all

products require.

On-Platform IP that Accelerates

Implementation Speed

Time to go-live remains a critical aspect for all

enterprise applications as substantial benefits

hinge on timely implementation and smooth

operation. Infosys provides several Intellectual

Property (IP) assets on the Infosys Nia

platform that help enterprises launch quickly.


Infosys packages the following assets with the

Nia platform:

• Adapters. Next-generation applications

generally do not operate as standalone

offerings but are integrated with other

applications used by enterprises. Infosys

provides adapters for the most popular

ones, such as applications from SAP, Oracle

and Salesforce. This allows enterprises

to worry about less in operating and

integrating an AI platform, with the

software vendor taking ownership of

integration and managing updates.

Enterprises can focus on improving

processes and investing strategically.

• Automatic correlations and insights.

While enterprises could build their

own Machine Learning, analytical and

statistical applications, it is always faster

to use something that is already

available. Infosys makes out-of-the-box

correlations and several insights available

on the Nia platform.

• Ontologies. Creating and maintaining

ontologies is a cumbersome and expensive

process for anyone – especially enterprises.

Jumpstarting the ontology availability

process is valuable for enterprises, and

Infosys makes ontologies available to clients.

• Scripts and workflows. Every software

product has several scripting capabilities

and equally needs some degree of workflow

automation. Not having to start with Line

One in these coding efforts is important

for enterprises to achieve an earlier

launch date, so Infosys makes these code

assets available.

Constellation POV. Time to go-live is essential

when enterprises use strategic software

applications that change the way they operate

– internally and externally. Any help to

launching more quickly and more effectively

is of immense value. Ingestion of large bodies

of data has emerged as a significant challenge

with ML and AI systems adoption. Infosys’

systems integrator DNA helps its customers

by expanding the pre-built IP asset offerings

to improve go-lives. This approach helps


differentiate Infosys Nia from competitors that

discover many of these capabilities in a later

phase of the product lifecycle.

A S S E S SIN G INFOS YS NIA

The success of AI platforms hinges on the four

criteria introduced earlier - data, computing

capacity, data science and time.

Data

While more data helps, making sense of

the data and making it actionable is more

important. The KM-based DNA of Infosys

Nia provides a very favorable feature, as

information that comes from the KM-based

system has already been organized, normalized

and made available for both machine and

human interaction. Moreover, through

its business process and IT outsourcing

businesses, Infosys knows what works and

what does not, so it doesn’t have to establish

the business validity of ingested data before

formulating actions. On the voracity aspect,

when putting more data into Infosys Nia, the

flexible deployment of the platform is a key

advantage. Enterprises can decide where

to operate Nia on the whole spectrum of

deployments – from on-premises all the way to

the public cloud.

Computing Capacity

Organizations need to run many models of

the data and of the numerous permutations

on subsets of that data. This key capability of

the AI platform requires a lot of computing

capacity. Often, many models run without

generating much value, until they fire up one

fine day. Access to cheap computing capacity

in the public cloud enables hyper-scaling of

resources as well as subscription pricing.

Data Science

The quality of algorithms and the innovation

on the algorithm front can give enterprises

a substantial leg up on the competition. The

search for the uber-algorithm that can predict

the right algorithm to run on the right subset of

data to automate decisions is in full swing. The


brightest minds in the discipline are working

hard to come up with the first and the best

solutions that use such algorithms.

Infosys has made substantial investments in its

data science practice and has many respected

data scientists on its payroll. But it would be

a surprise if the uber-algorithm came from

Infosys or any other technology outsourcing

firm, as the services DNA is likely too strong

and would overshadow the level of R&D

required. But when it comes to innovation, you

never know what will happen and these new

algorithms may come from Infosys as well as

from anybody else researching them.

Time

It takes time to get all the ingredients for

success in AI right. Infosys is in a good position

when it comes to KM-based AI for business

process and IT outsourcing use cases. The

stakes in this field remain high, though, and all

players need to invest substantially to remain

at the top of the game.

Constellation POV. Across the four criteria

for success in AI, Infosys Nia scores well for

handling use cases related to business process

and IT outsourcing. Business process and IT

outsourcing services will be the sweet spot

for Nia in the near future. Over time, Nia will

have to prove it can provide validated benefits

beyond these use cases.

Infosys is at a disadvantage with data and

computing capacity when it comes to AI

competitors that are also IaaS providers.

Infosys or its customer will have to pay

a premium to procure data storage and

computing capacity, compared to the IaaS

vendors’ native AI offerings. But by making

Nia a multi-cloud product, effectively giving

customers a choice to deploy Nia across a

variety of IaaS providers, neither Infosys

nor its customers should be held hostage to

unfavorable terms by a public cloud platform.

On the flip side, data privacy and residency

requirements are in Infosys’ favor, as the

flexible deployment options of Nia allow the

use of code close to the data – with no public

cloud exclusivity.


On the data science side, Infosys has no

data science “superstars” on its payroll, but

it is less likely that a current superstar will

be tomorrow’s superstar when it comes to

innovative data science ideas. Partnering

with smart minds in academia is a good

alternative strategy.

Finally, Constellation sees Infosys Nia scoring

well on the time dimension. It is certainly in

a very good position compared with systems

integrators as well as with pure software

product vendors. But Infosys must make sure

that it keeps investing into Nia as a product,

resisting the potential organizational urge to

return to being purely a services organization.

Competitive Positioning

The field of ML/AI is rapidly getting

crowded with vendors coming from diverse

backgrounds. It’s important for enterprises

to understand where each vendor started

its ML/AI journey, as there are pros and cons

associated with each point of origin. Here are

some common paths:

• SaaS/ERP vendors shift to AI. This group of

vendors has all the key data of the system

of record and powers the transactions that

create those records. Increasingly, these

vendors are moving away from traditional

business intelligence and data warehouse

solutions to address their customers’ need

for business insights and they are acquiring

and/or launching their first Machine

Learning and Artificial Intelligence offerings.

• IaaS/platform vendors have first-mover

advantage. ML/AI is computing intensive,

and as such, attractive to IaaS vendors.

Enterprises are building next-generation

applications using ML/AI and therefore

expect the platform vendors to allow the

creation, operation and support of these

applications. This group was the first to get

to the market.

• Business intelligence/data warehouse

vendors must make the shift. The vendors

in this group were the traditional software

providers to enterprises, helping them

understand what was happening in their

businesses. These vendors were driven by


human intelligence and talent, but now face

an innovation challenge in making machine-

driven processes real in their products.

• Systems integrators/business process

outsourcing providers seek new

opportunities. BPO providers have talked

about robotic process automation for over

a decade with their customers, seeking

to ensure consistent, cost-effective and

scalable service to them. In the past, this

was usually a rules-based approach; now

ML/AI provides a better and more effective

platform to address these needs. Infosys

falls in this category.

• Startups address innovation gap. Fast-

growing software fields, especially those

with a lot of potential for enterprises, always

attract startups that can tackle innovation

through ML/AI.

Constellation POV. Infosys Nia is well

differentiated among offerings from these five

groups of vendors. As a systems integrator/

business process outsourcer, Infosys enjoys

early-to-market advantages. Compared

to the SaaS and ERP vendors, Infosys can

look broadly at data across these vendors,

something that well reflects the reality of the

installed base in enterprises.

Compared with IaaS/platform vendors, Infosys

offers a complete solution for the needs of

enterprises, not merely a platform on which

they can build a next-generation application

with ML/AI. Compared with business

intelligence/data warehouse vendors, Infosys

is not weighed down by a demanding installed

base that often still requires outdated business

practices. Instead, Infosys can start with a

clean slate to tackle ML/AI.

And lastly, compared with startups, Infosys

has global reach, a blue-chip customer base,

deeper capital investment budgets and, first

and foremost, a service delivery capability that

startup vendors traditionally lack.

However, Constellation believes ML/AI-

powered platforms are in their early days.

All vendors have to address gaps and fill in

offerings quickly as the space quickly evolves

and matures. Infosys must address functional


gaps when moving into other ML/AI use cases

such as customer experience and “know your

customer” use cases.

With its ML/AI offering, Infosys can help

customers automate their processes for better

business outcomes. Infosys’ deep domain

expertise and existing business process and

IT outsourcing business have produced valid

benefits with the first-generation ML/AI

platform, which now becomes part of Infosys

Nia. This means that Infosys Nias a strong

solution of value for business process and

IT outsourcing use cases. When the robotic

process automation of Infosys AssistEdge is

added, Infosys Nia only becomes stronger.

As for additional ML/AI use cases, Infosys

Nia still has to show that it can create value

for enterprises in real-world engagements.

The good news is that these are currently

underway. Before making a systems decision,

CxOs should ask Infosys for customer

references for their specific use cases.

RECO MMENDATIO NS:

FO CUS O N BUSINE S S

OU TCO ME S

Organizations should focus on business

outcomes first. Prioritize which business

problems are the biggest ones.

Identify and understand what the organization

needs to address and to achieve before

considering Infosys Nia or any other ML/AI

application or portfolio. Don’t experiment

with ML/AI before addressing more urgent

priorities and don’t force fit an off-the-shelf

application into a service if the solution doesn’t

promise desired business outcomes.

Consider these factors when selecting an ML/

AI-based solution:

• Organizational fit. The solution needs

to fit the challenges and capabilities of

the organization. The best product in the

market may not be the best product for an

organization because the product needs to

be deeply embedded in the value creation

and service chains of the organization.


• Solution fit. Simply put, it has to work. The

beauty of an ML/AI solution is that if a

business user sees results, he will be very

fast at discerning the quality of the solution

- whether it’s a positive surprise, simply

interesting, or a waste of time. Capitalize on

the business user’s ability to very quickly

perceive if there is value in a solution.

• Landscape fit. The solution needs to fit

into an enterprise’s systems landscape. It

needs to relate to the rest of the automation

portfolio, as organizations don’t want to

create a stand-alone solution. Instead, work

toward an integrated portfolio that elevates

the enterprise’s overall capability and helps

employees make better decisions.

• Cost versus benefit. Measure new

innovative technologies such as ML/AI

with a cost/benefit ratio. Consider all

factors in how the technology supports the

business models.


ANALYST BIO

Holger MuellerVice President and Principal Analyst

Holger Mueller is vice president and principal analyst at Constellation Research, providing guidance for the

fundamental enablers of the cloud, IaaS, PaaS, with forays up the tech stack into big data, analytics and SaaS.

Holger provides strategy and counsel to key clients, including chief information officers (CIO), chief technology

officers (CTO), chief product officers (CPO), investment analysts, venture capitalists, sell-side firms and

technology buyers.

Prior to joining Constellation Research, Holger was VP of products for NorthgateArinso, a KKR company. He

led the transformation of products to the cloud and laid the foundation for new business-process-as-a-service

(BPaaS) capabilities. Previously, he was the chief application architect with SAP and was also VP of products for

FICO. Before that, he worked for Oracle in various management functions - both of the application development

(CRM, Fusion) and business development sides. Holger started his career with Kiefer & Veittinger, which he

helped grow from a startup to Europe’s largest CRM vendor from 1995 onwards. Holger has a Diplom Kaufmann

from University of Mannheim, with a focus on Information Science, Marketing, International Management and

Chemical Technology. As a native European, Mueller speaks six languages.

@holgermu | www.constellationr.com/users/holger-mueller

www.linkedin.com/in/holgermueller


A BOU T CO NS TELL ATIO N RE S E ARCH

Constellation Research is an award-winning, Silicon Valley-based research and advisory firm that helps organizations

navigate the challenges of digital disruption through business models transformation and the judicious application of

disruptive technologies. Unlike the legacy analyst firms, Constellation Research is disrupting how research is accessed, what

topics are covered and how clients can partner with a research firm to achieve success. Over 350 clients have joined from an

ecosystem of buyers, partners, solution providers, C-suite, boards of directors and vendor clients. Our mission is to identify,

validate and share insights with our clients.

Organizational Highlights

· Named Institute of Industry Analyst Relations (IIAR) New Analyst Firm of the Year in 2011 and #1 Independent Analyst Firm for 2014 and 2015.

· Experienced research team with an average of 25 years of practitioner, management and industry experience.

· Organizers of the Constellation Connected Enterprise – an innovation summit and best practices knowledge-sharing retreat for business leaders.

· Founders of Constellation Executive Network, a membership organization for digital leaders seeking to learn from market leaders and fast followers.

www.ConstellationR.com @ConstellationR

[email protected] [email protected]

Unauthorized reproduction or distribution in whole or in part in any form, including photocopying, faxing, image scanning, e-mailing, digitization, or making available for electronic

downloading is prohibited without written permission from Constellation Research, Inc. Prior to photocopying, scanning, and digitizing items for internal or personal use, please

contact Constellation Research, Inc. All trade names, trademarks, or registered trademarks are trade names, trademarks, or registered trademarks of their respective owners.

Information contained in this publication has been compiled from sources believed to be reliable, but the accuracy of this information is not guaranteed. Constellation Research,

Inc. disclaims all warranties and conditions with regard to the content, express or implied, including warranties of merchantability and fitness for a particular purpose, nor assumes

any legal liability for the accuracy, completeness, or usefulness of any information contained herein. Any reference to a commercial product, process, or service does not imply or

constitute an endorsement of the same by Constellation Research, Inc.

This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold or distributed with the understanding that

Constellation Research, Inc. is not engaged in rendering legal, accounting, or other professional service. If legal advice or other expert assistance is required, the services of a

competent professional person should be sought. Constellation Research, Inc. assumes no liability for how this information is used or applied nor makes any express warranties on

outcomes. (Modified from the Declaration of Principles jointly adopted by the American Bar Association and a Committee of Publishers and Associations.)

Your trust is important to us, and as such, we believe in being open and transparent about our financial relationships. With our clients’ permission, we publish their names on

our website.

San Francisco | Belfast | Boston | Colorado Springs | Cupertino | Denver | London | New York | Northern Virginia

Palo Alto | Pune | Sacramento | Santa Monica | Sydney | Toronto | Washington, D.C

http://www.ConstellationR.com

http://@ConstellationRG

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