Captures a roadmap as to how Post Pandemic, Organizations Like CGI can in grow in ITOM automation space considering they have existing IP and experience IN RPA , BPM space

1. CGI UNIFY 360AI
Challenges facing ITOPS – in the new Normal era
Post Pandemic, there has been a mindset change w.r.t the way many organizations work
resulting in a radical change where remote working is encouraged mostly but not limited
to IT organizations.
This paradigm requires a robust IT backend infrastructure along with high availability, zero
downtime and auto correcting mechanisms in place. With on demand computational power
being available with the advent of solutions like AWS, Azure and GCP, most of the new age
software solutions are moving to a Cloud native mode or a hybrid mode. Still many legacy
solutions are running on OnPrem and hybrid mode as part of their transition plans. So, the
opportunities for ITOM automation we are looking at can be:
- Handling Uptime needs + Application Monitoring of OnPrem computational
Infrastructure.
- Uptime needs + Application monitoring of Hybrid computational infrastructure
- Application monitoring/Uptime of Cloud native Applications.
The advent of AI/ML solutioning coming as AIOps which is bringing in predictive/preventive
maintenance and Auto rectification to the ITOMS space.
Why AIOps and Why CGI should Look to grow its’ ITOM solution space.
MarketsandMarkets estimates the global AIOps platform market size to grow from $2.55
billion in 2018 to $11.02 billion by 2023, at a Compound Annual Growth Rate (CAGR) of
34.0% during the forecast period. StackSpace and other players are already in this segment
partnering with Accenture to capture the AIOps market.
Hence CGI should look at this growing space and take a leap from its existing RPA and
BPM solutions to reposition as a AIOps service provider.
CGI being in the ITOM space for the last 40 years with its huge repository of data &
Expertise is uniquely positioned to leverage its advantage and be a key player in this huge
growth opportunity.
The Entry barrier will be small compared to its competitors as CGI will have access to huge
operational date from its long service history and the existing client base spread
worldwide.
Strategic Advantages vs Competitor Landscape.
Let’s do Porters 5 Stage analysis to understand this in more detail.
Intensity of the competitive Landscape:
Agreed the Competition is intense with players like Broadcom, MicroFocus, Moogsoft,
etc. with lower entry and Exit barrier, the real winner will be who can bring in better
actionable insights and intelligent automation and can help its clients in desired business
outcomes like accelerating business growth, increasing the bottom line and enriching the

2. customer experience. CGI can enter into partnership with high tech and low market share
organizations such as StackSafe and try to capture the market early.
Access to Market for New Entrants:
With its Huge client base CGI has already a huge advantage where in it can push its
AIOps solution. New players will have a disadvantage unless they are organizations like
ServiceNow which has a huge captive market.
Capital requirements are not abnormally high and new entrants will need
partnerships to enter into the market as they lack the data to train their AI Models and
optimize them, the expertise CGI has over its competitors is its’ huge Knowledge base of
existing issues and resolution workflow which can help in building automated resolution
workflows.
Supplier Power/Hold
This will not be an issue as CGI is servicing clients over 40 years and a diverse set of
clients spread across multiple domains and these data sets will help in building a right
solution the first time and training them will not be a challenge.
Buyer’s Entry/Exit Costs
CGI shall focus on the robustness of its Solution and continuous learning to refine the
solution, and publish the Business metrics to its clients, Once Clients see the ROI across
multiple domains, CGI can leverage this advantage to scale not only within existing
domains, but also towards upcoming, newer business areas.
Existing Solutions and AIOps can be bundled as a unified solution so that Buyers exit
costs will become substantial for them to exit.
Threat from Substitutes
This will be minimal as manual solutioning is costly, takes humongous time to resolve
when it is a complex issue and experience sharing is risky.
To conclude, though the competition is substantial with no clear winner in AIOps segment,
the key lies in an organization who can help its clients in desired business outcomes like
accelerating business growth, increasing the bottom line and enriching the customer
experience. CGI with its existing client base is uniquely positioned to start early and not
only retain the existing customers but also looking at organically/inorganically growing.
As we can see above, the competition plays in segmented space, as they play in a
specialized area, CGI can acquire or partner with any data agnostic tool vendor build AI

3. models by sharing its huge repertoire of Data across domains and focus more on event
correlation and auto-remediation.
AIOps segmentation & Prioritization:
While stand-alone AIOps tools can flag critical event patterns, CGI shall aim at service-
centric AIOps solution which combines data, context and insights for end-to-end incident
management. Clients should be able to handle incident workflow activities like event
prediction, Event recognition, impact analysis, root cause identification, incident
escalation, and automated remediation in a single place.
CGI should take a holistic approach to address the problems of alert fatigue, constant
firefighting, and IT staff burnout with a comprehensive IT outage lifecycle solution. These
4 feature segments may be targeted for the strategic roadmap.
Service Context. This is all about understanding and presenting contextual relationships
for applications, infrastructure resources and operational aspects of the business service.
Domain-based discovery, topology models and service maps to present holistic visibility
and predict business impact for IT event stream analysis.
Intelligent Alerting. Actionable insights using relevant metrics for both legacy and modern
workloads separately need be built and delivered. Dynamic, change-based and forecast-
based alert thresholds help IT teams understand the true state of dynamic infrastructure
resources and generate alert whenever there is a significant change to the dynamic infra
or going beyond a predetermined threshold.
Event Correlation. Both native and third-party alerts generated by existing tools need to
be processed and correlated with the topology and right context and optimized events
should be generated to reduce process overloads. This needs to be built with the right
inference models for analyzing, deduplicating and reducing event volume with data
science techniques and machine learning algorithms.
Automated Remediation. Building programmatic remediation for well-defined IT
operational issues and invoking the right responses (reboot a server or restart a process)
with software-controlled actions. IT OPs teams have the option of resolving incidents with
automated actions wherever possible so that human operators have fewer events to view,
analyze or triage.
Before we prioritize:
Let’s understand the pain points of our target customer segment, to get the right
perspective.
There are 2 segments primarily:
1. The Business owners/Shareholders/Leadership who get directly impacted due to
application downtime, Cost overruns and revenue impact.

4. 2. IT Ops team who gets swamped by multiple incidents spanning multiple areas and
are constantly on a 24/7 mode support depending upon the nature of the business.
The first category may be the decision maker and the second may be the consumer
of our solution, but the pain point is the same. Reduce downtime and minimize
triage so as to minimize the Business impact.
Hence to prioritize among the above 4 features, we also need to take into account
the latter 3 features cannot exist without the First step which is Service Context
Building. Hence by factoring Effort, Cost and Impact and Dependency the following
might be what we can bring into the development pipeline for the next 6 Sprints.
Epics sprint1 Sprint2 Sprint3 Sprint4 Sprint5
Service
Context
BuildingTool
Domainspecific,
Language specific
Bot whichcan parse
and buildatopo,
service Layering
map,start withhigh
impactcustomers
such as Telecom,
Ecommerce
Extendtoother
domainsandother
languages
Intelligent
alerting
Train modelsto
generate actionable
Insights,withspecific
logsor data points
Correlate withService
Contextandhave
more meaningful
insights
Retrain-
Optimisation
Cycle
Event
Corelation
trainmodelsto
understandrelateble
eventsacross
subdomainssuchas
infra,DB, Application
behaviour
Filterduplicate
alretsand
generationof
an unified
Incident/Event,
more data
pointsfor
learning
Auto
remediation
Building
Signature
eventsand
their
corresponding
remediation
workflow
Categorise what
can be
automatedand
Buildthe
necessaryflow
may be a rest API
suite,whichcan
be pluggedinto
diverse APM
Tools

5. Pricing and Launch:
Since CGI is yet to be a market leader, it needs to go for a value-based pricing for
its SAAS solutioning and may look at bundling this as an upgrade to its exiting ITOM
solution for existing customer base.
For existing customers, it has to follow a competitor-based pricing until it sees an
organic growth and can bundle these solutions as follows
Option 1: Service Context Building + Intelligent alerting: $ X /Month
Option 2: Service Context Building + Intelligent alerting
+ Event Optimization : $X + a /Month
Option 3: Service Context Building + Intelligent alerting
+ Event Optimization
+ Auto Remediation : $X + a + b /Month
AI based solutions will start adding Values as the solutioning grows over Years,
hence quarterly releases may be planned, but as this will be a SAAS based
solutioning, the launch should not impact the Clients.
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