implementing an AIOps

Having previously written on various advantages that an autonomous monitoring solution brings to the table, we wanted to focus this post on how to implement such solutions with an aim of maximising ROI and minimising friction.

This would allow teams to smoothly transition from a manual and chaotic way of troubleshooting to one which is driven by science thus increasing the QoS.

Best practices to successfully implement an autonomous monitoring solution
Pun intended (We know its not your troubleshooting process)  Source: 

However, let us be honest here. Although there is a general consensus on how AI can improve autonomous monitoring practises and the potential value it can bring, proper implementation is key to maximise the ROI. 

Each company has its own particularities and challenges when it comes to managing IT operations: business needs, technologies/tools used, nature of the infrastructure – single/multi cloud, on premise, managed services, organisation of teams, processes etc. 

While choosing a solution and implementing it all of the above factors need to be considered along with a proper project management plan to make sure all stakeholders are aligned, milestones and timelines fixed and expected results pre-defined. 

Although we are very confident in the abilities and the technical prowess of our solution,  we’d be wrong if  we say – “Deploy our solution and get X ROI or Y benefits” without understanding your business needs, architecture design, technology stack and organisational structure. 

We strive to work with our clients and deliver long term value rather than make the sale for a quick buck. And delivering long term value is more of a partnership of sorts rather than a purely transactional sale. Thus before signing a commercial deal, we always propose a Proof of Value (duration and pricing differs depending on client use cases) – during which our clients can test our solution, validate the results internally and then move on to general deployment of our solution. 

Below are the 4 steps we suggest companies follow in order to achieve operational excellence and maximise ROI of any AI based monitoring solution. 

Step 1: Understand your pain points.

Start by listing your main monitoring pain points and all the negative consequences that result from it. 
– “How are things working now ? Are your teams spending a lot of time setting up rules and thresholds to create monitors and still missing on key incidents ?”.
– “Are teams receiving alerts from different sources and co-relating them takes hours thus impacting customer service ?”.

Step 2: Define priorities.

What are the 3 most important objectives that you would want to achieve ? What does success look like ? Then for each objective set KPI’s that will clearly establish whether or not the expected outcome was reached.

Some examples of objectives that our customers have set are: 

  • Reduce alert noise and incident tickets by 25% year over year 
  • Reduce MTTR by 30% in Q4 2020  vs. Q3 2020 
  • Reduction of system impacting incidents (P1) by 25% year over year

Step 3: Calculate your expected ROI.

This is an extremely important step which may not seem simple to calculate or avoided due to the perceived complexity of the task. 
Some may consider it as a marketing gimmick but experienced IT managers would tell you the importance of establishing a clear expected ROI. It is the figure that would help you align internal stakeholders, clearly define success criteria and get budget approvals.  It also makes sure that the solution provider delivers to their promise and the deal is based on a mutual understanding avoiding unexpected surprises.

Step 4: Start with a small perimeter and gradually expand.

We always recommend that our customers start with 1 or 2 (few) critical applications and master the workflows from incident prediction to remediation. Teams can then expand the perimeter and move to clusters of other critical applications or other logical blocks of the infrastructure.  
Starting with a limited scope allows you to better measure outcomes, adapt workflows, document new processes and create advocates within your team fostering a great working culture.

Finally, as your partners we would be available throughout the journey to make sure the deployment is a success. Also the plug and play nature of our solution allows easy installation and gives you complete autonomy over operations without the need for add on consulting services that can potentially slow you down. 

We hope this was helpful, for any questions you can reach us at and our team will get in touch asap. Thanks for reading!

PacketAI is the world’s first autonomous monitoring solution built for the modern age. Our solution has been developed after 5 years of intensive research in French & Canadian laboratories and we are backed by leading VC’s. To know more, book a free demo and get started today!

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