AIOps – a must-have rather than a nice to have

Where IT is concerned, there’s no longer a valid business case for the old argument of “doing more with less.” The stakes are too high given the tightly connected global economy, the 24/7 speed of business, digital security threats, and their corresponding data protection regulations. On top of that, the shift to hybrid operations has provided valuable flexibility but multiplied potential failure points. Put simply, it’s no longer a question of if your organization needs to fully optimize its IT production environments, but why haven’t you optimized them already?

The only hitch is that effective IT management takes work. Even when nothing is breaking and your data centers aren’t being battered by hurricanes or holiday-driven demand spikes, software always needs to be updated or patched; security certificates need reissuing; and the interns forgot their passwords again. But since you can’t simply hire your way to seamless IT operations, you need to make them less reliant on human intervention. And artificial intelligence is the way to make them more autonomous.

Integrating AI into IT operations, or ITOps, creates “AIOps.” This technique leverages the power of sophisticated algorithms to capture human insights into how your whole IT estate behaves – not just when everything is running smoothly, but what behaviors are early warnings of potential crashes. AIOps can go beyond detecting and diagnosing IT problems to proactively solving or even preventing them, closing the loop without requiring a human to step in.

Quantifying the value of AIOps

Compelling evidence for the value of AIOps is out there. According to a recent Forrester Total Economic Impact study, Digitate’s AIOps technology makes IT operations teams about 60% more efficient – a result of the teams’ increased productivity and ability to scale. The study concluded that a typical company with a small, 10-person ITOps team could save $1.4 million in labor costs (contract or permanent) over a three-year period. For a large enterprise, that figure could be multiplied around 25-50 times.

To take one real-world example, retail giant Walgreens has 9,000 stores and 4,500 call center agents at four locations. During the COVID pandemic, the company would experience sporadic spikes in demand for vaccinations as the number of cases rose and fell. Supported by Digitate’s AIOps technology, Walgreens was able to determine when those spikes were most likely to happen and adjust store hours and staffing accordingly.

In addition, AIOps enabled Walgreens to optimize its Salesforce usage and automate the resolution of IT tickets. As a direct result, Digitate was responsible for resolving approximately 31% of Walgreens’ total IT tickets, along with successfully monitoring and managing 95% of all IT events since deployment.

Clear definitions: The key to successful AIOps implementation

Making the commitment to implement AIOps requires a strategic plan of action, of course. So it’s important to establish the rationale and context in which AIOps will be deployed. What problems are to be addressed? Is there a focus on specific areas or will there be a more holistic strategy? You need to define these requirements clearly, right from the start.

Typically, the first steps required in order to implement an AIOps solution are:

People: It’s important to assemble a project team to agree on the scope of work, set the criteria for potential vendors, and map out the entire engagement and deployment project. Identify a platform owner and executive sponsors, supported by strong IA architects and IA delivery leads. Key deliverables at this stage include:Assessing the maturity of your current ITOps and IT production environment.Assessing the most recurring issues.Building a business case and defining a clear path to ROI.Process: This is often the most difficult step for organizations because IT support usually relies on the “tribal knowledge” of the IT support team. These team members may belong to other organizations, for example, a System Integrator, which could mean the knowledge of the IT support function is not documented locally. Successful implementation requires the team to first:Document each Standard Operating Procedure (SOP) that describes how IT support is provided. This is critical because AIOps tools need to be “educated” on how to perform support tasks.Define and describe what are the organization’s most critical data flows. For example, what is normal and what is not for each observable element? (Such as IT service, traffic volume, or component state.)Technology: Selecting the right solution from the right technology partner is a hugely significant decision, given the importance of the task at hand, the significant investment in resources, time, and money, and the assumed longevity of the relationship with the vendor. Typical considerations here include:Listing the specific challenges and tangible deliverables.Balancing short-term and long-term needs and cost-benefits analysis/ROI.Qualities such as scalability, platform flexibility, and ease of use.Whether to opt for best-of-breed point solutions or a single, unified IA platform that can handle both vertical and horizontal data flows. (I recommend the unified approach, which will facilitate integration points and the adoption of ML algorithms.)Budget: Beyond the licensing, hardware, delivery, installation, and training costs associated with the platform of choice, the team should also consider wider organizational implications, such as change management. For example, they may need to retrain people whose tasks are now managed by IA for deployment elsewhere.

Top-down or bottom-up?

To actually deploy AIOps, there are two general reference models, which we refer to as Bottom-Up or Top-Down deployment. To better understand how these models are applied, Figure 1 below shows possible data flows for an enterprise with a typical technology stack, including ERP and other business applications, with a standard IT maintenance team.


Figure 1: An example of organizational data flow with a typical technology stack

The vertical dimension represents the technical layers needed to sustain a specific solution. The bottom and most fundamental layer is the hardware layer or infrastructure. Above that is the operating system that manages the communication and relationships of applications and hardware.

Above that lies the application layer, representing the actual business applications an organization might use – for example, an ERP suite, CRM system, email, website software, and databases, plus all the middleware or integration tools that connect them. The top layer illustrates the horizontal flow of data from one solution (column) to another.

During each transition this data can trigger actions or decisions – or become enriched for future steps. All these layers, both horizontal and vertical, are constantly communicating among themselves, to keep the whole data flow running smoothly.

The choice of Bottom-Up or Top-Down deployment can be affected by a number of factors. For example:

What is the organization’s operational maturity? Are all stakeholders completely ready for change? Have they successfully captured and prioritized their entire ITOps processes? Are all their SOPs documented?What are the immediate versus longer-term organizational needs? Are there specific areas that they need to address right away? Or are the needs more holistic?How fast is an enterprise looking to transform? Depending on the size, nature and structure of an organization, it might not be realistic to achieve complete transformation at the same time, globally.What is the overall production environment architecture? What are the most problematic IT solutions and is any major change happening in production?What is the architecture for IT support tools, for example, monitoring, messaging, ticket management?Who owns production support knowledge? How available is this knowledge?What is the driver of this transformation?

Based on the answers to these questions, alongside other considerations and rationale, the appropriate deployment model can be selected. Each method has its own benefits and challenges and is best suited to specific scenarios.

Bottom-up deployment model

Deploying AIOps via the “Bottom-Up” model means it is applied at the very foundational levels of the organizational infrastructure IT layer and across all SOPs within that framework. This type of deployment has a longer lead time. However, once all the SOPs have been learned, AIOps can handle any number of typical situations that may arise operationally on a daily basis. Once the SOP learning is in place, AIOps can look at dataflow, how an organization manages master data and start applying organizational use cases to the situations it identifies as actionable.

This methodology requires a bigger investment in the beginning, and it has a slower ROI, but it creates a very solid base that provides broader business improvements over time.

Achieving effective autonomous IT operation support requires the automation of around 80% of all ITOps SOPs, which means achieving the following Intelligent Automation (IA) index target percentages:

50% of total tickets resolved by IA95% of total alerts managed by IA80% of non-ticket support activities resolved by IA

Based on our experience it requires a minimum of 500 IA use cases to be deployed. So, if 50 are deployed each month it will take 10 months for deployment plus two months to set up a program, for a total of 12 months. This is very fast when compared to the average two to three years.

Top-down deployment model

In the “Top Down” model, AIOps is applied to the most critical business data flows first, then automates others one by one. This approach, while providing a faster ROI, is usually a response to a specific problem that an organization has identified. It might create the illusion that the IA journey is no longer needed.

To avoid such a problem, a top-down model requires a carefully planned architecture to fit all data flow requirements into one single IA solution and an equally well-planned deployment strategy, so that each deployment improves the overall Intelligent Automation indexes. Organizations must consider all data flows, not just one, along with having an excellent understanding of just how the different end-to-end data flows connect with each other. While this can create short-term business value, benefits, and ROI, it might also be more expensive in the longer term.

The best of both worlds?

While these two deployment models outlined are very much “horses for courses,” dependent on the reasoning and needs of an organization, they are not necessarily mutually exclusive. As Boston Consulting Group (BCG) stated in its October 2020 report, AI is a Powerful Weapon in the Fight Against IT Problems, “by prioritizing use cases, you can start reaping the benefits of AI quickly — in as little as three months if you know how you want to use AI and can access the relevant data. Contrast that with an all-encompassing ‘big-bang’ approach, where you may wait two years for a grand unveiling.”

BCG goes on to assert that “by prioritizing high-value use cases, you visibly demonstrate the benefits of AI” in the short-term by tackling immediate challenges, which “helps build support and funding for a continuing effort and for the necessary changes to processes and organization. This kind of progressive approach also lets you deploy your target operating model in a gradual, value-driven way. Use cases and operating models develop in parallel and in sync.”

This “hybrid” approach, where organizations can realize value from triaging immediate key problem areas through top-down quick fixes, while simultaneously committing to a bottom-up approach to AIOps deployment can, if carefully planned, present very good options.

CIOs are under constant pressure to provide good news to their bosses and boards of directors, and IT is all too often the favorite target. In such environments, a quick win to solve an immediate issue can spur a commitment to more major changes. A hybrid approach can be a perfect compromise if it is properly planned, explained, and executed.

AIOps delivers proven benefits. Customer satisfaction increases as mean time to recovery (MTTR) and incident management improve. Operational resources are used more efficiently, overall operating costs decrease, and intelligent observation instantaneously flags, and can even pre-empt, potential operational problems. Employee satisfaction can also improve, thanks to the automation of lower-value and often tedious tasks, allied to greater control of operations and empowerment to focus on higher value-add work.

Key to unlocking all of this value is ensuring that the deployment of AIOps is optimized right from day one. The team needs to create an objective view of organizational needs that can prioritize focus areas and choose the correct path to intelligent automation. 

The AIOps journey is a necessary path and organizations must plan how to make it a wanted one, too. Implementing IA at scale is akin to hiking a mountain; the challenge can be great but the rewards and satisfaction are well worth the time and effort.

To learn more about the AIOps journey, visit Digitate.

Devops, IT Leadership, Software Development

Despite a push for automation across businesses of all kinds, the compliance review process for medical, educational, and promotional materials created for healthcare professionals and patients remains largely manual.

As an increased number of drugs are being launched, a proportionately higher number of promotional assets are being created. This high volume of promotional assets, combined with limitations in reviewer skills, can result in an increased time to market. Also, performing repeatable review checks manually can lead to increased costs. Given the growth in the volume of digital content being produced, life sciences companies need a digitized, enterprise-grade approach to prevent errors and accelerate compliance review.

Fortunately, with the right review and approval processes in place, these companies can document every step and publish all the information they need in a timely, compliant fashion.

Why governance is critical in medical review

Medical, legal, and regulatory (MLR) review refers to the process by which life sciences companies make sure that their promotional and advertising materials comply with internal and external regulations and guidelines. This process is non-negotiable for life sciences companies to get their marketing materials to the stakeholders, medical science liaisons, patients, and healthcare providers.

Effective and efficient MLR review processes can be set with a focus on governance and processes right from the beginning. The review framework needs to represent every group involved in the content strategy. The framework also needs to define the systems and processes to manage and review the content. Finally, with therapies becoming increasingly personalized, companies can no longer count on catering to a mass market.

All this creates a demand for a modular content strategy, and a review process flexible enough to manage narrowly tailored content needs.

How the power of cloud can help

Biotech and pharma companies are increasingly turning to technology to manage the robust creation and review processes required for their content. Both established and emerging life sciences companies can benefit by creating a strong content foundation with digital asset management systems.

With a dedicated MLR system, these companies can move beyond the complex paper processes of the past and keep the reviews flowing even when the reviewers are not always at the same location. The solution can use native AWS services, such its Relational Database Service and Elastic File System for storage requirements. It also can be extended to leverage AWS native cognitive services, such as Amazon Textract, Rekognition, and Comprehend. These services are scalable and can process complex tasks, such as text extraction, optical character recognition, and natural language processing.

TCS can provide a critical integration layer, via PromoRev – an AI- and machine-learning-enabled, cloud-based, insights-oriented platform for MLR review optimization. Our secured platform can be integrated with digital asset management systems, helping organizations to accelerate the MLR review cycle time.

MLR reviews have always been crucial for life sciences companies, and this is not going to change. However, the days of executing them on paper, spreadsheets, or PDFs can now be a thing of the past. By embracing a purpose-built MLR review system on cloud, life sciences companies can better manage the process and scale it to make it more efficient as the business grows.

Author Bio 1


Ph: +91 9650422144

Anand Singh is an enterprise architect and senior consultant at TCS Business Transformation Group. He has over 24 years of experience in architecting digital solutions leveraging data, analytics, and artificial intelligence. He holds a bachelor’s degree in Electrical Engineering.

Author Bio 2


Ph: +91 9911115375

Nitin Kumar is the Global Head of Data & Analytics at TCS, BTG. Prior to the current role he was the Chief Digital Officer for the LifeSciences vertical at TCS and was responsible for multiple focus areas within the vertical such as Digital, Analytics, IoT, Digital Marketing etc. He has extensive experience of more than two decades in architecting domain led solutions, innovation driven technologies and large program management.

Author Bio 3


Ph: +91 9223265919

Mihir Patel leads business solutions for LifeSciences and Healthcare in TCS’ AWS Business Unit. With more than 20 years in TCS, he has led many strategic customer engagements globally and provided key solutions to address clients’ needs. His specializations include enterprise architecture and cloud technologies.

To learn more, visit us here.

Artificial Intelligence

Companies and organizations are experiencing the first stage of a new digital support: GDPR management tools. We analyzed some of them.

As for all previous cases of new business compliance processes there is today a growing number of tools in the market addressing the all new European privacy law, the General Data Protection Regulation, which came into force on May 25, 2018. Our main conclusion: these privacy tools have design limitations.

Il problema

In alcuni casi l’approccio della soluzione è tecnologico -sistemi progettati come se fossero indipendenti o di natura statica- mentre in altri casi è funzionale, quindi tecnico in materia di compliance, ancora specifico.

Classifichiamo entrambi gli approcci come principalmente orientati al marketing; non per criticare la qualità di questi strumenti in quanto tali, ma il fatto che le soluzioni sono principalmente opportunità commerciali guidate dallo slancio per una domanda improvvisa, il cui mercato non è ancora esperto in materia. Questa pratica solleva problemi, anzi.

Parlando con gli esperti di GDPR emerge che alcuni imprenditori e dirigenti hanno adottato una visione che limita la conformità al GDPR a una gestione – burocratica – dei documenti o, peggio ancora, sembrano un’operazione one-shot che non richiede manutenzione. Il tutto nonostante i tanti e ripetuti avvertimenti e rischi di incorrere in enormi sanzioni amministrative.

Inoltre, ci è stato confidato che le aziende apparentemente preferiscono processi di business del mondo reale non corrispondenti rispetto alla presentazione di “processi ufficiali” e continuano con quelli abituali. Conclusione: il rischio e lo scopo dell’audit di conformità vengono dissipati nonostante si spenda tempo e denaro e allo stesso tempo con un costo di rischio elevato.

Ritorno al passato

Notiamo un notevole parallelo con gli anni ’90, quando la certificazione di qualità ISO era di moda. Non era raro trovare imprenditori che inseguivano in modo contingente una serie di certificati, senza tuttavia alcuna seria intenzione di cambiare la loro cultura aziendale.

Abbiamo lavorato con un bel po ‘di loro in quel momento e, purtroppo ma non a caso, nessuno di loro aveva illuminato il proprio futuro dopo tali scelte. (Nessuno di loro esiste più sul mercato, ma questo è solo un account personale.)

Tre decenni dopo, la qualità in generale, infine, sembra diffusa in molti ambienti aziendali e la mappatura e la reingegnerizzazione dei processi non sono più una novità. I vantaggi che ne derivano sono riconosciuti come parte della nostra cultura aziendale.

Un approccio innovativo: un’opportunità

Sottovalutare gli interventi necessari per soddisfare il GDPR o non sfruttare tutte le azioni necessarie durante questo processo, può portare le aziende a scegliere strumenti sbagliati che richiedono un serio impegno di conformità. Spesso questa strada porta anche all’impossibilità di collegarsi ad altre aree di competenza fondamentali come Legale e Operativo. Considerato tutto quanto sopra, solleviamo una domanda cruciale:

Perché le aziende e le organizzazioni dovrebbero mappare i propri processi solo ai fini del GDPR? Perché gli strumenti GDPR non partono dai processi gestiti?

Sono disponibili standard di scambio, come IDEFx, FFBD o BPMN 2.0 per la modellazione o standard universali come XML o Json, solo per fornire alcuni esempi. Allora, quanto è comune l’adozione di strumenti di mappatura dei processi?

Questa mancanza di integrazione delle migliori pratiche e degli investimenti precedenti porta a un costoso logoramento.