When Brad Clay became chief digital officer of GlobalFoundries in early 2021, he knew his role would be less about technology implementation and more about process change.

In 2018, the $8 billion global semiconductor manufacturer announced a pivot in its business strategy: The company would no longer develop and produce 7-nanometer and smaller chip technologies; instead GlobalFoundries would focus on producing specialized chips for high-growth markets such as automotive, 5G, and the internet of things.

“When we shifted from commodity contract wafer manufacturing to delivering more value to the device manufacturers, we faced a different set of business problems,” says Clay. “We had to align our business processes with the new strategy.”

Wholesale shift in process management

GlobalFoundries grew up from a collection of companies, each doing things differently, so processes had become siloed and fragmented, with no single person or organization accountable for an entire process. This, along with the change in business strategy, required a wholesale shift to global process management.

Clay, who is also CIO, worked with the IT team to spearhead development of a global business process owner model. “We wanted to understand and define how our processes could be interconnected and work end to end,” he says. “We had to break down the silos that naturally occur between finance, planning, and supply chain, for example. We did not start with an organizational construct; we started with a process construct.”

Once the global process model was defined, the technology team would create two common platforms, one for global processes and the other for data, but they could not put the cart before the horse.

“When you start a transformation, everyone wants a quick win,” says Clay. “We resisted that approach. We spent a year developing, defining, and visioning our new process model before we bought any software. That approach has paid huge dividends.”

Introducing a new role: Global process business owner

Clay and his senior executive peers identified eight global processes: idea to product, hire to retire, order to cash, demand to deliver, source to pay, market to contract, make to order, and record to report. They then identified people for a new role, global process business owner (GPO), which would be the linchpin to the new model.

In companies that have a relatively clear understanding of their global processes, that GPO might be a Six Sigma Black Belt with a continuous improvement lens, but this was not the case with GlobalFoundries. “Because the concept of global business processes was new to us, we needed VP-level leaders to step into the new role. We had to drive the transformation from the top down.”

Taking on a GPO role is not for the faint of heart. Clay needed the newly appointed leaders to understand the magnitude of the change they would drive. “We communicated that this was not a continuous improvement effort where the owner would make the process 5% better,” he says. “Our message was that transformation starts at 50%, and that our leaders had to have the vision and courage to sign up for that level of improvement. Anyone will sign up for 5%; it’s in the margins. But 50% is where people get nervous. That’s a very visible level of accountability.”

Once process owners were identified, they went through training so they could have a common understanding of processes, lexicon, and ways of interacting. “We even did 360-degree assessments on the leaders, because we needed the process owners to be a tight-knit group,” says Clay. “They would be accountable for driving common process through the company in a way that had never been done before.”

Under each GPO are process advisory groups that span the various departments involved in a single global process and have a stake in its improvement. Because a GPO cannot have detailed knowledge about every single piece of a process, these advisory groups are critical to making the global process owner model work.

“The advisory groups ensure that the GPO understands the user stories, and they make sure that everyone knows what is going on with the processes,” says Clay, who also reorganized IT so each GPO has a dedicated technology owner.

With the GPO model in place, Clay and his IT team could now address the challenge of implementing new software to automate the global processes. “We had primarily been using point solutions for specific requests held together by manual effort,” he says. “We had to cross ‘the gap of stranded investment’ and focus on platforms. We replaced pretty much everything — ERP, CRM, PLM, quality management — new software soup to nuts.”

GPO lessons learned

Now that Clay can see the faster decision-making and increased productivity that has resulted from the GPO model and platform architecture, he has some lessons to share.

The first: Transformation is more than software implementation. GlobalFoundries’ GPOs are aware that transformation has two elements: digital enablement and business change, which ensure that your operating model is aligned with the business strategy.

“That is why the GPO has to be a senior person,” Clay says. “The GPO aligns the processes to the corporate strategy and then makes sure that what IT is building into the platform aligns. I believe that digital transformations fail because its leaders miss that duality.”

The second lesson is that when you implement the software, minimizing customizations helps you avoid “fighting gravity.” Clay sometimes gets up in front of his colleagues and drops a rubber ball to make the point that when you choose to veer from a vanilla ERP, for example, you are trying to keep the ball in the air.

“Commercial software was built by people with expertise in business processes,” he says. “When you decide to customize the software, you are deciding about whether you are removing friction or fighting gravity. Our goal is to fight gravity only where we absolutely have to.”

Finally, Clay points to the importance of senior-level support and business engagement.  Early on in the transformation, hundreds of people across the entire global company came together to walk through each process, with each GPO standing up to address plans for their own area. “It was the global process owner explaining how processes are going to change,” he says. “It wasn’t an IT person explaining how SAP works.” That, and the fact that CEO Dr. Thomas Caulfield described the program as “business transformation enabled by IT,” were critical to the program’s success.  

“At GlobalFoundries, we manufacture semiconductor chips in four different facilities across three continents,” says Clay. “But the GPO model was a true transformation, which we had never done. And that’s the challenge of transformation. It’s always unique.”

Business Process Management, Digital Transformation

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
E-mail: anand.singh@tcs.com

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
E-mail: nitin.kumar@tcs.com

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
E-mail: mk.patel@tcs.com

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.