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AI is embedded in everything that we do: Mohammad Wasim, Group VP Technology, Publicis Sapient

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As organizations strive for agility and efficiency, many face hurdles in effectively implementing their multi-cloud strategies. Integrating AI can empower Infrastructure and Operations leaders to navigate the vast scale of multi-cloud setups. It enables anomaly detection, automates remediation, and enhances key metrics such as response times, incident management, and infrastructure changes. Mohammad Wasim, Group VP Technology at Publicis Sapient, offers valuable insights into how AI technologies like ChatGPT are transforming business operations. From enhancing multi-cloud management to shaping the role of Global Capability Centers (GCCs), Wasim sheds light on the pivotal impact of AI on security, compliance, and DevOps practices.

Some edited excerpts:

How does the integration of AI technologies, such as ChatGPT, impact the management and optimization of multi-cloud environments for businesses?

Organizations have been adopting multi-cloud strategies for many years now, mostly to bring in the much-desired business agility, reduced time to market for their respective end-consumers and making the overall experience seamless and secure. While some mature organizations have been able to manage the multi-cloud environments quite well, there are majority of organizations that are struggling with their strategy implementation and achieve cloud optimization benefits. One of the underlying causes is lack of use of data intelligently – be it at data ingestion stage or using the data for proactively (and predictively) managing the cloud environment.

Dealing with the scale is by far the most important impact of integrating AI technologies. The integration of AI can help Infrastructure and Operations leaders to detect anomaly through understanding patterns in the large data sets, automate remediation and improve key metrics such as time to resolve the network issues, responding to incidents early on and managing change within the infrastructure effectively. AI based tools have capability to help developers in code generation and speed up the overall development processes (e.g., AWS Code Whisperer is AI-powered tool to help developments in using IDE and command line). However, this goes with caveat that Security guardrails and Governance must be strict, so that proprietary data is not exfiltrated and security vulnerabilities within corporate network are not exploited. Thus, while AI is becoming part of everything we do digitally, bringing in efficiency, speed and productivity, we must pay attention to the data protection, while leveraging Gen AI for the benefits it offers.

What is the role of GCCs in facilitating the transition towards AI-driven multi-cloud adoption?

The role of GCCs has evolved over time. Gone are the days when GCCs were responsible for only handling operations. Fast forward to present day, where GCCs are shaping culture and engineering mindset within organizations. The scale of operations in GCCs is so large that managing the scale requires innovative approach in making intelligent decisions on data handling and data processing.

Over the years, GCCs have become innovation hubs that drive R&D and help develop products and services based on industry demands. These centres have been working towards automating repetitive operational workflows, finding ways to improve efficiency and reducing errors. The leaders of various GCCs are now adopting data driven decision making through the use of AI and ML based platforms (also called AIOps platforms). According to Gartner’s Market Guide for AIOps1, AIOps platforms are usually shortlisted for one or more of the operations use cases, such as anomaly detection, event management and correlation, root cause analysis, log analysis, behaviour analysis and centralized dashboarding. In a way, GCCs really help to bring in process re-engineering mindset, become not only testbed for new ways of working, but also develop use cases, model a retail space (for example) and eventually shape the culture of organization, there by influencing how other BUs within the organization engage and work with clients.

In what ways does AI enable GCCs to enhance security measures and compliance protocols when operating within a multi-cloud environment?

AI helps organizations to enhance security measures and compliance protocols in number of ways, such as
Security Information and Event management – Using AI in SIEM solution can help team to automate manual processes developed for threat detection, incident management and reporting, thus extending typical SIEM solution beyond log management capability.

AI enables IT and infra management teams to become dedicated centres to understand nuanced security and privacy policies of the organization to fine-tune the front-end services. Since GCCs are turning into innovation hubs, the teams within GCCs that are working on developing IP and accelerators, can focus on ensuring that the use of AI is no longer a black box. These teams can help to ensure AI governance and encourage the use of AI in ethical way to stay compliant with data and security regulations around the globe.

Teams in GCC, working on specific and niche solutions can promote use of AI ethics through the skills, competencies and Responsible AI principles (such as Fairness, Explain-ability, Privacy, Security etc.) offered by leading CSPs such as AWS, Azure and GCP.

What benefits does a multi-cloud strategy offer for AI/ML projects, particularly in terms of cost optimization and efficiency?

Multi-cloud strategy offers multiple advantages for AI and ML projects, and it helps to overcome many challenges, such as: Complex technology environment and vendor lock-in risk; Disparate and de-centralized deployment
and data security and guardrails

Having a platform or a tool that works across leading CSPs helps organization to navigate beyond the realms of single vendor and foster innovation using best-in class features without the worry of integration challenges that may come up. Specific to the above-mentioned challenges, such solutions can help address these challenges by:
Assessing, testing and fully integrating with leading technologies, allowing organizations to focus on deployment and value creation.

Offering best practices on logging, monitoring and guardrails be consistently applied across all use cases deployed.
Offering robust data security, with the ability to ring-fense the data in enterprise network.

Thus, AI and ML can help the cloud team address key challenges of managing at scale, build automated workflows and improve collaboration.

What impact does AI have on the evolution of DevOps practices in multi-cloud deployments, and promoting collaboration between development and operations teams?

AI is embedded in everything that we do and it is becoming visible in every aspect of software development and operations. Impact of AI in DevOps can be felt through efficiency and speed (of SW development and delivery), automation in testing, security (real time alerts) and optimization of cloud resources.

Tools such as Pilot, Code Whisperer have reduced the time it takes to create business logic and propagation to production environment is swift, allowing the team to produce digital assets quickly.

AI helps in automating CI/CD pipeline. By leveraging AI-powered monitoring and management tools, DevOps teams can automate routine tasks, predict performance issues, retract errors quickly, and optimize resource utilization across diverse cloud platforms. AI-driven solutions help DevOps teams to dynamically allocate resources, detect anomalies, and enforce compliance across multi-cloud deployments. Thus, DevOps teams are in a better position to get actionable insights and have intelligent decision-making capabilities in multi-cloud environment.

AI technologies can help build automated workflows and improve collaboration and experiment tracking. AI powered tools can even enable teams within organizations to develop and manage large-scale AI/ML models.
It can also be used in automating testing processes. Think about AI powered tool that can provide comprehensive quality frameworks to help organization to accelerate the delivery of their cloud-based engineering projects. This can reduce time for go-to-market and delivering tangible value to our clients faster.

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