Three step model to operationalize Scale AI | HCLTech

3-step model to operationalize Scale AI

How Scale AI allows you to adopt an outcomes-first strategy that improves data liquidity and ROI
10 minutes read
Andy Packham


Andy Packham
Chief Architect and Senior Vice President, Microsoft Ecosystem Unit, HCLTech
10 minutes read
3-step model to operationalize Scale AI

Work backward to move your organization forward

represents a significant shift in how organizations can approach — and benefit from — AI technologies. AI has long extended beyond the domain of data scientists and IT experts. We are seeing a democratization of AI across the enterprise, and this has implications for how to successfully operationalize AI. 

An effective Scale AI vision combines learnings from data and making predictions with creating new unique solutions from that learned data — e.g., . Together, these technologies enable a value-driven strategy to be deployed that can be scaled more effectively across your enterprise. It takes your desired business outcomes as the starting point and then works backward to determine the best-fit data solutions and AI applications.

Shifting from a data-first to a customized outcome-driven approach not only supports your organization’s specific goals but also amplifies business value and increases project ROI.   


A cleaner approach to data liquidity

Data is still paramount, but the focus has shifted. It’s about getting more value out of your data by identifying use cases first and then cleansing and preparing only the relevant data, rather than expending unnecessary resources on structural work related to its state. This streamlines data liquidity investments and aligns projects with specific business goals with a recognized value.  

70% of investments are yet to deliver value.

We know that data quality is crucial to leveraging AI. Yet, investment in digital transformation initiatives has largely focused on modernizing the application environment, with the result that data infrastructure hasn’t kept pace and data remains mainly in legacy architectures. CIOs are understandably cautious about how to cost-effectively modernize platforms. Scale AI helps overcome challenges relating to this by applying generative AI to avoid spending time and resources on bringing quality to data that ultimately may not be used and to accelerate the data liquidity process efficiently.

HCLTech’s expertise — fused with — springboards this process to quickly generate transformative value and meet your desired business goals. This holistic approach to Scale AI combines engineering capability with modern data and AI technologies to improve outcomes and deliver measurable value. 


To help you understand how we approach operationalizing Microsoft technology for Scale AI, we developed a three-step model:

  1. People: the importance of collaboration between humans and machines
  2. Platforms: modernizing data effectively and responsibly for AI readiness
  3. Processes: organizational change to deliver AI optimally


Three-step model to operationalize Scale AI

Scale AI is an opportunity to change how you approach and implement AI technologies by starting with your desired business outcomes and then determining the best-fit data solutions and AI applications. There are three key areas to consider.

Step 1: People

Microsoft generative AI and traditional AI are centered around a deep collaboration between humans and algorithms. Technology alone cannot solve our challenges. People determine the business goals and desired KPIs as the starting point for an effective Scale AI strategy. They also need to explore the responsible considerations around generative AI — and emerging AGI — and determine levels of responsibility, trust and validation, which are a key part of your AI implementation. 

8% of organizational challenges can be solved by technology, while the other 92% are related to people.

As these technologies become embedded in new processes, users are becoming increasingly cross-functional, too. This brings greater focus to upskilling the workforce and creating organization-wide AI literacy initiatives that are introduced in tandem with new Microsoft technology. Humans are key to your AI journey; they must be directed appropriately to meet your desired end goals.

Takeaway:  Human involvement is crucial to unlocking the generative AI opportunity of Microsoft technology. 


Step 2: Platforms

Modernizing data infrastructure by shifting to robust platforms built on Microsoft technology that leverage AI tools and frameworks enables a more streamlined and resource-efficient approach to technology upgrades. We know from our clients that data readiness is a challenge — with 70% of time spent cleansing, preparing and improving the quality of data that may not even be relevant. We combine Microsoft's comprehensive AI portfolio with our engineering capabilities to deliver a fully integrated data pipeline solution built on defined opportunities and aligned with your enterprise goals.   

Poor data quality costs companies an average of $12.9 million annually.

This means you no longer must begin every AI initiative with system integration, data quality or data preparation work. By clearly defining your commercial goals and identifying the necessary datasets, you get a best-fit Microsoft technology solution that improves resource efficiency across your projects and delivers quicker outcomes. 

Takeaway:  Robust, modern data and AI platforms accelerate enterprise data maturity.


Step 3: Processes

To successfully introduce AI technologies into your organization, processes must change. There are multiple aspects to consider — including transforming entire business practices, building wider system integration, outlining new KPIs and continuously monitoring data, as well as governance and culture. Across all of these, it’s about embracing an agile, iterative mindset to create adaptive strategies that can pivot to incorporate new and evolving technologies. 

Setting up processes that track the impact of implemented Microsoft AI solutions back to business objectives measures the impact of your Scale AI implementation. Have the original KPIs been met? Do you need to adjust processes? A continuous feedback loop drives improvements, identifies changes and has the potential to create new business opportunities. 

Adapting processes to incorporate your AI principles provides clear governance and guardrails and helps reduce risks, making Scale AI more responsible. These principles need to be reflected across technical aspects — such as AI system design and monitoring data bias — as well as in non-technical areas, including decision-making, training, human-in-the-loop. 

Takeaway:  Adaptive, multi-dimensional strategies accommodate evolving AI technologies offered by Microsoft.


Accelerate your data modernization

Reliable data is the foundation that moves your organization from AI hype to reliable — and tangible — business value. HCLTech helps you overcome the complex data challenges that can hinder the adoption of generative AI and AI technologies by leveraging the AI and data capabilities of Microsoft. We fuse our proven engineering expertise with Microsoft solutions to accelerate your enterprise data maturity and modernize platform infrastructure to align the benefits of generative AI and AI to your business outcomes. 

Share On