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That the contents of third-party articles/blogs published here on the website, and the interpretation of all information in the article/blogs such as data, maps, numbers, opinions etc. displayed in the article/blogs and views or the opinions expressed within the content are solely of the author's; and do not reflect the opinions and beliefs of NASSCOM or its affiliates in any manner.

We all know- artificial Intelligence is no longer a future concept. It has become one of the most important technologies shaping how businesses operate, compete, and grow. However, while many organizations understand the potential of AI, the bigger challenge is moving from AI experiments to real business impact. Successful AI adoption requires more than just technology. It needs the right infrastructure, expertise, data strategy, and implementation approach.

That the contents of third-party articles/blogs published here on the website, and the interpretation of all information in the article/blogs such as data, maps, numbers, opinions etc. displayed in the article/blogs and views or the opinions expressed within the content are solely of the author's; and do not reflect the opinions and beliefs of NASSCOM or its affiliates in any manner.

Artificial intelligence has moved beyond chatbots and virtual assistants to become an integral part of enterprise operations. As organizations seek smarter ways to automate customer interactions and streamline workflows, Salesforce Agentforce Service is emerging as a powerful solution for AI-driven customer service and business automation.

Construction companies in Saudi Arabia are working in a market where project ambition is high, but execution control decides profitability. The work itself may be clear. The harder part is keeping every commercial, procurement, site, subcontractor, and finance decision connected across the full project cycle. A BOQ does not remain static after award. Quantities change. Supplier rates move. Site requirements shift. Subcontractor claims come in phases. Client billing depends on certified progress.

Introduction When HR automation first entered the enterprise, its purpose was clear: reduce paperwork, eliminate repetitive tasks, and improve operational efficiency. While those objectives remain relevant, they no longer capture the full value of automation. Today's business environment demands faster decisions, greater workforce agility, and stronger alignment between people strategy and business goals.

Cloud waste is quietly draining budgets across the industry. Industry estimates, including data from FinOps Foundation and Flexera, suggest that a significant share of cloud spend, sometimes cited at around 30%, is lost to idle resources, overprovisioned servers, and poor cross-team visibility. For SaaS companies in particular, where margins are tightly tied to infrastructure efficiency, this isn't just a finance problem: it's a product and growth problem.

That the contents of third-party articles/blogs published here on the website, and the interpretation of all information in the article/blogs such as data, maps, numbers, opinions etc. displayed in the article/blogs and views or the opinions expressed within the content are solely of the author's; and do not reflect the opinions and beliefs of NASSCOM or its affiliates in any manner.

As businesses expand, managing customer information becomes increasingly challenging. What starts as a simple spreadsheet for tracking contacts and sales often grows into a complex system of multiple files, manual updates, and scattered data. For many startups and small businesses, Excel is the first tool used to organize customer information. It's familiar, affordable, and flexible.

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming how Indian organizations solve business problems, automate operations, and deliver digital services. From fintech and healthcare to manufacturing, retail, and logistics, AI-powered applications are becoming integral to innovation strategies. However, successful AI initiatives rely on more than sophisticated algorithms—they require infrastructure capable of handling data-intensive and compute-heavy workloads.