Early adopters of data analytics, Artificial Intelligence (AI), and Machine Learning (ML) tools have found favor in today’s rapidly accelerating digital world. Far from being a point of differentiation, these technologies have become imperative for survival. As businesses that have lagged struggle to play catch-up, a trusted IT partner becomes a critical business asset, helping organizations adapt and thrive in a new world.

Better decisions, faster

Over the last few years, remote working and collaboration have gained significant prominence and are driving factors for many employees and clients. While some businesses struggle with this new reality, others have seen it as an opportunity to improve their data and analytical assets, operationalize, and update their processes.

Today’s business success is all about making better decisions faster. This is where the power of AI and ML comes into play. The ability to analyze data, apply analytics through AI, and then leverage technology to train algorithms that enhance the ML decision-making process is crucial. It’s not just about the technology, but the speed and accuracy it brings to your decision-making process.

With the sheer volume of data available today, it is beyond human ability to gather, analyze, and deliver insight in any meaningful timeframe. Crucially, adopting AI/ML should not be seen as a replacement for human resources but rather as an augmentation of human ability, reducing the risk of human error and ensuring reliable insights.

The goal should be to use data and analytics to increase revenue, improve efficiency, and respond to customer/market trends, driving better decisions that create a competitive advantage. AI-powered analytics can identify patterns, detect anomalies, and predict outcomes accurately, ensuring businesses stay ahead of the curve. Companies that have embedded AI/ML into their core operations can now make real-time decisions based on predictive insights, significantly reducing the risk of errors and inefficiencies.

Adaptation to AI/ML is not just a choice. It’s a necessity. In the rapidly evolving business landscape, the transition to AI/ML is no longer about experimentation but full-scale adoption. It’s the key to avoiding becoming irrelevant in the market. The time to act is now.

Market Trends and Future Predictions

According to Gartner, by the end of 2024, 75% of enterprises would have operationalized AI, driving a fivefold increase in streaming data and analytics infrastructures. Grand View Research states that the global AI market size is expected to grow at a Compound Annual Growth Rate (CAGR) of 42.2% from 2020 to 2027. A McKinsey survey reports that, for the financial year 2024, 58% of respondents agreed that adopting AI/ML in their business has helped increase revenue, and 87% believe it will boost the bottom line majorly in the next three years.

AI/ML is no longer a competitive advantage but is necessary to keep pace with global business. However, it can be challenging to get right, as a Deloitte report states that 94% of enterprises face problems when implementing AI. Common challenges include a lack of data strategy, poor data quality, resistance to change, and insufficient AI expertise within organizations.

Getting the Foundations Right

Before implementing AI in data analytics, organizations need to look at their data and ensure they have sufficient data points for the AI to process. Without enough data points, AI will inevitably be biased toward a particular outcome, which means it will not provide meaningful analytical insight. But things are changing with more and more AI Engines entering the system, such as DeepSeek and kimi.ai. The more competition, the better for consumers.

Quality data is not just a requirement. It’s a cornerstone in reducing noise and bias in the data. This is necessary for more accurate outcomes and reduces the computational power required by analytics. If data is clean and relevant from the outset, it also speeds up the model training process for ML. Therefore, ensuring your data is of the highest quality is crucial for successful AI implementation.

Additionally, companies need to adopt a robust data governance strategy. This includes investing in data lakes and structured data pipelines and ensuring compliance with evolving data privacy laws such as GDPR and CCPA. Data governance improves AI model accuracy and ensures ethical AI adoption by eliminating biases that could lead to unintended consequences.

It is also essential to implement AI in the right place. Not everything needs AI to solve problems; an indiscriminate approach will reduce value and impact. Additionally, organizations must manage the change to maximize adoption and minimize confusion. Employee training and awareness play a vital role in AI adoption. Employees may resist AI implementation without the proper knowledge, fearing job displacement. However, with appropriate training, AI can be seen as a tool that enhances efficiency rather than replaces human expertise.

Industry-Specific AI Use Cases

AI and ML are not confined to a single sector; their applications are diverse and industry-specific. In retail, AI-driven predictive analytics enable personalized recommendations and optimize inventory management. In healthcare, AI assists in early disease detection and drug discovery. Financial services leverage AI for fraud detection and algorithmic trading, while manufacturers optimize supply chains through AI-driven demand forecasting. Even the legal industry is leveraging AI for document review and contract analysis, significantly reducing the time required for due diligence.

Partnerships to Success

The biggest issue is that using AI in data analytics is not just about applying AI models to the data. It also needs to understand the data being captured for analytics while understanding which models would yield the best results. These are not skills that many enterprises necessarily possess in-house, which is why partnering with a reputable technology provider is key.

Maximizing value from AI requires enterprises to focus on the right business lines with the right AI models. An experienced partner can help organizations understand the nuances of data and assist with gaining meaningful insights to drive business capabilities to a competitive advantage. In addition, a technology partner can help organizations understand which areas of the business could optimally benefit from using AI.

AI implementation is not just about technology; it is about business transformation. A technology partner can assist in developing AI roadmaps, selecting the right tools, and ensuring smooth integration with existing IT ecosystems. They also bring expertise in cloud computing, cybersecurity, and scalability—critical factors in AI success. With cloud-based AI services, businesses can scale AI adoption without heavy capital investments, making AI more accessible to small and medium-sized enterprises (SMEs).

The Road Ahead: AI-Powered Business Agility

AI/ML-related solutions offer a promising future for business decision-makers. They can simulate thousands of models and iterations, providing a deep understanding of risk and returns. This level of insight is practically impossible without these next-generation technologies. Therefore, it’s not just about catching up but about embracing the potential that AI/ML can bring to your business.

With AI-powered automation, businesses can enhance customer service through chatbots, streamline operations with intelligent process automation (IPA), and even use AI-powered digital twins to simulate different business scenarios before implementing strategic decisions.

As AI models evolve, ethical AI, explainable AI, and AI regulation will take center stage. Enterprises must prioritize responsible AI use to maintain customer trust and regulatory compliance. Moreover, with the rise of generative AI and large language models (LLMs), businesses will have newer opportunities to automate content creation, customer interactions, and more.

Sarthak Rohal – Senior Vice President, Product Team

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