In an exclusive email interview with CIOAXIS, Kapil Mehta, Vice President – Data and AI, Visionet, shares his insight on the technological aspects of Enterprise Data & Artificial Intelligence…
Q1. How can business leaders use proven frameworks, outcome-focused thinking and powerful data & AI technologies to create accurate, accessible and actionable insights that help organizations achieve their business goals?
For today’s business leaders, the challenge is no longer data collection but converting it into something meaningful—insights that can directly influence business outcomes. This is where proven frameworks like CRISP-DM (Cross-Industry Standard Process for Data Mining) or data governance models come into play. These frameworks offer a structured approach to ensure strategic yet repeatable initiatives are embarked upon by data initiatives. They guide everything from data collection to implementation with a focus on actionable, measurable outcomes.
When we talk about outcome-focused thinking, it means each data-driven initiative is tied to specific business goals. So, if the goal is to improve customer retention, then we’re looking at churn prediction models or personalized engagement strategies. For example, a recent report shows the organizations that align their data strategy with business outcomes are 1.8 times more likely to have a high return on their AI investments.
The role of technology is pivotal here. Tools like predictive analytics, natural language processing (NLP), and real-time dashboards help leaders extract actionable insights from complex data sets.
That said, technology is not the only concern, but rather the various measures we can take to ensure accuracy and accessibility are also important. For example, embedding strong data quality checks and using self-service BI tools can help the teams to make data-driven decisions with minimal or no dependency on IT. According to a survey by Forrester, 74% of decision-makers believe that their teams are more productive if data is readily available through such tools.
Q2. What should CIOs and business leaders look for when assessing whether to incorporate AI and data into their technology strategy?
Reports indicate that organizations that effectively integrate AI into their core strategies have experienced improvements in profits of as much as 20%. However, if data and AI need to be integrated into a company’s strategy, the first step that the chief information officer and business leaders must make is to decide if it aligns with the business goal. AI initiatives must directly support main objectives such as efficiency gains, innovation, or revenue growth.
As a second step, consider data readiness. For this, try asking questions such as, “Is data accurate, accessible, and structured for AI applications?” Because studies have revealed that the poor data quality costs businesses around $15 million annually.
Scalability is another major factor. AI solutions must evolve with the business needs. Flexible platforms can ensure investments remain relevant as technology and market demands shift.
Furthermore, it cannot afford to ignore the ethical and compliance requirements. Using AI in a responsible manner, with respect to all frameworks like GDPR or local standards, is essential for risk mitigation and trust.
Also, always make sure not to underestimate the human factor; in this case, having the right talent combined with a data-driven culture. Studies show that AI can indeed unlock trillions in economic value, but the talent for executing those strategies is paramount. And, of course, there must be a clear ROI roadmap to justify and prioritize investments effectively.
Q3. With the convergence of data and AI, how can organizations improve business performance and efficiency for analyzing consumer behavior and automating customer segmentation?
When data and AI are brought together, they have the potential to transform business understanding and interaction with customers on a massive scale. It begins with consolidating data from multiple sources to get a complete, unified view of the customer—think building a single source of truth. Once that’s done, AI can work its magic.
For instance, AI analytics identify customer behavior patterns so that decisions could be proactive, machine learning can dynamically segment customers using real-time data, and NLP helps in analyzing data from unstructured content like social media, thereby automating the processes. This automation reduces manual work, improves accuracy, and leads to more targeted marketing and better ROI, thus increasing business performance.
Q4. How can InfoSec leaders assess the effectiveness of AI and data in cybersecurity solutions, ease of use and its trustworthiness?
When assessing AI and data in cybersecurity, InfoSec leaders should look at key performance metrics like the threat detection rate, response speed, and the system’s ability to identify emerging threats like zero-day vulnerabilities. Metrics like detection rates, false positives, and mean time to detect (MTTD) offer you quantifiable insight into AI’s effectiveness.
Moreover, ease of use is equally important. Solutions should integrate with tools that employees already use, be an easy dashboard to understand, and automate routine tasks like log analysis so that the workforce is left free to focus on more challenging issues. Concerning trustworthiness, transparency and explainability are key considerations—leaders should assess how well the AI can explain its decisions and ensure that they are in line with ethical standards of AI.
Also, to ascertain reliability and security in a cybersecurity solution, it’s important to perform regular audits and real-world testing. Solutions should be validated against real attack scenarios, and compliance with regulations such as GDPR and CCPA is also a must toward maintaining the right level of privacy. Trust is built through strong data governance and privacy through AI training.
Q5. How can organizations apply data and AI to improve products, generate better conversions, improve brand loyalty, spot trends earlier or find additional ways to improve overall customer satisfaction?
Data and AI power can become the game-changer for businesses looking to improve their products and customer experiences. For instance, predictive analytics enable organizations to grasp customer preferences, allowing the organization to enhance the product, thus resonating with the public. It thereby allows more personalized offers, recommendations, and experiences that boost conversions and customer engagement.
Brand loyalty also calls for the use of sentiment analysis. Through analysis of social media, reviews, and feedback, businesses can proactively address any issues, enhance customer relationships, and earn trust. Real-time analytics are also important as they give businesses the chance to identify trends early, thereby allowing them to quickly adapt to changes in the market against their competitors.
AI can further discover unidentified customer needs through behavioral and usage patterns. This may create new products and services that result in more well-rounded satisfaction for the customer. Automated customer service systems that use AI, such as chatbots, may resolve problems quickly, making customers feel better overall.
Q6. In the health care sector, how can AI and data analyze electronic health records to identify patterns and trends that can help predict risks to patients, susceptibility to disease and other aspects that can improve patient care?
In healthcare, AI and big data are enormously transforming patient care, especially through the analysis of electronic health records to identify trends and predict risk. By identifying risks early on, predictive analytics, for example, have in many cases shown to cut down on the rate of death among patients. This is the reason that an AI-based system can sense patterns that are not discovered by traditional methods, thereby letting a health provider predict chronic conditions like diabetes, heart diseases, and even cancer. Along these lines, an AI’s capacity to analyze all the unstructured data—like physician notes or social determinants of health—helps to better diagnose with more insightful patient needs.
Moreover, AI surveillance of real-time patient data significantly decreases the chance of readmissions since critical conditions are flagged faster. The AI functionalities have been proven to be salient for improving the quality of care with better outcomes and patient satisfaction rates. In addition, EHR aggregation aids providers in population health trend identification that assists them in preventive care initiatives aimed at reducing long-term cost burdens on healthcare systems.