Expert Opinion
It is soon becoming clear to stakeholders that creating an AI/ML application or system is not at all a challenge. Building an AI system that one can rely on without any doubt- that is the real challenge.
For years, artificial intelligence (AI) has inched into enterprise systems, carving a space for itself, multiplying its use cases. The capability to utilize intelligent systems has become a market differentiator. However, the definition of successful AI implementation is changing. The focus has shifted from merely assessing the performance of AI models to determining how well it can establish trust among employees, customers, decision-makers, and other stakeholders. It is no longer about how well an AI model works but how trustworthy its outcomes are.
Suppose you are a brand protection company. You have developed an AI application that can identify fake websites, social media accounts, and eCommerce pages built in the name of any brand to help businesses stop brand abuse. Your clients rely on this AI tool to keep themselves apprised of any shady activity going on in the name of their brands. What do you imagine will happen if even a single report generated by that AI contains incorrect information?
To uphold the integrity of the promise you made to the market and to your clients, your AI system must be flawless. That can only happen when you ensure that the data it collects from the web and bases its reports on is flawless.
In this case, all you have to do is ensure that the data scraped by AI undergoes cleaning, validation, and enrichment. Do some manual web research where needed to fill in the gaps. And that’s it! With a bit of careful data management, your AI system can continue to help brands tackle counterfeits and copyright abuse.
Apply this example to any AI system; the results remain the same. At the core of a trustworthy AI lies trustworthy data.
The build-up around AI is not without its reasons. Generative artificial intelligence has impacted annual revenue figures across most industries in 2023, from high-tech, retail, and hospitality to logistics, manufacturing, energy, and banking. Markets are experiencing unprecedented demand around data centers (the heart of the AI revolution). Nvidia is leading the charge of this AI boom, with a valuation of two trillion U.S. dollars in February 2024.
Source- Potential impact of AI on industry revenues 2023 | Statista
Another Gartner research sheds more light on what to expect from AI in the coming years, to the extent that some pros and cons can be derived from the report.
Yet, many are not entirely convinced that AI will continue to deliver on the hype that tools like ChatGPT, Gemini, and Claude and their underlying, powerful models have created over the past year and a half. To quote Forbes, effective, safe, and sustainable AI usage depends on three factors: reasoning, retrieval, and reliability. Without them, organizations risk AI hallucinations, outdated responses, or sensitive data leaks.
That brings us to three critical conclusions.
As a business owner or a stakeholder, you have two ways to be. First, you build an AI system that can provide suitable guarantees. Second, you don’t and, hence, lose opportunities for better efficiency and growth.
Now, not all of these guarantees revolve around data quality. There are other factors to consider, like transparent algorithms, information on the biases that may exist in a system, the purpose and sources of data collection, and so on. And these are areas where bringing global leaders on the same page will still require time, effort, and better policies. But the one thing no one refutes in the present is that the foundation of a consolidated, dependable AI enterprise lies in the data it ingests. That fact alone makes data management a priority for every enterprise trying to implement AI across its architecture.
Implementing robust data validation and management practices is crucial for maintaining the integrity and enhancing the capabilities of AI systems, ultimately leading to more trustworthy and effective applications.
From leading global travel tech companies to enterprises wanting to automate ESG reporting, AI's utility is rampant. It would be impossible to list every possible use case under the sun in this small space to highlight the significance of data management in that particular AI system. The best I can think of is to highlight a couple of similar cases SunTec India has handled.
Allow me to explain the problem statement of these particular businesses that came to us seeking data support, and hopefully, my point will shine.
This client operated a global hotel search platform that aggregates and compares hotel prices from various websites. They have an AI tool in place to match property listings across different platforms. It ensures that customers see the most accurate, up-to-date information and the best deals available and can make informed decisions about their travel accommodations.
They approached us with a hypothetical problem- What would happen if the AI tool incorrectly matches properties, showing outdated prices or incorrect hotel information? Naturally, that could lead to customer dissatisfaction and loss of trust in their platform at both ends. The end users would not want to use the travel platform, and hotel vendors would not wish to register for it either. This could ultimately affect their brand's reputation and revenue.
To us, the solution was straightforward. A team of six data specialists was assigned the task of validating each data point scraped by the AI. After cleaning, validating, and enriching the data, the team added a manual checkpoint to verify anomalies or fill gaps in automated data collection. This data management process ensured that the client’s AI system provided reliable and valuable service, helping travelers make the best choices for their needs. Additionally, we were able to achieve a 65% higher customer acquisition rate for the client.
This client sold a subscription-based competitive intelligence platform that helps businesses gain strategic insights into market trends, competitor strategies, and industry changes. The AI-driven tool that gathers and analyzes data from various sources to provide actionable intelligence.
They faced repercussions from end-users in a few cases where the AI tool had mistakenly analyzed outdated or incorrect data, leading to flawed insights.
Our team started processing the data that this AI was analyzing. This involved cleaning incoming data files, formatting them, and aggregating incoming data from electronic mail, direct mail, print media, mobile digital, social media, and website URLs and entering them into client’s systems. Along with ensuring data integrity, we also worked on automating certain tasks, thus increasing the capability of their AI model with a 96% performance gain.
Becoming a data-driven organization that fits in the emerging AI economy is challenging. You must evaluate if your business needs a data strategy, an AI strategy, or both.
A successful AI enterprise needs to generate long-term value from its data. It needs to construct a system where every player in the organization can access a single source of information, and the same source feeds the AI models being used. That way, not only is the data known to all, but the level of trust in AI decisions improves.
Every AI enterprise must be a responsible steward of the data fueling said AI.
At the same time, you need to trust and follow the process. To get the most out of your company's data and information assets, you need to set up a dedicated operating model that's integrated into the organization’s AI and aligned with your business goals.
That is what SunTec is- a one-stop shop providing access to data professionals and related subject matter experts. We offer all you need to ensure that everything is in place to warrant trust in your AI solution.
We have been humans in the AI loop for a long time, handling data management for AI-driven tech companies, eCommerce businesses, travel companies, global brands, and several Fortune 100, 500, and 1000 companies. Gartner has recognized us as one of the top global representative vendors in the data services market, and B2B marketplaces like Clutch and Goodfirms have repeatedly acknowledged us as a leading outsourcing company.
Furthermore, we help businesses boost their trust in the quality of their data and information assets, no matter their priorities. For any related queries, our representatives can be reached at info@suntecindia.com
Rohit Bhateja is the Director - Data Division & Head - Digital Marketing at SunTec India. With over a decade of experience in the industry, his core expertise lies in digital marketing, customer acquisition, marketing analytics, and brand communication. He likes exploring data trends and devising transformative marketing solutions. He is also an avid writer who enjoys sharing his thoughts and insights on trending topics shaping the industry & transforming businesses.
