Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Generic AI or Tabular Data AI: Which One Gives You the Competitive Edge?

Which approach will truly give you the competitive edge in today's rapidly evolving market - generic AI or in-house tabular data AI

In recent years, artificial intelligence (AI) has become more available and accessible than ever before, making it a game-changing technology across various industries. One of the primary reasons behind this surge in popularity is the emergence of generic AI systems, which are off-the-shelf AI solutions trained on general data. These solutions are not tailored specifically to your business or customer needs, but their widespread adoption and rapid growth, as exemplified by platforms such as ChatGPT and Dall-E, have generated quite a buzz. These platforms managed to attract millions of users within just months of their launch, sparking conversations and debates around their potential applications and limitations.

However, amidst this hype, it’s important to remember that many businesses still rely on in-house AI systems that utilize structured tabular data, which may seem relatively mundane compared to the cutting-edge capabilities of generic AI. This begs the question: which approach will truly give you the competitive edge in today’s rapidly evolving market – generic AI or in-house tabular data AI?

There are pros and cons to both approaches, and the following insights can help you make informed decisions about which AI strategy is best suited for your business.

In spite of its ubiquitous presence in pop culture, AI remains a concept without a universally accepted definition. Here, AI is defined as a computer system capable of performing tasks that historically required human intelligence. This definition is helpful as it is not limited to specific types of algorithms, such as neural networks, but differentiates AI from common computing use cases like spreadsheets and databases.

The buzz surrounding AI stems from its potential to revolutionize industries and act as the catalyst for the “fourth industrial revolution.” The latest generation of generative AI systems can create images and text that are convincing enough to be mistaken for human-generated content. As visual learners, humans are wired to process sights and sounds quickly, making it no surprise that text-to-image generators and platforms like ChatGPT have gone viral.

Generic AI: A Good Fit in the Right Situations

Generic AI systems make sense in certain cases, such as when dealing with generic skills like voice recognition or language translation. Some AI-powered tasks require massive amounts of data and computational power, which may be too resource-intensive for all but the largest enterprises to build. However, off-the-shelf AI systems also mean that your competitors have access to the same AI tools, leading to the same decisions, words, and images. Generic AI systems don’t account for your unique customers, products, services, data, business rules, or expert employees.

Tabular Data: Giving Organizations a Competitive Edge

On the other hand, tabular data can offer a competitive edge. Businesses and organizations typically lock this data behind firewalls, making it inaccessible. Your competitors cannot benefit from your proprietary data, and generic AI systems did not have access to this proprietary data when they were trained.

Related Posts
1 of 5,850

AI Survey on applicationsMaybe that’s why more than half of the AI industry experts who participated in a recent survey voted that internal tabular data will give organizations the biggest competitive advantage.

Challenges, and Unlocked Potential (and Value) in Generic AI

Despite its potential, tabular data has not delivered as much value as expected. While it is easier to work with when kept simple, real-life data is often more complex and challenging to use. Simple and easy data manipulation does not create much value; instead, it is the intricacies of real-life data that hold the key to unlocking its potential. The best AI solutions use more than raw data from your database, supplementing learning with inputs such as business goals, domain knowledge, and feature engineering for effective AI applications.

Real-life data typically presents several challenges that make it more difficult to use, including one-to-many relationships across tables in a database, missing or incorrect values, and spanning time with potential structural changes. Additionally, it requires context and domain knowledge, feature engineering to become AI-ready, and is sourced from an almost infinite set of schemas without well-defined AI-specific semantics. Furthermore, it changes quite frequently, adding another layer of complexity.

As a result, generating value from tabular data requires significantly more effort, but overcoming these challenges can unlock its true potential in AI-driven solutions.

For Competitive Differentiation: Focus on Data Quality and Domain Knowledge

To make tabular data competitive, it is crucial to focus on data quality, as poor data leads to poor outcomes. Feature engineering is necessary to carefully select and transform data for successful model building. Domain knowledge is also essential to ensure that the algorithm learns the correct business rules and addresses the right business problems. This deep understanding of underlying data allows businesses to harness the power of their proprietary information and leverage AI to gain a competitive advantage.

In today’s rapidly evolving business landscape, it is essential to consider whether you want to be behind, equal to, or ahead of your competitors when it comes to adopting AI technologies. By not leveraging AI systems like ChatGPT, you risk falling behind as your competitors embrace these advancements. However, simply using generic AI solutions may only put you on equal footing with others in your industry.

To truly gain a competitive edge, it is vital to harness your unique intellectual property by building AI systems that are trained on your proprietary data and business rules, and incorporating insights from your valuable subject matter experts. By capitalizing on your organization’s distinctive strengths and knowledge, you can elevate your AI strategy beyond that of your competitors, ensuring that your business remains at the forefront of innovation.

[To share your insights with us, please write to sghosh@martechseries.com]

Comments are closed.