Why Generative AI Democratization is Creating New Opportunities in Information Services

Customer-centricity fundamentally asks the question: What do the customers need, and how can we provide it?

Yet many organizations miss important corollaries to this question:

• What capabilities do customers already have, and how do we add additional, more transformational value?
• How will customer needs and expectations change over time as they become exposed to new technologies and different ways of solving their problems?
• And, do our competitors—or startup disruptors—understand our customers and the transformational impact of new technologies better than we do?

The latter questions are becoming increasingly relevant as generative AI opens the door to increased democratization. Because specific AI applications make it easier for nonspecialists to retrieve and contextualize information, the design of search experiences will change and the potential of AI to automotive workflows will be realized. Information services companies can leverage these opportunities to open new markets and revenue streams.

However, this opportunity won’t happen if companies limit themselves to innovation on the margins. Applying AI to improve “what we have today” will be less impactful than reimagining what is possible. 

Rather, transformational, game-changing AI applications are the only way to realize this opportunity while staying deeply grounded in customer centricity.

Why is generative AI democratization so transformational?

One of Gartner’s major trends for 2024 is the continuing ability of generative AI to democratize access to information and skills. There are two primary reasons why this is the case:

• Generative AI doesn’t require hard technical skills to operate, opening the door to previously inaccessible insights (although “prompt engineering experts” may disagree)
• Generative AI is becoming more widely available through public, open-source instances, enabling access among the general public – and it is becoming increasingly infused with B2B product experiences as well.

For these two reasons, Gartner has labeled AI democratization “one of the most disruptive trends of the decade.” Here are some of the specific areas where this is playing out.

Analyzing queries to improve information retrieval

Anyone with experience in information retrieval knows that the structure of your query can dramatically impact the output. With complex datasets, then, only people highly trained in the specific data taxonomy can effectively retrieve the needed insights.

AI-based sentiment analysis disrupts this entire process. Now, people don’t need to be data taxonomy experts to retrieve the information they need. All they have to do is ask a question, and the generative AI will analyze that question and determine the sentiment behind it.

What’s more, automated generative AI engines can monitor user activity within an application, analyzing them to uncover the data most needed at that stage. By delivering insights at the point of need, information services companies can dramatically enhance the user value they provide.

Expanding your user base

As information retrieval becomes easier and requires less expert skill to perform, the barriers to entry will go down dramatically. The lower the barrier, the more users you’ll be able to serve, opening the door for you to grow your user base and expand your revenue streams.

However, many product architectures as they stand today are too clunky and burdened by tech debt to take advantage of this opportunity. As you expand your user base, it’s important to align your platform’s user experience (UX) with those users’ expectations.

Further, it’s important to understand that your current user base is accustomed to operating your platforms in a specific way. Completely overhauling your legacy platforms overnight can often do more harm than good. It’s important to have a product strategy in place that carries current, more technical users along while also opening up the door for a broader user base.

Minimizing production costs

Generative AI not only helps your external users, but also your internal team. By enabling more advanced capabilities without requiring highly technical skill sets, you can reduce costs by hiring fewer in-house experts. Generative AI can also speed up time-to-production, enabling faster deployment.

However, this transition isn’t something that happens on its own. It requires a thoughtful approach, AI ready data and platforms, and a deep understanding of your team, resources, and processes to ensure that you’re not using generative AI to help your team members to solve for customer needs, pain points, and challenges. Treat internal customers with the same respect and level of investment as your external customers.

Which specific areas of expertise is AI democratizing?

An unintended—and mostly positive—trend within AI democratization is that people can perform increasingly complex tasks without technical training. The ubiquity of publicly available, open-source generative AI tools certainly is accelerating this trend.

Proprietary data for complex questions

Generative AI is empowering more people to access, visualize, and even interpret previously esoteric information to get answers to questions faster than ever before. In many cases, the answers are more robust than previously.

Information services companies have an advantage in that they can provide users with proprietary data inaccessible anywhere else. However, unless these companies transform their data delivery processes to align with modern use cases in generative AI and machine learning, powered by “AI ready” data, they will find it difficult to attract and retain users.

Algorithms and AI at your fingertips

Users don’t have to be data experts to take advantage of AI algorithms. The benefits of Bidirectional Encoder Representations from Transformers (BERT), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM), and even ML algorithms like decision trees are democratized.

So long as the user knows what outcome they want to achieve, they can simply go and choose the tool from the list that best accomplishes that outcome—only a basic knowledge of this functionality is necessary.

While most of your users won’t be able to realize this advantage, it makes it easier for your competitors to deploy marginal improvements to their applications. As such, incremental change is no longer a competitive advantage—transformational, customer-centric change, however, is.

Model development

Building a generative AI model is Step One. Continually improving that model through development and training is key to driving success among users. This process involves ingesting new data and adapting the model based on user behaviors—transforming your prototype into something usable in the market. It also means removing bias.

Recent tools like AutoML are enabling model development even among those who lack this advanced skill set. While expert human input is required—especially among nuanced edge cases—this tool makes the training process faster and more scalable, enabling competitors to launch products faster.

Why do information services businesses need to take AI democratization seriously?

Based on these AI democratization examples, we can see that incremental improvements to existing capabilities won’t present a competitive advantage for information services companies. Achieving such marginal change is easier now than ever before.

What will present a competitive advantage, however, is using generative AI and similar functionality to drive game-changing transformation within your products and services. This involves not only technological competence, but strategic competence in product development:

• Reimagine the product experience. You can’t just focus on building a smarter, faster, better version of the technology you already have. You need to find new and more efficient ways to deliver user value.
• Incorporate democratized AI into customer-centricity. Don’t just ask what the customer needs, but what they already have. If you’re duplicating an existing, publicly available tool out there, you’re not actually adding value.
• Prepare your data to support transformational AI. Adopt a unified data store, processed in the cloud, that’s accessible to all your AI models, and can enable a wide range of ever-expanding use cases.
• Transform your internal processes for scalability. Adopting an automated MLOps process can drive efficiency, scalability, and risk reduction.
• Hire the right people. Not only do you need technical expertise in house—like AI and ML engineers—but product development experts who know how to capture user insight and develop transformational, strategic applications that align with their true needs.

It’s much easier to adopt a customer-centric, technology-agnostic approach to generative AI when you have a partner to guide you every step of the way. 3Pillar Global can provide you with access to our global term of experts that will provide a clear roadmap for leveraging generative AI to drive transformational user and business value.

Get in touch with 3Pillar Global today.