The Information Services Company’s Guide to AI Readiness

Many information services companies are in deep discussion around how to leverage AI to drive customer and business value. But a topic often missed is AI preparedness. Without it, you’ll crash and burn like all the other tech-chasers. According to Gartner, by 2025, generative AI will be a workforce partner for 90% of companies worldwide. That’s why AI readiness is one of their top 10 technology trends for 2024. In other words, generative AI isn’t in the future. It’s already here. Read this short guide to see how to make sure you’re AI-ready for 2024 and beyond.

How can AI provide value to information services companies?

Executives and information services leaders generally approach generative AI in one of two ways: jumping in head first, or taking the “wait-and-see” approach. Although those approaches seem like polar opposites, they’re both tech-based decision-making frameworks, not value-based. Here’s the difference:

• Tech-based approach—should we adopt this new technology?
• Value-based approach—can this new technology add user and business value?

Because information services companies live and breathe data—often proprietary—you have a unique competitive advantage to offer complex, comprehensive, and time-sensitive insights that no one else can. This is a gold mine for AI development, and if you’re not capitalizing on it, then you’re going to fall behind. But it goes deeper than that. There are two broad categories of AI adoption: everyday and game-changing. Game-changers not only have the potential to realize an ROI on AI adoption, but can outpace competitors by redefining the terms of engagement and delivering customer value that’s exponential—not incremental.

The 5 critical areas of AI readiness

Whether you anticipate adopting AI tomorrow or later in 2024, taking these steps now will set you up for success and enable you to move quickly when the time comes.

1. Data

AI rises or falls depending on the data used to build and train it. At the risk of over-simplification, it’s an input vs. output situation. Good inputs lead to good outputs. Bad inputs lead to bad outputs. The AI development process doesn’t begin when you start building the product, but with a more fundamental component—data. To understand why data is so important, we need to take a step back and discuss some core concepts related to generative AI: machine learning and deep learning.

• Machine learning (ML) is based on data-trained algorithms. These algorithms ingest data, detect patterns, then learn how to make predictions and recommendations based on that data. This differs from traditional programming in that the machine creates its own instructions based on the patterns observed in the training data.
• Deep learning is a subset of machine learning that uses neural networks—based on the ways neurons interact in the human brain—to ingest data and process it through multiple iterations. This approach allows the AI to learn increasingly complex attributes of the data itself.

In short, AI’s fundamental functions require high quality data inputs to drive high quality data outputs. This is why 3Pillar advocates having a unified data store, processed in the cloud, made available to all your AI models.

2. Personnel

The people you have on your team and in your corner matter. In addition to data preparedness, here are five types of personnel you need to achieve AI success:

• User research experts. These people deeply understand users, customer journeys, and customers’ goals, pain points, and jobs to be done.

• AI product designers. These people translate user needs and goals into AI-powered product experiences.

• AI product managers. These people understand the complex interactions of customer needs, business needs, and AI capabilities.

• AI / ML engineers. It goes without saying that to build an AI-based product, you need someone who gets how AI and ML work.

• Data scientists. Data quality is key to AI readiness. Fortunately, most information services companies have in-house data scientists who can meet this need.

3. Product strategy

When it comes to digital product success, technology is never a differentiator. No user actually cares about which machine learning model or deep neural network powers your product. Rather, they care about what the product will do for them. In other words, value > capabilities. This goes back to the tech- vs. value-centricity we mentioned earlier. When preparing for your new AI product, you need to start with a clear understanding of the value you’re delivering before you start the build. Otherwise, you’ll waste time and money building something that no user will use—and that you won’t be able to monetize.

4. User testing

You don’t want to build an AI model, think it’s working well, then release it into the wild and have your users break it. You don’t want it generating unusable or false insights because you didn’t test it thoroughly. That’s why user testing must be a critical component of building—and validating—your AI-enabled products and customer experiences. Unfortunately, it’s too often an afterthought. A lot of people think they’re really smart on these subjects, but it turns out they’re wrong because they misunderstood the user. For example, Casetext (acquired by Thomson Reuters) invested nearly 4,000 hours of training and fine-tuning their AI product, CoCounsel, based on over 30,000 legal questions. The result: a product that was better positioned to provide real user and business value. Instead, build the prototype, put it into the hands of users, and let them break it. Then figure out what went wrong, iterate, and try again. Once you’re sure you’ve got a product that delivers value to users, only then are you ready to take it to market.

5. MLOps

In addition to data readiness, it’s critical that your machine learning operations (MLOps) are also prepared to manage and productize your generative AI model. The steps that go into the machine learning lifecycle—data ingest, data prep, model training, model tuning, model deployment, model monitoring, explainability, and more—require active management and rigor to avoid producing errors. MLOps can result in the following benefits:

• Driving reproducibility of ML pipelines, enabling more tightly-coupled collaboration across data teams
• Reducing conflict with devops and IT, and accelerating release velocity
• Enabling regulatory scrutiny and drift-check to offer greater transparency and greater compliance with company policies

Because of our extensive experience working with information services companies, 3Pillar Global can help you assess your AI readiness and pursue game-changing AI innovation. We’ll help ensure you’re well positioned to drive transformational change and open up opportunities for additional revenue within your business. Contact 3Pillar Global and we’ll help you assess your readiness and help you fill in the gaps.