Introduction

How Domain-Specific AI Creates Business Breakthroughs

Increasingly managers are under pressure to resolve challenges with AI. They need to use their data to drive key decisions, manage risks, and achieve a competitive advantage. Their data changes frequently, making speed a critical component.

Complicating matters, managers have too much data and are unable to determine value from noise, making a wide-scale AI deployment difficult. They need to scale across the organization to affect the type of success they need.

Domain-specific AI is ideal for resolving situations like these.

Domain-specific AI helps businesses process and analyze data within a specific industry and function. Domain-specific AI is configurable to a client use case.

With domain-specific design, AI can be configured and deployed quickly for client use cases. Industry analysts have validated domain-specific AI as a disruptor in the industry. In comparison, generic AI models don’t cut it – especially not to resolve the challenges many companies face in their industries and job functions.

You need human experience to use AI successfully in domain-specific situations.

Consider a general-purpose image recognition algorithm that can identify persons, animals, and objects. Compare that with a high-speed industrial AI application that detects defective packaging on an assembly line. While both are complex AI algorithms, the latter is a domain-specific model for the manufacturing industry.

Here is another example. A general application of an AI content generator can write a college essay or a poem. Compare that with a model that writes toxicology reports for chemical safety analysis. The latter example is a domain-specific AI that is trained on data from the oil and gas industry and serves the energy industry.

The Benefits of Domain-Specific AI

Businesses benefit from domain-specific AI in several ways, including more reliable outcomes, time to impact, pre-trained data from multiple industry parties, and scaling across the enterprise with more use cases.

Here are some benefits of domain-specific AI that Evalueserve has experienced:

1 Spreadsmart works at a 99% accuracy rate, much higher than traditional, manual methods.

2 Our automation platform deploys customer analytics models in weeks vs. what normally takes months to build from scratch.

3 While a single client may not have enough data, we do because we work with multiple clients in a single industry or function. This allows us to build AI models trained on multiple client data sets.

4 Without constraints on the availability of training data, we build domain-specific AI that works well.

Using general AI to resolve specific challenges can be cost prohibitive. Whether the challenge impacts strategic decisions, deals with frequent changes, there is not enough training data to build their own AI, or there is a need to scale, domain-specific AI Is the most reliable, impactful, cost-effective, and fastest means of achieving results.

How Domain-Specific AI Works

Domain-specific AI designs algorithms and models to resolve specific challenges for a particular industry, function, and use case. Domain-specific algorithms: 

  1. Generate more reliable outcomes 
  2. Speed up time to value 
  3. Use pre-trained data from industry sources 

Before the rise of domain-specific AI, companies often vacillated from general models to bespoke models, both of which require a lot of effort. Market needs and experiences created a demand for domain-specific AI. Domain-specific AI allows companies to scale and achieve results faster and with a lower data requirement entry point than general and highly customized “bespoke” AI models.

Domain-specific AI offers a happy medium where we develop an algorithm that is specifically tuned for a use case, but it’s trained with data from the entire domain. This provides distinctive speed and cost advantages over bespoke models because they have limited access to industry and functional data sets.

Building and Implementing a Domain-Specific AI Solution

Using an algorithm already trained on industry- and function-specific data quickens time to value. Domain-specific AI delivers better accuracy and relevance because it is built with a purpose in mind. Here are some examples of domain-specific AI use cases:

  • Customer segmentation for  B2B industrials, logistics, tech

  • Customer lifetime value for  B2B industrials, logistics, tech

  • Upsell/cross-sell recommendations for  B2B industrials, logistics, tech

  • Table detection for  financial statements, ESG disclosures, research reports

  • Document classification for  financial statements, scientific literature

  • Article classification for  automotive, life sciences

  • Phrase cloud for  automotive, life sciences

  • Article sentiment for  supplier risk, chemical safety

  • Named entity recognition (NER) for  automotive, life sciences, chemicals

  • Parameter extraction from free text  for financial services, ESG

Five-Step Macrolevel Process

On a high level, Evalueserve goes through a five-step business process. Of course, the review of the implementation of an AI solution is much more extensive than the below process, but this does offer a simplified macrolevel process to identify and implement a use case.

 

1 Identify a High ROI Use Case

Domain-specific AI is use-case specific. Deep experiences in the financial sector, manufacturing, consumer-packaged goods, energy, technology, and professional services provides Evalueserve a deep understanding of the full spectrum of potential domain-specific AI use cases.

Being able to recognize can develop the correct use case is essential to building an AI solution capable of returning high value requires time, resources, and experience. Domain expertise helps identify how AI can best match and serve the company and its use case to achieve desired outcomes.

2 Identifying AI Models

The next step in the process focuses on selecting the correct foundational technologies. Usually, the correct foundational technologies are large AI algorithms as the basis for an AI model, for example, the popular GPT-3.5 generative AI algorithm or the SIFT computer vision algorithm. Large models are trained on terabytes of data, but the data inputs are static and cannot be controlled by the user. Large models require further fine-tuning to answer prompts in a dependable and actionable manner for clients. 

In a domain-specific model, the data science team selects the correct algorithms. Then, based on the use case, they take the extra step of specifying the data inputs that will improve model accuracy over time. 

3 Train AI with Domain-Specific Data

AI becomes domain-specific when the data science team begins training the AI model with industry-specific training data. Data sources include industry- and function-related documents, terminology/vocabulary, data sets, and other relevant sources. Data is labeled to ensure accuracy to meet the use case within the domain.

Training the AI model to become domain-specific requires subject matter experts to spend significant time supervising the learning. The model must meet use case needs and be domain-sensitive, effectively resolving industry-specific nuances. The efforts invested by subject matter experts help the relatively limited industry data set train the model to achieve domain-specific outcomes.

4 Deploy and Integrate

Moving from training a domain-specific solution to implementation allows you to create an AI product-led solution that delivers results. Creating and training a model is one thing, but the solution needs to be exposed to end users. Deployment can occur through platforms, microservices, and APIs embedded inside business intelligence tools like CRMs or Slack or via other core knowledge management technologies.

5 Fine-Tune for Client

User refinement ensures that a domain-specific effort is a purpose-built solution for the specific company. Once end users have the output, end users further validate and refine results, fine-tuning the model further with real-world experience.

Often this is called human-in-the-loop, but the accurate industry term is reinforcement learning from human preference or (RLHF). The RHLF stage is when the AI model achieves the accuracy level necessary to provide the outcomes a business needs. This effectively completes the training cycle.  

A Simulation Software Company’s Domain-Specific Experience

A software simulation company wanted to modernize and streamline its go-to-market research and analytics to better support its strategy. The software company takes insights from a variety of data sources, from internal customer data to competitive intel, to drive its go-to-market strategy.

Evalueserve aligned with the software company on a use case to deploy a sophisticated domain-specific AI model that would streamline customer segmentation to lifetime value and upsell product recommendations.

To achieve these outcomes, Evalueserve recommended implementing its MagnifAI solution. MagnifAI unifies actions like customer segmentation, a simple audience builder, customer lifetime value, and an up-sell recommendation engine on a single platform. MagnifAI is a domain-specific product trained to operationalize and scale customer analytics for an industry’s most common customer use cases across teams.
The solution successfully unified data from an array of analytics projects. We helped reengineer the customer data and analytics tech stack, so all the key sources flowed through a data factory into MagnifAI. MagnifAI uses a low-code platform to run models on real-time data. All the attributes needed to run a model in the containerized applications are on the platform, allowing users to simply drag and drop relevant attributes and run their model.