Businesses are engaged in a less spectacular but no less significant race as scientists strive to create the first artificial intelligence that can match that of humans. the competition to implement revolutionary AI use cases within their companies.
Even if AI has the potential to revolutionize business, implementation time and value are crucial. It can be difficult and time-consuming for companies without a lot of AI and data science expertise to design or even merely implement an AI model. Although 73% of executives point to a serious lack of AI skills as a persistent obstacle, 93% of executives believe AI is crucial to future success. As a result, transformative change is still difficult and many AI adopters still struggle with ROI.
Generic foundational models can produce answers quickly, but they frequently have errors and require a lot of manual evaluation. Businesses must increasingly orchestrate a variety of highly specialized AI models in order to achieve high accuracy and confidently and efficiently carry out critical business processes. However, achieving this calls for a wide range of platform capabilities, integrated governance and controls, and the capacity to train or optimize these models using data unique to a given organization. When time and talent are limited, that is no easy task.
In this post, I’ll provide two methods for creating custom AI models that maximize accuracy and performance while reducing time to value.
Method 1: AI As A Service
Few businesses possess the technical know-how or resources necessary to create an AI model from the ground up. Many choose to “build your own” instead, putting together the required parts from different outside suppliers. These patchwork systems can still be expensive, challenging, and dangerous to construct, though. And that’s even before performance and compliance monitoring, prompt engineering, and model training start.
Fortunately, developing a unique AI model doesn’t require relying on intricate, in-house technologies. Prominent suppliers offer their clients access to pre-built, cutting-edge AI models along with extensive tools to customize it to meet their specific requirements. Data labeling is typically used to help users train a strong but general foundational model to comprehend the particulars of their organization.
Let’s say you wanted to automate the order-to-cash (O2C) process of collecting. Since the process is centered on communications, you would require an AI model that can correctly interpret customer messages, extract pertinent data, and even identify sentiment in a variety of communication formats. Even with current components, putting such a system together will be expensive, dangerous, and time-consuming.
As an alternative, think of a feature like Communications MiningTM, which offers the CommPath AI model as a service. The model starts learning and training from your data as soon as you upload it. Comprehensive tools for fine-tuning, performance monitoring, governance, and continuous improvement are offered, along with integrated features like sentiment detection and multilingual support. An intuitive, guided user interface and its interaction with the larger Business Automation Platform for facilitating end-to-end automated workflows are further advantages for users.
Naturally, tailoring a prebuilt model to your needs still takes time when using an AI as a service method. Recent advances in GenAI, however, have had a revolutionary effect.
With little to no training, GenAI models, such as DocPath and CommPath, may begin analyzing and extracting useful information from documents and messages right away. Then, through a procedure known as active learning, language or concepts unique to the industry or firm are refined. Here, your team and the AI model collaborate to swiftly personalize the model. The model performs the majority of the training without supervision. Users are only asked to classify instances that the model is unsure of.
Custom, accurate AI models can be produced using an AI as a service method in a fraction of the time and expense compared to doing it alone. Time to value is significantly accelerated and most of the pain associated with model development is eliminated when one has access to a fundamental model and the easy-to-use tools required to refine it.
Method 2: Tailored AI Services
But what if you wanted a unique AI model that didn’t require any subject-matter specialists or staff training? You may not have the time or personnel to perform even a small amount of data labeling or prompt engineering, despite all the benefits AI as a service offers.
The top suppliers are beginning to establish “model factories,” or teams dedicated to providing specialized AI services. These are specialized groups that collaborate with you to develop the most effective, high-performing, personalized AI models. They will carry out the necessary model training, prompt engineering, and data labeling. They can use synthetic data (lookalike data based on your processes) or your own data, depending on your data policies. Additionally, the team can complete model training far more quickly than the typical employee team due to their experience and competence.
Even though AI services teams operate quickly, the procedure is nevertheless quite collaborative and engaged in order to guarantee the model’s uniqueness and dependability. Usually, it consists of:
The team collects requirements and examines procedures through consultation and use case analysis. Additionally, they will choose the project’s technical approach, which frequently involves choosing between data labeling for a bespoke model or prompt engineering for an existing one. Generally speaking, the demand for data labeling increases with the specificity and complexity of the use case.
Data preparation and collection: sample data is gathered, data quality is evaluated, and a secure data transfer occurs. All of this is carried out in accordance with the customer’s privacy and compliance rules.
Model training and development: the group creates the necessary features and plans the model architecture. Depending on the specified technical method, either prompt engineering starts or training data is annotated. The model is trained and iterations are carried out in this step.
Testing, validation, and deployment: As the team gets the model ready for customer deployment, user acceptability is tested and model performance is assessed.
After the model has been installed, some suppliers will give post-deployment support. As goals and business dynamics shift, this involves ongoing performance evaluation and retraining.
The Model Factory For IDP
For developing unique, industry-specific AI models for intelligent document processing (IDP), the IDP Model Factory is the perfect collaborator. Data extraction from documents is a prime example of an AI use case due to its demonstrated return on investment and quick time to value. For this purpose, has previously created cutting-edge IDP models, but when time is of the essence, our IDP Model Factory team can also assist clients in quickly creating customized models.
Our team at IDP Model Factory has outstanding AI knowledge and experience in both rapid engineering and creating original document extraction models. It can assist customers accelerate and realize faster value from AI in their company by utilizing its best practices and top document extraction expertise, which have been built via the creation of hundreds of unique IDP models.
For customers like insurance industry leader HUB International, the team has effectively integrated generative and specialized AI to standardize fiduciary decisions and improve efficiency throughout its HUB Mail postal service. Over 900,000 papers were processed as a result of the initiative, and created more than 50 prompts.
For businesses to attain high accuracy and a true understanding of their business data, they require more than just basic, general-purpose AI models. They require customized models that meet their unique requirements and have been trained on real business data. DocPath and CommPath are pretrained models that may be optimized to perform well in any communications or document use case. Through our IDP Model Factory services, however, we also provide our clients the most of our model training and prompting knowledge.