Integrating AI projects with other systems can be challenging, but also rewarding. AI projects often require different tools, data sources, and workflows than traditional software development. They also involve complex and dynamic interactions with users, stakeholders, and environments. To successfully integrate AI projects with other systems, you need to consider some key aspects, such as:
Data is the fuel of AI projects, but it can also be a source of complexity and inconsistency. You need to ensure that your AI project can access, process, and store data from various sources, such as databases, APIs, sensors, or web scraping. You also need to handle data quality, security, and privacy issues, as well as data governance and compliance. To facilitate data integration, you can use tools such as data pipelines, data lakes, data warehouses, or data catalogs.
- Assess data sources required for the AI project and ensure they can be accessed from or integrated with other systems. This may require building APIs, setting up databases, or creating data pipelines. - Clean, normalize, and preprocess data to get it into a format usable by AI algorithms. Resolve any data schema mismatches across systems. - Establish persistent data stores and data lakes to enable sharing of data between the AI system and other applications. - Implement Medallion Architecture: To integrate the date from raw to refined. - Bronze - Silver - Gold
AI enabled systems can read millions of data transactions from BI , Backend Data and front-end systems ; the inferences based on AI could build a better predictability models empowering Businesses . AI can read multiple Digital footprints left by the customers across touch-points say browsing history, key words search behaviours, transactions made, service requests , abandonment of cart , moving from online to stores , that would enable Analytics powered by AI in Business insights & decision making for continuous processes improvement and feedback mechanisms and build zero tolerance for errors in Projects implemented. AI based Data Visualisation give a single view of Customer Life Cycle !
Model integration refers to how your AI project interacts with other systems through its outputs, such as predictions, recommendations, or insights. You need to decide how to deploy, update, and monitor your AI models in different environments, such as cloud, edge, or hybrid. You also need to ensure that your AI models are compatible, scalable, and reliable across different platforms, devices, and applications. To achieve model integration, you can use tools such as model registries, model servers, or model orchestration.
Integrating AI models with your enterprise data and services is an exciting part of our Generative AI practice. These solutions are built faster than traditional, pre-AI apps, saving CapEx and OpEx. Moreover, they enhance user engagement by allowing your customers to interact directly with your data and services. Large Language Models (LLMs) excel at orchestrating complex workflows from a list of available services. The key to a successful integration of the chosen LLM with your enterprise digital assets lies in dynamically managing prompts. Contextually crafted LLM prompts ensure reduced costs, best performance, and the optimal balance between short-term conversation history, long-term semantic memory, and the list of available services.
Model integration is somewhat different if you are looking at classical AI vs generative AI. With generative AI, model orchestration is very rich these days. There are plug-in architectures available that enable your Large Language Models model to integrate with external services such as databases or web services. There are also orchestration frameworks for generative AI such as LangChain or Llama-Index which help connect your LLM to other data sources. With all types of models, you need to leverage MLOps or LLMOps technologies to practice good lifecycle management of your models. These tend to help you manage model registries and deploy the right models in the right environment.
Process integration refers to how your AI project aligns with the business processes and workflows of other systems, such as ERP, CRM, or BI. You need to understand the goals, expectations, and constraints of your AI project's stakeholders and users, as well as the impact and value of your AI project's outcomes. You also need to establish clear and frequent communication and collaboration channels with other teams and departments, as well as external partners and customers. To enable process integration, you can use tools such as agile methods, user stories, or feedback loops.
Before implementing any AI tool, it's a strategic imperative to understand the business processes. Do this by hiring a BA to understand the As-Is and/or employ process mining tools for operations. Customer journey mapping will also show you where in the business you will have the best success with AI integration. Be mindful not to do away with human touch points that are valued by the customer or you stand the risk of devaluing your offering.
Ethical integration refers to how your AI project respects and promotes the ethical principles and values of other systems, such as fairness, transparency, accountability, or privacy. You need to identify and address the potential risks and harms of your AI project, such as bias, discrimination, manipulation, or intrusion. You also need to ensure that your AI project is trustworthy, explainable, and responsible, and that it follows the relevant laws and regulations. To foster ethical integration, you can use tools such as ethical frameworks, impact assessments, or audits.
Integrating AI projects with other systems is not a one-time task, but a continuous and iterative process. You need to constantly evaluate and improve your AI project's performance, usability, and alignment with other systems. You also need to adapt and innovate your AI project's features, functions, and goals according to the changing needs and demands of other systems. By integrating AI projects with other systems, you can create more value, efficiency, and satisfaction for your organization and your customers.
This is where algorithmic accountability is important. AI algorithms will perpetuate bias based on the input. Bias identification and rectification needs to take place to ensure organisational biases are not re-enforced. To build trust among stakeholders, ensure that you can show how the AI has arrived at a certain decision. In the event that an AI makes a harmful or costly error, algorithmic accountability determines who is responsible - whether it's the developer, operator or somebody else. Ultimately, each organisation needs to have a framework that is adapted to its needs, taking into account complexity and risk impact.