AI Application Development is a Team Sport
Enterprise AI is a team sport that requires various roles and functions to collaborate in order to deliver resilient, repeatable AI-powered solutions at scale.
Data Scientists and Data Engineers collaborate to build out models, algorithms, decisioning systems and insight engines.
Architects and Software Developers can turn models and insight engines into solutions, applications, or embedded intelligence in various systems of engagement or operational applications.
Subject Matter Experts (SMEs) and Business Analysts contribute end-user experience and process re-engineering requirements, and help to fine tune applications to drive specific goals (impact on key performance indicators or “KPIs”).
Business and Technology Management want visibility into model and application performance as well as return on investment (ROI) KPIs such as the cost of delivering AI projects versus the value delivered.
And Compliance, Risk Management and Auditing need to know that models and applications can be trusted, are being governed and responsibly used, and meet explainability, bias, fairness, accuracy, and robustness requirements.
Each of these collaborators use different tools and applications to play their part in application development.
Gap Between AI Technologies and Outcomes
There is a large gap between AI technology - the data science, data engineering, and machine learning (ML) software and infrastructure - and business outcomes - the value derived from AI-powered applications and decision intelligence. In order to realize value from AI applications, development teams must address several challenges:
- Access and availability to high quality data that is spread across the enterprise in data silos
- Coordination and collaboration across multiple development teams
- Contextual understanding of key entities (e.g. customers, products) to drive hyper-personalized insights and next best actions
- Optimizing AI applications to meet the desired business outcome at a system level rather than a model level
- Trust and governance across the lifecycle of AI development and deployment in order to mitigate the risk of bias, explainability, accuracy, and more
Enterprises have invested in a variety of data, data science, application development, and cloud infrastructure tools and technologies over the years, yet are not getting the value they expect. AI application development is often focused on Data Science and ML Ops with a reliance on manually “wiring” AI into solutions for point-to-point isolated successes. AI Applications rarely leverage reusable assets, are often not easily repeatable, and are difficult and expensive to maintain.
AI application development teams are finding that development is taking longer than originally projected, requiring an increasing amount of time from high-level, high-cost developers and data scientists. These projects and applications are not realizing their original goals and financial returns, and AI Roadmaps are taking longer to deliver with fewer use cases and potential applications than originally planned.
An AI Engineering Discipline Helps AI Teams Deliver the Value of AI Initiatives
It takes a team effort to build AI-powered capabilities, put them in production, monitor them, and realize value from them. This requires collaboration across the enterprise, new processes, and Enterprise AI tools and platforms. This level of attention to the people, processes and tools - enabling this team to develop a discipline or competence around AI solutions development and value realization - is the essence of AI Engineering.
An Enterprise AI Platform Empowers AI Teams
CognitiveScale’s Cortex AI Platform is enabling our clients to build and deploy AI applications across the enterprise, positively impacting KPIs like development time and costs, value attainment, and risk mitigation.
Contact us to explore how we can help your organization realize value from AI.
- White Paper: AI Engineering Powered by CognitiveScale
- Case Study: AI Engineering and the Cortex AI Platform Drive Improved Speed-to-Value