30 Nov Artificial Intelligence FAQ
Questions & Topics
Our subject matter experts answer the most commonly asked questions.
In the past, AI development entailed designing and building highly-complex, one off models that were fragile, error-prone, difficult to understand, and even more difficult to support. With the emergence of open source AI function libraries such as TensorFlow and PyTorch, this landscape has changed dramatically. Today, many complex AI problems can be addressed using functionality from these libraries, turning custom development from an esoteric exercise into a component assembly process – what we refer to as component integration.
Without consistent, accurate, timely data, even the most sophisticated AI model or powerful use case doesn’t work.
- Reliable data sources (internal or external) must be identified, in some cases contracted for, and integrated into your data infrastructure.
- Data must be normalized, cleansed, and in some cases transformed for consistency, otherwise models will not perform.
- Data must be received, processed, and available by the time it is needed.
- Most importantly, your data infrastructure must work. Every time.
Some AI solution providers tout their ability to develop a minimum viable product (MVP) at a very low cost. Typically, an MVP includes using your test data set to implement a simplistic base model. Unfortunately, a minimally viable solution doesn’t include the cost and effort to implement a resilient data infrastructure; ramp the model to reflect real world data volumes, the cost of data (internal and external); ongoing testing, validation, and retraining; careful curation of data/model/result sets; compliance requirements for explainability and ethical behavior; and don’t forget user adoption.
The premise and promise of AI means that some people’s jobs are going to change. When users can’t accept and support the changes that AI brings, projects fail. Change readiness, stakeholder management, communications, training, and reinforcement are all important factors to consider.
Business Process Outsourcing is the short and direct path to getting the support services you need without the investment, time commitment, and risk of hiring and training internal resources. By investing instead in BPO, you get:
- Enhanced Business Focus – Lose the distraction—focus your expertise and resources on your core business.
- Repeatable Processes – Leverage Access Sciences’ broad industry experience.
- Improved Quality – Expertise can’t be overrated. Our BPO resources bring targeted expertise in their fields and we back that up with tangible service level agreements.
- Headcount Management – Transfer the burden and risk of headcount management to us.
- Oversight Relief – Lessen the burden of oversight with a single point of accountability
- Increased Accountability – Our BPO services are provided via binding contracts with legal redress.
- Operational Expertise – Benefit from industry best practices, shared across our BPO operations.
- Exceptional Talent – Gain access to Access Sciences’ sustainable, high-quality talent pool, backstopped by our entire company’s knowledge and experience base.
Some people call this managed services and others call it business process outsourcing. What that really means is that you’re hiring an outside firm to fulfill a business function for you.
No, all of Access Sciences’ BPO operations are located in the U.S. In fact, many of our BPO operations are co-located at the client’s site and function as an embedded part of their overall organization.
There are generally two alternatives to outsourcing business processes: using internal resources (employees) and using resources from staffing agencies. While internal resources may deliver quality results, these can be a distraction from focus on your core business and make it difficult to manage headcount during the ups and downs of business cycles. When using a generic staffing agency, accountability for quality results typically ends once resources have been placed.
Our BPO resources strike an optimum balance between loaded labor cost savings, headcount management, accountability, quality, and expertise.
Explanations & Definitions
General resources to aid in information gathering.