What is an AI consultancy?

Artificial Intelligence (AI) is a topic that seemingly within a few short years has transitioned from being in the realms of science fiction to a powerfully transformative real-world technology domain with the potential to reshape nearly every aspect of our lives and society. From commuting with self-driving cars and smart algorithms that can anticipate our needs and send groceries before we order them, to advanced models that can plan and manage complex tasks such as designing houses or provide health insights using data from smart devices. The opportunities seem endless, but the competition is fierce, sparking an arms race amongst the big tech superpowers and Bluechip organisations who compete and offer competitive salaries to attract the high-demand-low-supply pool of data science talent needed to assemble their own internal data science and R&D teams. For businesses who are unable or uninterested in competing for talented data scientists but still want to leverage data and AI to automate and make smarter data-driven decisions, they are increasingly turning to AI consultancy partners.

An AI consultancy is responsible for leading and managing the end-to-end adoption of Artificial Intelligence for their clients. However, this brief explanation does not do justice to a complex and multi-faceted challenge which involves understanding the interrelation between distinct but interrelated factors, all of which influence and determine project feasibility and success. The range of factors that must be considered is extensive, ranging from “strategic challenges” such as:

clarify business objective(s)
define the nature of the business problem(s) and understanding the processes
understand the process in which the problem occurs
assess constraints or limitations owing to the nature of the available data
identify factors that may contribute to project risk and possible mitigation tactics
evaluate time, investment and personnel requirements for build and deployment

As well as “technical challenges” such as:

identify viable solution approaches
define metrics to test and validate these approaches
tweak model parameters to optimise output quality
deploy and enable interaction with the broader technology ecosystem
implement data and model governance best practice for data hygiene, auditability and regulatory purposes.

If you’re not a data scientist, odds are many of the above challenges might not mean much to you. They don’t need to. Your responsibility isn’t to be the technical expert but rather the subject matter expert on all things relating to your business and help your AI consultancy partner understand what they need to know to overcome the business challenges.

So how should a business go about choosing an AI consultancy to collaborate with? We provide a few tips on the things you should be mindful of.

Select a consultancy that understands the problem and can fulfill end-to-end project requirements

One of the best ways to mitigate risk is to partner with an AI consultancy with strong experience in developing strategies and solutions to address the types of challenges your business is encountering. The consultancy should be able to provide example case studies on similar projects, communicate the similarities between the cases, articulate their solution approach and the outcome. These don’t need to be a carbon copy in terms of your industry, data or scenario, but should be representative of the nature of the problem. For example, if your aim is to identify and extract information from image-based data then working with a consultancy able to demonstrate a clear competency in the design and training of computer vision and supervised machine learning models is a great starting point.

Ano