An AI strategy consultant is an external expert who can help an organisation develop an AI and data strategy. The AI strategy consultant works with the client organisation to identify and assess the data, and prioritise the AI initiatives with the highest return on investment (ROI) so that the client organisation can achieve their business goals. The consultant can also implement AI solutions, or alternatively hand over to in-house teams or another external consultancy to build and deploy any models.
Fast Data Science offers AI strategy consulting for public and private sector businesses as well as non-profits. We can help our clients design and implement AI solutions to improve profitability, customer satisfaction, efficiency, and other measurable metrics.
A regular AI consulting engagement is more hands-on: the consultant will do what the client asks. The consultancy may come on board and find that the client company already knows exactly what they need from AI, and how they want it done. That’s fine if you already know what your data can do and understand how AI can meet the needs of their users or customers, but you just need someone to implement the ideas in your existing AI strategy.
But what if the company is new to AI? An example would be an established business which has been operating for many years, where the management are aware that they “could” or “should” be using AI, but nobody’s sure exactly what that looks like. Or perhaps, a company has already been using AI for a while but they’d like somebody to take a good look at where AI is used in the business and identify which initiatives and projects have a reasonable ROI and which AI projects are a cost sink (for example, an employee has spent a lot of time developing an AI model which is unlikely ever to be used in production).
If there are strategic or “big picture” questions to be answered, then it’s a good shout to hire an AI strategy consultant to develop an AI strategy for your business.
Need AI strategy consulting
Your business may be interested in leveraging your in-house data, and need an external AI strategy partner with AI experience to work with. You can hire an AI strategy consulting partner company like Fast Data Science to help you identify and implement the AI solutions with the best ROI.
We can take the time to understand your data and your business needs (financial, marketing, customer service, operational, etc), and identify and prioritise opportunities for AI.
We can also develop RFPs (Requests for Proposals) for you to seek other AI partners for your broader AI strategy.
An AI strategy consulting engagement typically takes the form of three or four phases:
Data discovery. We explore your dataset and work with staff to understand the data that you are collecting, and identify opportunities where your data could lend itself to AI projects.
Opportunity identification. We organise brainstorming sessions with key stakeholders (e.g. VPs of each division) to identify potential opportunities where AI could be introduced in your business.
Opportunity prioritisation. We investigate the opportunities further to estimate the cost of implementation and benefits of each opportunity, as well as timelines. We work with senior management to prioritise the AI opportunities. The prioritisation is done quantitatively using methods borrowed from market research and social sciences, for example Likert scale surveys to estimate the perceived impact across divisions.
Optionally, we can assist in the development of RFPs (Requests for Proposals) for the opportunities identified in Phase 3, which you can put out to public tender. We can assist in the selection of AI partners for the delivery of these key projects. As an alternative, our AI strategy consultants can develop and deploy the recommended AI solutions.
AI opportunity prioritisation exercise as the beginning of an AI strategy consulting engagement. Projects can be categorised on two axes, difficulty and impact. Attractive initiatives would be towards the bottom right of the graph, that is, having a high impact for low technical difficulty.
Click on any phase above to view its details.
The phases of an AI strategy consulting engagement: data discovery, opportunity identification, opportunity prioritisation, and optional drafting of RFPs (Requests for Proposals)
Tarion is the regulator for the housing market in Toronto, Canada.
In Ontario, when a person buys a newly built home, the construction is registered with Tarion, which means that for over two decades, Tarion has been receiving text data in the form of homeowners’ completed forms. The forms describe any potential construction defects in free text.
The management at Tarion found that they needed to use AI to sift through the data on the constructions, and to see if they could pull value out of them. They were not sure what would be possible or realistic to achieve with AI, and what would be the most helpful AI application for the business.
For this reason, Tarion went out to tender and looked for an AI partner which could help them develop an AI strategy, with a focus on Tarion’s large amount of text data. Fast Data Science responded and was awarded the contract to develop an AI strategy with Tarion.
Phase 1: Data discovery
To kick off the AI strategy consulting engagement, the Fast Data Science team first spent a couple of weeks exploring the data in Tarion’s internal Salesforce database (CRM), and delivered a discovery report to the Director of Strategic Initiatives at Tarion. From some sandbox machine learning models, the AI consultants identified that it was possible to derive predictive value from Tarion’s data, although the data would be easier to work with if a data lake and warehouse were set up as an analytics platform, instead of querying Salesforce directly.
Phase 2: Opportunity identification
Fast Data Science moved on to hold brainstorming sessions with the VPs of all main departments of the business, including legal, underwriting, IT, and operations. They identified a few key areas where AI would make a huge difference to the business, but managed to collect up to 40 example projects, from predictive analytics, through to simple static data explorations.
Phase 3: Opportunity prioritisation
To conclude the AI strategy consulting engagement, Fast Data Science conducted a survey of VPs and stakeholders in all departments of the business and asked them to further rank the AI opportunities by stakeholder impact, including impact on homeowners, house builders, staff, and the financial outcome of any initiatives. Independently from the business-centric view of the opportunities, Fast Data Science also ranked them by technical effort, prerequisites, and duration.
The clear winner was a predictive model to triage homeowners’ submissions into those most likely to escalate vs those most likely to resolve. A number of important initiatives emerged, which were mainly predictive models that identify costly or problematic situations before they escalate. Other projects, such as analysis of social media sentiment, although useful, ranked lower in terms of company-wide impact. What is interesting is that without the opportunity prioritisation exercise, it would not have been clear which opportunities would be most beneficial to the business.
Opportunity prioritisation allowed the AI strategy consultants at Fast Data Science to identify the “low-hanging fruit”, that is, AI initiatives which have a broad impact across the organisation without being too difficult to implement.
Review of Fast Data Science after the AI strategy consulting Tarion project on our Google profile
Fast Data Science, a leader in AI strategy consulting, empowers organisations to unlock the full potential of artificial intelligence through tailored, actionable roadmaps. Our AI strategy consulting services begin with immersive discovery workshops, where our AI strategy consultants collaborate with stakeholders to align AI initiatives with business objectives.
We identify high-ROI opportunities, such as predictive analytics or NLP-driven automation, while addressing risks like data quality or ethical concerns. With a proven track record since 2016, our AI strategy consultants have guided global firms, including those in healthcare and finance, to integrate AI seamlessly. For example, we’ve developed strategies for clients like the NHS to leverage unstructured data for operational efficiency.
Our AI strategy consulting process emphasises practical implementation, providing clear timelines, cost estimates, and success metrics. By combining technical expertise with business acumen, our AI strategy consultants ensure sustainable, scalable solutions that drive profitability. Led by Thomas Wood, with a Cambridge Masters in NLP, Fast Data Science delivers jargon-free, results-oriented AI strategy consulting, positioning businesses to stay competitive in a rapidly evolving AI landscape while fostering innovation and long-term growth.
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