Getting value out of AI projects means solving for the skills gap
In a recent multi-industry, global survey, 65% of companies reported that they are not yet seeing value from the AI investments they have made in recent years.
MIT Sloan School of Management
#1 Reason AI Projects Fail
A lack of talent with appropriate skill sets for data science work
#2 Reason AI Projects Fail
Lack of a clear strategy for implementing AI applications across the organization
Both problems stem from...
A widespread lack of ‘AI literacy’ among the existing workforce. The most impactful step the organisation can take is to bridge the gap between the technical people and the business people who work with them.
Join the growing number of forward-thinking organizations who are empowering
their employees to become Fluent in AI
The Lucid Analytics Project's research into the ethical and effective use of AI has been sponsored, published, or covered by:
...and referenced in multiple academic papers, including work conducted by the European Parliament, the Bank of England,
and the World Economic Forum in conjunction with Ernst & Young and the University of Cambridge.
To really unlock the latent transformative potential of data science, organisations need to solve for the skills gap
65% of companies report not seeing
value from AI investments
In a recent multi-industry, global survey by the MIT Sloan School of Management, 65% of companies reported that they are not yet seeing value from the AI investments they have made in recent years.
And that doesn’t surprise us. Data science has the potential to bring tremendous value to organisations, but we’ve only really begun to scratch the surface when it actually comes to delivering that value.
And much of this is because there is still a pronounced knowledge gap. That is, there’s a widespread lack of ‘AI literacy’ among the existing workforce of companies looking to implement AI applications. More so than anything else, this knowledge gap poses the greatest barrier to the successful implementation of AI projects.
A recent study by McKinsey found that, by far, the two most frequently cited barriers to AI adoption are a lack of talent with appropriate skill sets for data science work, and a lack of clear strategy for AI.
Both problems are rooted in a lack of working familiarity with the domain.
To really unlock the latent transformative potential of data science, we need to solve for the skills gap
And what does this mean?
It means getting business people to understand what the data scientists need, and to learn how to work with them. Even if they don’t understand the data science itself.
It means teaching the people managing AI applications to set informed and reasonable expectations. And providing them with a framework for working with the outputs of machine learning models.
It means teaching them to avoid confirmation bias when evaluating decisions made or recommended by AI, while also not falling into the trap of blind faith in machines.
AI can be very complicated - but these problems are not. They are actually fairly straightforward to solve.
We built this course because, as a leader of data science projects and teams, this is the course I wish every one of my business partners, stakeholders, analysts, and management team could have had access to.
This fills a painful gap I know all too well.
Chief Data Scientist
Without a common language, data scientists
and the business experts they partner with fail
to partner effectively
And without this common language, they struggle to prioritize the right initiatives,
ask the right questions, and align around a common
– and informed – set of expectations
And that means that organisations investing in AI and other data transformation projects may waste resources on failed initiatives, get stuck with partially-completed projects, put AI solutions into production before they’re fully tested, and expose themselves and their customers to serious - and entirely new - risks.
This self-paced, online course is composed of 12 modules – grouped into 4 sections,
plus a primer (module 1) and a guide to next steps (module 12).
Each module consists of animated slide or video content with voice narration,
and a quiz to assess understanding and provide immediate feedback to participants.
Participants should expect that the course will take 12-16 hours to complete.
Part 1: How and why AI will transform the future
Part 2: Artificial intelligence and machine learning: what do you need to know?
Part 3: The anatomy
of an AI project
Part 4: How to help shape your organisation’s
Provide accreditation in Fluent in AI to your entire department, or organization, with a managed testing package and performance validation
Get Fluent in AI for your entire department, or organization
For questions related to the course, group licenses for employers, or to get on the waitlist for accreditation, please contact us through this form.