There’s no point denying it: cutting-edge technology has a powerful allure.
The promise of innovation can excite stakeholders, and a robust tech stack gives off an air of competitiveness.
Combine that with a rise in specialist tech roles elsewhere in the market, and you might feel the pressure to add similarly advanced capabilities to your team.
However, without a clear definition of the core problem within your organisation, making such hires and adopting new technologies too soon – such as AI and machine learning – can be costly.
Read on as we uncover how to know if your organisation is ready to adopt AI or machine learning, what to avoid when it comes to new tech investment, and how to plot the best route forward for your business right now.
Assessing AI-readiness
The question any organisation should ask before recruiting an AI or ML professional is: are we ready for this?
Because the honest truth is, most aren’t. There’s no shame in this of course – it’s just the reality of things.
AI and machine learning are incredibly powerful tools, but they can only be as powerful as the foundations on which they are built.
Those foundations being your core data capabilities.
In creating an environment where AI and machine learning technologies are of benefit, you will gain the most value if your data is first cleaned, stored and structured in a way that makes your key assets:
- findable
- accessible
- reusable
Although, in some instances, AI can be helpful in the data discovery phase, in general you’ll also want to be in a place where you have maximised the value of your core data analysis capabilities. This will set you up for “low hanging fruit” goals that are easier to reach and generate immediate wins.
You can then leverage this increased understanding to pinpoint areas where more advanced practices will further enhance value for your organisation.
For organisations and businesses that haven’t reached this level of maturity in their data capability, hiring AI and ML specialists will likely lead to suboptimal (and costly) results.
The impact of overpromising on AI
Prematurely hiring for a specialist AI or ML role makes it difficult to achieve the expected value on your investment.
Because without sufficient data maturity, your new hire’s day-to-day becomes less about performing the advanced tasks listed in your exciting job spec, and more about cleaning up data to ensure AI readiness within your company.
This quickly leads to a misalignment of inputs and outputs, where you end up hiring the wrong person for the actual work that needs to be carried out.
This will likely have cost implications, and can be frustrating and demotivating for the candidate. And it’s no surprise mismatches like these commonly lead to new hires resigning before their probation, or “quiet quitting” because the role simply wasn’t what the candidate (or the employer) had expected.
Calculating your next steps
If your core data practices aren’t quite AI-ready yet, here’s how we suggest you make the right hiring decision:
Strip back your priorities to the core elements of delivery within your organisation, and think about your data needs in relation to the direction of the business.
It may be the best decision to actually hire a data engineer to clean up, restructure and refine your foundational data practices first.
Make the hire less about matching technical skills to a job spec, and more about matching the aspirations of a talented data professional to the trajectory of your organisation.
And if you need support validating your resourcing plans or generating the next hire for your business contact Fenway for specialist support – from the data experts who recruit data experts.