7 steps to leveraging your data effectively in the AI era

Here are seven important steps  to consider that could help you leverage data effectively and better capture value from AI.

In brief

• Invest $20 in data for every $1 in AI to help contribute to quality data, reduce biases, and drive value derived from AI initiatives across the organization.
• Focus on enterprise-level AI solutions rather than small, department-led projects to drive greater efficiency, economies of scale, and a return on investment.
• Build a diverse talent pool by retraining existing employees, attracting skilled workers, and involving business experts to ensure successful AI adoption.

Not every business challenge actually requires a complex AI solution; sometimes the simplest answer is the right one. Rather than immediately throw the biggest tool in the toolbox at a problem (which, right now, tends to be generative AI), try to fully evaluate the options available and go with the one offering the best ROI. Demand forecasting and inventory planning are good examples of how classical applied AI is often still the most efficient and cost-effective solution and generative AI may not increase benefits.

3. Think big but iteratively

A lot of companies are focused on running small AI projects in individual departments rather than looking at the big picture or connecting them across process areas. Ask yourself: Is focusing on how to boost productivity by a few hours per week in a single team or process as worthwhile as figuring out how to automate 80% of, say, your organization’s procurement function? If you start by thinking big and then break down the journey into incremental parts, you’re more likely to go further faster and you’ll ultimately deliver greater value.

4. Fail fast but usefully

Expect to fail sometimes—but try to do it quickly and usefully instead of spooling on it and missing out on the value you could have derived elsewhere. Likewise, accept that 90% success in a use case experiment is sometimes good enough. Given the rate of technological advance, there’s a real chance you’ll have the tools to get to 100% within the next year.

5. Take an enterprise-level approach

Rather than invest in specific AI tools for niche use cases, it’s more useful to think holistically about the technologies you should invest in. Could, for example, the AI solution being used by marketing be tweaked to perform the same task for finance or procurement? As well as reducing the cost of buying and running multiple tools, this enterprise-level approach lets you benefit from greater economies of scale and helps create better collaboration. It also reduces the risk of being locked in with certain vendors. With that said, there are cases where a very specific piece of tech may be the right answer. This is most likely to be seen with very complex industry-specific challenges.

6. Build talent as well as technology

AI engineers (a role that combines data scientist, data engineering, and software engineering into one) are and will be the most important technical talent. Yet currently the talent pool is very small. Companies can help fill the gap by investing in programs that attract new skilled employees (either straight from education or from other industries), retraining their existing technical developers, and scaling data scientists to learn engineering and engineers to learn the math. Doing so may require considerable time and resources, but it is worth it.

7. Think beyond it

Building and scaling AI-enabled systems is a job that needs multiple different skill sets beyond the IT department. This includes business subject matter experts who best understand the processes being transformed and can therefore help identify particular improvement opportunities or pain points. They are also the people most likely to use any new solution that’s put in place, so unless you involve them throughout the journey, adoption will be difficult (even resisted) and any value stymied.

Just as it has for a decade or more, data remains the backbone of modern business. Yet with the arrival of ever-more-sophisticated AI, organizations across many sectors are being challenged to evolve their approach. This means prioritizing data quality, cultivating a new generation of skilled talent and being choosy about when and where they invest.

It’s also increasingly important to look beyond small, department-led use cases and instead target enterprise-level solutions that transform data strategy across the entire company. There may be missteps and failures along the way, but by focusing on the big picture, you will be well placed to leverage its data effectively no matter where the AI era takes us. The most important takeaway, though, is to really embrace the value AI can bring and not get discouraged when value isn’t easily realized. Focus on finding which of these seven points are missing or underrepresented in your AI initiatives and address them—and the value will come.

This article was originally published on FastCompany.com.