The ultimate checklist to get your business AI ready

Regardless of the industry you work in, it’s likely that AI will become an integral part of the way your business operates in the future. The potential for AI to increase workplace efficiency, improve decision making and change the way small-medium enterprises are structured is huge. Furthermore, the speed at which AI is currently evolving is currently faster than any other field of business enhancement.

This means that all businesses, regardless of their size, should be getting prepared for a future powered by AI. While AI terminology can be confusing and somewhat complex, with this handy checklist you should be able to get your business up to date when it comes to making Artificial Intelligence-based improvements to your infrastructure.

What you need to consider: a checklist

1) Start fostering a culture of experimental thinking within your company

Ultimately, machine learning involves iterative and experimental processes. While core algorithms are often mass-produced with a “one size fits all” approach, individual projects need to be tailored to the context of your business and the data it holds.

Like all experiments, sometimes things can go wrong. It could be a case of needing to generate or procure new data. The description of the challenges faced by your company might need to be tweaked based on what you find using AI. To do this, your key decision makers and individual employees alike will need to establish a test-and-learn approach to data analysis.

Iterative processes which provide maximum agility and flexibility will facilitate faster evaluation. This will help your team to determine whether you need to find an alternative approach to the issues faced.

2) Define the problems that require solving

Like all technology, AI functions at its best when there is a clear and definite problem that needs to be solved. Re-occurring or repetitive high-volume decision points usually need a fast response. These defined actions are perfect for machine learning, particularly if there are variable inputs along the way.

AI works well when there are intuitively captured rules or associations which cannot be easily identified by logical rules. In other words, when you require accuracy, but your data is too complex or problematic for traditional analytics, it could be time to turn to AI.

Machine learning is data-intensive and relatively quick. By defining problems that could benefit from AI interpretation, this will ensure that any returns or value gleaned from the data is relative to the effort put in to solve the issue via AI.

3) Develop a watertight data strategy

There’s an old saying among IT workers: “garbage in, garbage out”. AI requires large amounts of data, and setting up a process for the effective capturing, identification, delivery and access to good quality data is paramount.

To achieve this, your data guidelines need to include support for explorational environments (sometimes referred to as sandboxing). You’ll need to take a multi-level approach to allow for access and flexibility, but you’ll also have to ensure that privacy, security and quality aren’t compromised. It’s worth remembering that non-traditional data sources, such as speech, images or unstructured text might also require a fresh approach to data management – which brings us to point number four…

4) Create an interdisciplinary team of data experts

Investing in AI doesn’t simply mean investing in technology. Your organisation is also going to need the right people to manage the systems for the best possible impact. You’ll also need a dynamic team which includes experts who can:

• Bring data onboard the system
• Assess the data
• Assess the ethics of any proposed actions
• Deploy and maintain your data ecosystem

5) Create a “high risk tolerance” culture

All businesses should discuss what is “good enough” in terms of data input and ensure that team members understand how models should be developed and validated. The real test comes when training/test data is replaced by genuinely productive data – and fostering a “high tolerance” culture will ensure your data is valid when it comes to properly implementing AI.

6) Be prepared to overlap established processes with new ones

The implementation of any new system, including machine learning, can be disruptive to the everyday working environment. Before you implement AI, you should be prepared to assess any potential impact it could have on your existing processes, functions and roles. Even the briefest of checks can help reduce the time and cost associated with restructuring after AI implementation. To do this, ask your team the following two questions:

• How will AI influence our existing processes?
• Are we in a position to make any necessary changes?

Conclusion

AI is incredibly beneficial across a variety of industries, and after deployment, your business will need to remain committed to new IT practices. The maintenance of machine learning is an ongoing process that must be nurtured in the same way as traditional data development – but the good news is that it yields much more powerful results.