As we enter 2022, the ongoing trend of digital transformation in business shows no sign of slowing. Indeed, by most estimates, companies will invest trillions of dollars into digital technologies and strategies over the coming year.

Frequently, our clients come to us with only a superficial notion of what this can entail. They know that the AI/ML technologies we provide can help to streamline their businesses, but they rarely truly understand how to go from obsolete, antiquated, procedures to truly data-driven processes.

Based on literally thousands of hours of discussions with these kinds of clients, we have been able to pinpoint five steps any company can follow as they attempt to make AI/ML a part of their businesses. Using this framework, we have seen dozens of companies create enormous value and avoid costly mistakes in their digital transformations.

Many business leaders that attempt to implement data-driven strategies make one very costly mistake: They fail to inform their existing workforces about these technologies, and instead choose to rely entirely on new hires and external clients.

Getting experts in artificial intelligence and machine learning on board is essential when integrating these technologies into existing systems. This doesn't mean, however, that current team members shouldn't be incentivized to become familiar with new processes.

Include even low-ranking and non-technical team members in the conversation, and be ready to listen to their ideas. You may be surprised to find that their skill sets have a surprisingly high degree of overlap with AI/ML.

The key to success in AI/ML-powered digital transformation is good planning. Unfortunately, many businesses fail to put in place any kind of quantifiable framework before getting started.

Ultimately, defining business outcomes is a three-step process. Start by looking at different aspects of your business and survey team members in order to find problems that need to be solved, or to get suggestions for business opportunities that have thus far been ignored.

Then, for each of these points, determine quantifiable goals that can be set. These could come in the form of KPIs, as well as other kinds of metrics.

Finally, take a moment for each goal to ask, "why is this important?" The answer will be your business outcome defined.

Let's say your company is facing lower sales in a particular season (winter). You determine that you need to check into what sales staff are doing during that time.

You are worried that your team may not feel incentivized to work as hard in the winter as in other seasons. Based on our method, your business objective is to check all sales communications in winter in order to even out business year-round.

In order to begin processing data, it's essential that good, company-wide, data preparation, collection, and security policies be in place. Not only will this facilitate the analytics algorithms to complete their tasks with lower rates of error, but it will ensure that your organization remains compliant with regulations in your jurisdiction, such as GDPR or CCPA.

When considering external hosting providers, look for fast storage solutions, optimized for AI. Variables to consider include data ingest and workflow, and the solution you choose should not skimp on security features.

Overall, do not rush into decisions, here. Trust the advice of whichever AI/ML consultation firm you are working with.

Just because you have set good, quantifiable, objectives, and made them clear to your team doesn't mean the completion of your goals will be linear.

Digital transformation, like other kinds of tech-based enterprises, requires business leaders to always be ready to make adjustments to their strategies. This is all the more true when AI/ML is being implemented.

Our recommendation is to organize your digital transformation around the development of MVPs. As a first step to achieving each business objective, create and test a basic minimum viable product before deploying a full-blown solution.

This way, you won't waste resources on an implementation that doesn't work or that will become obsolete within only a very short timeframe.

Many who are new to digital transformation make the mistake of thinking that it's a 'one and done' kind of endeavor. You can't just hire a bunch of consultants, put together a quick strategy, and expect quick results. Business leaders need to take a long-term perspective when it comes to integrating digital processes and technologies into their businesses.

This is particularly true when it comes to implementations of artificial intelligence and machine learning technologies. Not only do these technologies improve with time (with new software releases and updates occurring every month), but they work better for your company the longer they interact with your data.

In other words, the algorithms that you will be implementing into your business require time to make good decisions. In the beginning, they will only be able to make predictions based on historical data.

Later, after many months and years of data-driven business, the algorithms will become more sophisticated in their interpretations. You'll be amazed by the business outcomes.

Creating an effective digital transformation strategy is often a challenge for businesses. The addition of AI/ML to this process brings on a certain amount of complexity.

Daiger can help you work through the framework outlined above in order to achieve excellent business outcomes using the latest data-analysis tools and AI/ML technologies. Do not hesitate to get in touch with them.