We knew it was coming and it’s finally here, the time when businesses will turn to AI (artificial intelligence) to boost their capabilities. It perhaps involves fewer flying cars than we’d imagined, but the future is definitely here and already well-established.
Even late adopters have now succumbed to the realisation that in order to remain competitive, in order to secure your future success, you need the help of data and AI.
But just because we’re all doing it, it doesn’t mean we’re doing it right. In the rush to develop companywide data-driven transformation, many have fallen foul to some common pitfalls. We’ll look at what they are, how to avoid them, and how to create an AI implementation strategy with legs.
1. Choosing The Right Metrics
What are you measuring and why? Having a clear roadmap to your business goals is key to a successful AI implementation strategy. What metrics will you need to track in order to help you to make more informed decisions? Ask the same question across all of your business processes from product development to marketing to HR.
It’s important to know that an AI implementation strategy in and of itself doesn’t guarantee success. Data doesn’t make the changes, it only informs you of where changes need to be made. Being realistic about what AI can and can’t do for you from the start will help to mitigate burnout amongst you and your team.
2. Work From The Inside Out
There’s a big difference between digitally native companies that have used AI from the outset and those that are undergoing the process of digitisation. The former has a much more natural relationship with AI processes. In a digitally native company, the value created by AI is clear from the outset and isn’t contested.
Companies that make the transition face having to prove the necessity of new technologies to employees who may be reticent to change or who prefer using legacy systems.
To minimise friction, involve your teams in understanding the importance of the transitions and selecting the technologies that will be most useful to them. Then choose carefully. Invite your teams to take part in the demonstration of the software so that they can envisage the use they can get from it from the start.
3. Committed Leadership
Once you’ve chosen the best technology, leadership needs to show consistent support and involvement from the onboarding process and beyond.
The most important thing is that the leadership recognises the value of AI implementation. It isn’t necessary for them to be AI or data experts from the outset. If they wish to expand their knowledge there are AI and data courses available, but depending on the tool, it’s not always even necessary to dedicate whole days to training.
If you choose AI technologies from a company that offers customer support and training, they’ll be able to guide leaders exactly on what they need to know and offer training for their team.
Leaders need to focus on fostering an environment that promotes the use of data and AI. That means setting data-based goals and following up on them, ensuring your existing team feels comfortable with new tools and technologies and has the appropriate training, and committing to maximising their investment.
4. Managing Data
One of the main pitfalls when it comes to implementing an AI strategy is to underestimate what’s involved in accessing reliable and high-quality data. When choosing a tool to process and analyse data you need to first be sure that it’s realistically and consistently get hold of the required data sets.
Is the data easy to gather and access? How much effort is required to retrieve it? Can you rely on getting the same or similar data sets for comparison month-on-month, year-on-year? How reliable is the data? What biases is it subject to? The more easily accessible and reliable the data, the more likely you and your teams will use the tools to measure it, and the more value you’ll derive from your AI strategy.
With Symanto you can measure organically written text data wherever it is found, from corporate documents to news sites, to customer generated social media posts and product reviews. The great thing about this type of data is that it’s easy to access, abundant, organically generated and reproducible.
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