The potential of artificial intelligence (AI) in improving business processes and performance is no news. From predicting customer behavior and buying patterns and optimizing supply chains to personalizing shopping experiences and understanding your workforce, there are dozens of examples of companies in various industries leveraging AI for optimizing their profitability and output.
However, jumping the AI bandwagon isn’t quite as straightforward as you’d like. Like many other forward-thinking and growing companies, if you’re considering a full-scale AI adoption into your business processes, then here are four core tips that will help you in the process.
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1. Secure Executive Buy-In
Just as cloud-based subscription software is changing how businesses operate, AI has the ability to revamp many business processes, but, with such major change, everyone needs to be on board. Having leadership buy-in is pivotal for successful AI adoption.
In essence, the more involved the top-level executives are in the adoption of AI, the better the odds of effective company-wide adoption.
According to a McKinsey Global Institute study, firms that have successfully adopted an AI technology at scale inclined to rate C-suite support nearly twice as high as those from companies that had not adopted AI technology.
Thus, it’s highly recommended to have a dedicated top-level executive taking charge of your AI transition. Consider holding a bi-weekly conference with all the key stakeholders to make sure roles are constantly refined, and everyone’s on the same page regarding the status of AI adoption.
The executive in charge must communicate resources, investment, and overall strategy across the company, so everyone knows exactly what’s going on. Doing so also helps to secure support for strategy, human and IT assets, and cultural adoption.
2. Hire Talent in Data Science and Analytics
The rapid growth in AI technology in the last few years has led to an AI skill gap. In fact, 68% of global respondents in a Deloitte survey indicated moderate-to-extreme AI skills gaps, and the top three roles needed to fill the gaps include AI researchers, software developers, and data scientists.
And it’s not just technical talent that’s needed, but also non-technical talent such as managers and creative heads who can help bring together everyone to navigate the complexities of successful AI adoption.
A good way to prepare a team for tackling AI adoption challenges and work hand-in-hand with automated systems is to outsource technical talent like data scientists, machine learning engineers, and appoint prominent data consultants. Another way to guide your company’s AI journey is to train and retrain your existing talent.
3. Invest Some Time in Change Management
Deploying an API to leverage a new dataset for AI is pretty straightforward. That being said, modifying the management and training for engineers and analysts and who’ll be using these processes can be tricky.
Typically, AI helps with automated binary decisions. But often the integration of machine learning algorithms can allow for more subtle responses too, which can be used together with existing processes to deliver optimal results.
For instance, suppose an AI algorithm scores a loan application on a 1-10 scale of suitability, scores from 8-10 may lead to an instant yes.
However, a score lower than 8 and higher than say, 4, will still require human input to make a decision on the application.
In other words, just as you train employees on how to use a specific tool or process, the same goes for AI-based processes. Your employees may need to spend a few days analyzing the results provided by the AI algorithms, in order to interpret the scores accurately.
If you’re working with an AI vendor, they can guide you on how to comprehend results and how employees can get the most out of the new system. Otherwise, using a digital adoption platform, such as the digital adoption solution by Whatfix, can be a good investment to accelerate user adoption and get employees up to speed.
Essentially, AI is a way to understand patterns, make accurate data-driven predictions, and to deliver more accurate results. For it to work as intended, you need a clearly defined problem that needs solving and the right metrics to succeed.
4. Ensure High Data Quality and Data Availability
When striving to embrace the potential of AI, just keep in mind that a custom AI solution is only as good as the data used to create one.
In the aforementioned Deloitte survey, Carlo Torniai, Head of Data Science and Analytics at Pirelli explains that the “challenges most of the time have been related to data quality and availability, clear and measurable key performance indicators (KPIs), and resistance to change,” and underscores the significance of thinking ahead about what types of data will ML engineers need to train a model and what are the best sources of credible data.
Furthermore, not all the data is useful for accurate predictions. Companies often run into trouble by gathering inadequate or worthless data, which can then make it near-impossible to train a model that can make correct predictions. Simply put, an exhaustive dataset from reliable sources is imperative to ensure the best results.
Successful AI adoption is both challenging and rewarding. By revamping your business processes and operations with AI today, you’re laying the foundations for the future prosperity of your company.
About the author:
Gaurav Belani is a senior SEO and content marketing analyst at Growfusely, a content marketing agency that specializes in data-driven SEO. He has more than seven years of experience in digital marketing and loves to read and write about education technology, AI, machine learning, data science, and other emerging technologies. In his spare time, he enjoys watching movies and listening to music. Connect with him on Linkedin and Twitter @belanigaurav.