Does your business have an AI strategy?
The buzz surrounding artificial intelligence (AI) has grown loud enough to catch the attention of c-suites near and far… and for very good reason.
It’s becoming commonplace to read yet another success story of how AI is lifting organizations to higher levels. From Amazon and its AI-powered warehouse robots driving operational efficiencies to retailers creating more personalized experiences for consumers; the examples are plentiful and diverse.
Yet, while it is fast becoming clear that organizations need to consider the business opportunities of the new technology, the nascence of AI in the business world makes it less clear how to profitably leverage it.
In addition to this is the worry of AI and its impact being overhyped. Many argue that while investment in AI is on the rise, businesses should not just jump on the AI-bandwagon. Instead, businesses need an understanding of how AI could provide value- and this understanding ensures a clear link between AI and business priorities and outcomes.
Without an effective strategic plan for AI, businesses risk wasting money, falling short in performance and falling behind their competitors.
So, how does your business go about creating an AI strategy? Here are three tips to get you started.
# 1 | Define your business outcomes
A good place to begin is by asking the simple question of “what business processes could, or should we accelerate, enhance, or replace with AI that would have favorable outcomes?”
According to a 2018 McKinsey report, only around 16 percent of AI use cases are completely new innovations. This means the highest potential for AI in your business lies in improving the way your organization already operates.
Typically, AI is being used to replace repetitive, low-skilled tasks that involve straightforward decision-making. A great example of this is how AI is being used in law firms for tasks such as document assembly, review and drafting.
These operations are often time-consuming but light on value-add. Automating such responsibilities can reduce human error while simultaneously freeing highly-skilled lawyers from repetitive tasks
As you start to identify similar repetitive tasks or workflows, you can begin to map how AI could play a valuable role in your business.
# 2 | Collect and organize your data
If AI is the engine, data is the oil. If your businesses doesn’t have the data, there is nothing an AI system can do to help.
Harnessing a data and analytics culture within the business is vital. If there is no active collection of results and outcomes, then a predictive or explanatory AI model cannot be created.
Additionally, many of the AI solutions available today are designed to handle ‘clean data’, meaning that the data must be available in a format that is readable by the algorithm.
It’s difficult for a business to know exactly what data may be important to AI analysis. So, it’s wise to gather the rawest data in order to ensure you have what you need- should you need it!
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# 3 | Choosing the right technology
Once you have defined the business outcome you wish AI to help with and ensured you have the data ready, the next step is to determine what type of AI to implement.
There are many algorithms to choose from, each with different functions of performance, quality and specificity. You can choose to use the AI service of the cloud goliaths such as IBM, Google, Microsoft, and Amazon, or to more niche and specialized providers.
With so many options available in today’s digital era, you’ll want to shop around to see which AI technology will meet the requirements for your use case. While we tend to use the term AI consistently, it consists of many different techniques, approaches, and tools.
While it’s early days, AI promises to be the next digital frontier. According to Gartner’s “Annual Hype Cycle for Emerging Technologies” report, AI-based emerging technology is going to play a critical role in business longevity.
To ensure your business is ready for this accelerating trend, it’s vital to have an AI strategy in place which aligns machine learning techniques with business problems. Are you ready?
27 February 2020
27 February 2020
27 February 2020