AI has moved from just being a buzzword to something that forward-looking companies are using to transform the way they do business to optimize financial, operational, and strategic performance. Artificial Intelligence finds wide applications in areas such as medical research, cloud services, logistics, retail, transport, and freight, among others.

E-commerce is a major facet of the fourth industrial revolution. Therefore digital marketers stand to gain or lose the most when it comes to AI deployment. For the digital marketer in 2021, a keen understanding of the impact of AI on digital marketing techniques, tools, productivity, and output is almost a matter of survival. This isn’t to imply that other sectors such as manufacturing and physical retail should be left behind. However, other leaders in the AI industry also face many of the problems encountered by digital marketers.

AI as a major engine for economic growth has been propelled and highlighted by the Covid-19 pandemic, with businesses implementing AI solutions to increase competitiveness and drive growth. However, not all implementations of AI are successful. 40% of AI deployments in business aren’t successful or yield little to no returns. This could result from erroneous training models, wrong business or use cases, lack of enough data at scale, a lack of expertise, or insufficient funds to implement these models.

AI is a complex subject even most computer science and tech students might have problems gripping. However, a list of essay writing services that is reliable and trustworthy can help you find the right person to tackle research papers and essays in this and other complex subject areas.

This article delves deeper into some best practices for AI deployment and why most business models aren’t quite there yet in 2021.

  1. Finding the Right Business or ‘Use’ Case Through Pilots

Finding the right business case coupled with the right data has been a major hindrance for companies looking to deploy AI at scale. As a result, some of these ventures may fail due to the lack of a proper justification for the AI deployment, especially if the use case hasn’t been proved.

Since AI is an investment with an anticipated ROI, companies should always engage various business teams to find the right use case for specific AI technologies. This ensures that there is always an ROI and that the AI roll-out can be scaled at the right time.

  1. Getting the Right Data

With mega-tons of data coming out from virtually all corners of the internet every second, harnessing the right data for particular AI use cases has become paramount. This data shouldn’t just be massive in scale, but it should also be rich in terms of geospatial, psychographic, and real-time qualities. Growing data formats include HD audio, video, and images, and companies implementing AI solutions should also integrate such data into their AI applications.

  1. Creating a Proper IT and Data Management Framework

Getting high fidelity data at scale just won’t suffice if there isn’t an adequate system to take care of such data. Such a system should be secure, integrate easily with AI partners and suppliers and the existing current systems. In some cases where an external development team is setting up a pilot system, there should be an effective roll-out that mitigates risks and losses and provides a clear path to an ROI.

  1. Getting the Right People for the Job

Solving AI problems starts with the people before the technology. While most businesses may still be green in specific techs such as computer vision and machine learning, they can outsource most of these tasks to external contractors while keeping a minimum number of qualified personnel on the job. Companies that have made sufficient progress in AI implementation can outsource less and build internal teams with positions such as Chief Data Officer or ML specialists and data scientists.

  1. Learning Is Paramount to Progress

AI solutions are rarely a ‘one cut fits all’  type of narrative. Progress can only be made if companies adopt a culture of learning and collaboration, especially between in-house staff and experts in the field who may also provide such business solutions. There is also a need to promote the use of AI beyond data scientists to ensure that expertise and responsibility are well-distributed across the board.

AI: The New Paradigm of Business

AI is a forerunner and a resource that is key to the emergence and use of smart applications within organizations. Yet many businesses and executives are still uncertain about its application and deployment in business cases, especially where the ROI hasn’t been guaranteed. Keeping these 5 tips in mind will help business leaders and executives make smart choices for their organizations as the paradigm shifts to the age of AI.

About

Mitch Flanagan is a start-up founder and author who also doubles up as tech research lead at https://topessaybrands.com/. She is passionate about empowering more girls and women to take up roles in tech and STEM by sharing knowledge and simplifying tough concepts through her writing.

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