AI is becoming more and more prevalent in every industry. Many companies use it to make informed decisions and predictions, streamline operations, and create new innovative solutions and products. Examples include assessing risk with bank loans, automating customer services, innovation in health care, creating smarter cities, and these are only a few examples among an endless list of uses. The rapid emergence and evolution of technologies like AI-enabled development platforms and data storage create new opportunities for all businesses.
Challenges in adoption
Despite the increasing popularity of AI, many businesses are not ready to implement AI technology and never emerge from experimentation in ad hoc pilot programs. One of the biggest mistakes that organizational leaders tend to make is to believe that AI is a plug-and-play technology that will provide immediate returns. They might even invest large amounts of money into data infrastructure, AI software, data expertise, model development, etc., only to be disappointed as they struggle to move from pilot to full-scale adoption. A few of the common challenges facing organizations while implementing AI are:
- AI only learns from the data that it has access to. It requires training datasets and the quality of the output from an AI system depends on the quality of data that it has to learn from.
- A number of AI systems are trained in a supervised manner requiring the data to be labeled. With the advent of the Internet of Things (IoT), a vast amount of unstructured data is generated every day that must be labeled. AI makes objective decisions based on the data on which it has been trained on.
- Implementing AI requires that an organization have a base level of knowledge and understanding for optimizing the technology to solve specific problems.
- For a successful implementation, an organization needs to incorporate technical knowledge with the business strategy where corporate technical expertise may be limited.
AI implementation best practices
For successful implementation, an organization should ensure interdisciplinary collaboration between stakeholders, ensuring that initiatives address comprehensive organizational priorities. Data-driven decision-making facilitated by suggestions driven by AI-powered analytics shifts a great deal of traditional manual heavy lifting to a process leveraging more automated insights. Trust builds in an organization as algorithms empower individuals to make more informed decisions.
A few best practices to consider while implementing AI include
Before implementing AI, answer basic questions like – How will AI help create better products or services? Will it improve the time to market? Will it mitigate risks? Execute an AI strategy based on answers to these questions.
Determine relevant use cases
There are a variety of areas where businesses can utilize AI. These include machine learning, natural language processing, image recognition, and chatbot technologies, to name just a few. Look for AI-capable platforms that specifically address the business needs and conduct surveys to gather feedback from your peers and employees where appropriate.
Understand the raw data
High-quality historical data is required to train machine learning algorithms. Insufficient and incorrect data can lead to costly mistakes – “garbage-in, garbage-out.” Ensure that you include all relevant data. When preparing for the analysis of raw data, make sure that the data has all the necessary features and exceptions, along with realistic predictions. Verify the accuracy and correctness of the predictions, trigger points, system boundaries, etc.
Incorporate disciplined monitoring, tracking, and measurement systems at every phase. It is essential to regularly check the progress of deployments and re-validate along with the business’s goals.
A common stumbling block for companies that can result in failed AI implementations is not rooted in the technology itself but rather organizational structure and corporate culture that impede progress. To successfully deploy AI, executives must foster a culture that enables seamless collaboration. Hence, a framework is required to help executives prepare their organization for implementing AI at scale. This framework should include embracing a data-driven decision-making culture, clearly defining roles and responsibilities of the stakeholders, building AI awareness across the organization and ensuring a solid integration and change management process.
If you are interested in learning more about seamlessly implementing AI into your organization, email us at email@example.com. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.