5 Ways To Implement AI In Your Business Strategy

One way to implement AI into a business is by using predictive algorithms to learn customer habits and make predictions about trends. These algorithms collect data on what customers do and predict what will happen next. Automation is another excellent benefit of AI technology because it can complete tasks in a fraction of the time that usually takes humans. Another option is using automation software to make decisions that humans would traditionally make.

In latter, some datasets can be purchased from external vendors or obtaining from open source foundations with proper licensing terms. AI involves multiple tools and techniques to leverage underlying data and make predictions. Many AI models are statistical in nature and may not be 100% accurate in their predictions. Business stakeholders must be prepared to accept a range of outcomes

(say 60%-99% accuracy) while the models learn and improve.

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“AI capability can only mature as fast as your overall data management maturity,” Wand advised, “so create and execute a roadmap to move these capabilities in parallel.” If you want to ensure this solution is for you, download our free step-by-step guide on how to implement AI in your company. The answers to these questions will help you to define your business needs, then step towards the best solution for your company.

Education and training can help bridge the technical skills gap internally while corporate partners can facilitate on-the-job training. AI is already helping thousands of businesses and customers with daily transactions. I recommend starting small and fast so you can understand the logistics behind the technology without higher risks and make sure the company you are dealing with has trusted security standards and certifications in place.

The biggest challenges are people and processes.

Once the overall system is in place, business teams need to identify opportunities for continuous  improvement in AI models and processes. AI models can degrade over time or in response to rapid changes caused by disruptions such as the COVID-19 pandemic. Teams also need to monitor feedback and resistance to an AI deployment from employees, customers and partners.

A well-formulated AI strategy should also help guide tech infrastructure, ensuring the business is equipped with the hardware, software and other resources needed for effective AI implementation. And since technology evolves so rapidly, the strategy should allow the organization to adapt to new technologies and shifts in the industry. Ethical considerations such as bias, transparency and regulatory concerns should also be addressed to support responsible deployment. For this step in the process, you’ll want to brainstorm with various teams like sales, marketing, and customer service to learn what they feel would best help the company reach these goals. There are a wide variety of AI solutions on the market — including chatbots, natural language process, machine learning, and deep learning — so choosing the right one for your organization is essential.

The majority of business owners believe that ChatGPT will have a positive impact on their operations, with a staggering 97% identifying at least one aspect that will help their business. Among the potential benefits, 74% of respondents anticipate ChatGPT assisting in generating responses to customers through chatbots. According to the Forbes Advisor survey, AI is used or planned for use in various aspects of business management. A significant number of businesses (53%) apply AI to improve production processes, while 51% adopt AI for process automation and 52% utilize it for search engine optimization tasks such as keyword research. Large organizations may have a centralized data or analytics group, but an important activity is to map out the data ownership by organizational groups.

A company’s data architecture must be scalable and able to support the influx of data that AI initiatives bring with it. No AI model, be it a statistical machine learning model or a natural language processing model, will be perfect on day one of deployment. Therefore, it is imperative that the overall

AI solution provide mechanisms for subject matter experts to provide feedback to the model. AI models must be retrained often with the feedback provided for correcting and improving. Carefully analyzing and categorizing errors goes a long way in determining

where improvements are needed.

Software

Following closely, 40% of decision-makers see the potential for AI to increase revenue as a significant driver. AI can provide retailers with valuable insights into customer behavior and preferences, enabling them to make data-driven decisions that can increase sales and revenue. To get the most out of AI, firms must understand which technologies perform what types of tasks, create a prioritized portfolio of projects based on business needs, and develop plans to scale up across the company. Consumers, regulators, business owners, and investors may all seek to understand the process by which an organization’s AI engine makes decisions, especially if those decisions can impact the quality of human lives. Black box architectures often do not allow for this, requiring developers to give proper forethought to explainability. Data scientists must make tradeoffs in the choice of algorithms to achieve transparency and explainability.

Focus on business areas with high variability and significant payoff, said Suketu Gandhi, a partner at digital transformation consultancy Kearney. Teams comprising business stakeholders who have technology and data expertise should use metrics to measure the effect of an AI implementation on the organization and its people. What is interesting about AI is that all these models are scripts or pieces of code humans have been training for years. With this new era of AI, there is much more that businesses can do to benefit their internal operations and final customers. It requires lots of experience and a particular combination of skills to create algorithms that can teach machines to think, to improve, and to optimize your business workflows.

Assess each vendor’s reputation and support offerings, and find out if the solution is compatible with your existing infrastructure. But successfully implementing AI can be a challenging task that requires strategic planning, adequate resources, and a commitment to innovation. Let’s explore the top strategies for making AI work in your organization so you can maximize its potential.

• Begin by researching use cases and white papers available in the public domain.
• Don’t assume AI is always the answer, choose business objectives that are important for the business and that AI has a track record of addressing successfully.
• What is interesting about AI is that all these models are scripts or pieces of code humans have been training for years.
• Once your new AI program or technology is operational, it is time to test the system for a predetermined period of time.

Attempting to infuse AI into a business model without the proper infrastructure and architecture in place is counterproductive. Training data for AI is most likely available within the enterprise unless the AI models that are being built are general purpose models for speech recognition, natural language understanding and image recognition. If it is the former case, much of

the effort to be done is cleaning and preparing the data for AI model training.

Do we have executive sponsorship to infuse AI within existing business processes?

After the AI program becomes operational, now is the time to test the system to see how your efforts are helping reach your goals. When you know your metrics, such as order times, sales improvement how to implement ai in business and productivity, you can decide how to best implement AI in your business. Now that the preliminary stages of AI implementation are completed, the actual implementation of AI comes into play.

Learn more about how Epicor can help you lean into the future of retail by contacting us at or visiting epicor.com/retail. For example, AI can help cut down on the amount of time spent analyzing data which would otherwise take humans months to do. AI can work through millions of pages of text in just seconds and find out what patterns exist in the data.

• Understand the ethical implications of the organization’s responsible use of AI.
• Once the overall system is in place, business teams need to identify opportunities for continuous  improvement in AI models and processes.
• Many AI models are statistical in nature and may not be 100% accurate in their predictions.
• The winter 2024 issue features a special report on sustainability, and provides insights on developing leadership skills, recognizing and addressing caste discrimination, and engaging in strategic planning and execution.
• Depending on the use case and data available, it may take multiple iterations to achieve the levels of accuracy desired to deploy AI models in production.
• No AI model, be it a statistical machine learning model or a natural language processing model, will be perfect on day one of deployment.