Throughout history, certain technologies have reshaped industries, redefined norms, and propelled businesses to unimaginable success. In recent years, artificial intelligence (AI) has become that disruptive force, revolutionizing industries from healthcare to finance and everything in between. With the ability to analyze large amounts of data, make decisions without human intervention, and continuously learn and adapt, AI technologies have the potential to significantly improve business operations and drive growth.

However, given the many forms AI can take, many businesses struggle to successfully incorporate it into their operations. This is why businesses must take a strategic and thoughtful approach to implementing any AI model.

What are the common types of AI companies can leverage?

Before diving into how to implement AI, it’s important to understand the various types of AI and how they can be applied in different industries. Here are the most common types of AI businesses use today:

  • Machine learning (ML) – ML is the foundation of AI. It involves training algorithms on massive data sets to enable them to make intelligent decisions and predictions, and even take actions autonomously. This can be especially useful for predictive analytics where businesses can use past data to anticipate future cash flow patterns, customer behavior, and even system failures. Based on this information, ML software can then choose the best course of action to take without human intervention.
  • Natural language processing (NLP) – NLP is the technology that enables machines to understand and interpret human language. It’s the technology used on most virtual assistants, such as Siri and Alexa. Businesses can use NLP for personalized chatbots to answer customer inquiries or for sentiment analysis to analyze customer feedback.
  • Generative AI (GenAI) – GenAI is a highly popular form of AI that can create new content, ideas, and solutions using massive data backlogs that have been fed into the program. All a user has to do is provide prompts and the AI can generate new images, text, or even music. GenAI is commonly used in creative outlets such as content generation, design, and audio and video production, but it’s also incredibly helpful for custom software development and programming.
  • Computer vision – Modern devices with cameras and sensors use AI’s computer vision to process visual data. For example, self-driving cars rely on computer vision to identify pedestrians, traffic lights, and road signs. In the business world, computer vision can be used for quality control in manufacturing or inventory management in retail.

What factors should companies consider when implementing AI?

To ensure a successful AI implementation strategy, businesses must consider these key steps:

Identify business needs and objectives

Before investing in AI, companies must first justify the need for it. This means identifying specific business problems or goals that AI can help solve or achieve. For example, companies with high volumes of customer data may benefit from using AI-driven analytics to better understand their customers and improve marketing strategies. Alternatively, professional services and finance companies looking to eliminate repetitive tasks may benefit from utilizing the automation capabilities of AI.

Whatever the case may be, clearly defining the objectives and expected business value will help guide decisions on which type of AI technology to invest in.

Choose the right AI solution

Aligning an AI solution with your business needs and objectives unlocks its true potential and maximizes its impact. GenAI tools such as ChatGPT and Gemini, in particular, work well for customer-facing applications, while solutions like IBM Watson Analytics are better suited for predictive analytics. Some companies may even choose to deploy their own custom AI solutions using platforms like Azure Machine Learning or Google Cloud AI Platform.

Beyond choosing the right type of AI, businesses should also consider factors such as cost, user-friendliness, scalability, and compatibility with existing systems. Doing thorough research and possibly consulting with AI experts can help companies make the best decision for their specific needs.

Build an AI-ready infrastructure

Implementing AI also requires a solid IT infrastructure to support it. This includes having enough data storage capacity, high-speed and reliable internet connectivity, and proper IT support. Without these elements in place, businesses may not be able to fully leverage the capabilities of AI.

Establish an AI task force

Implementing AI involves more than just installing software or purchasing hardware. It requires a dedicated team of individuals who understand the technology and can effectively integrate it into business operations. This task force should include IT professionals, data engineers, and business leaders who can work together to identify use cases for AI and oversee the implementation process. Managed IT services providers are often a valuable resource for businesses looking to implement AI, as these providers have the expertise and resources to assist with infrastructure setup and ongoing maintenance.

Companies should also consider bringing in external experts and early adopters to kick-start pilot projects. A pilot program tests the AI solution in a controlled environment, evaluates its performance, gathers feedback, and makes refinements before full-scale implementation.

Develop a data strategy

To make sure an AI system produces accurate and useful results, it needs to be fed quality data. This means having a solid data strategy that encompasses the following:

  • Data inventory – Identify the types of data (structured, unstructured, internal, external) required for AI models.
  • Data location – Determine where data is gathered and stored, including internal databases, cloud platforms, external APIs, customer interactions, and IoT devices.
  • Data management – Establish processes for data collection, security, and access to ensure data quality, privacy, and compliance.

Start small and scale gradually
Rather than attempting a large-scale AI deployment from the outset, start with small, focused projects with clear objectives and measurable outcomes. This approach minimizes risks, allows for iterative improvements, and demonstrates the value of AI to stakeholders. When initial projects bear fruit, companies can then scale up gradually across different departments and business functions.

Incorporate AI into daily tasks

For AI to deliver long-term value, it needs to become an integral part of daily operations. This requires training employees to use AI tools and systems, integrating AI into existing processes, and developing new workflows that incorporate AI capabilities.

The AI task force should monitor the performance and usage of AI systems, gather feedback from employees, and make continuous improvements to ensure the technology is fully integrated and utilized effectively. Doing so ensures AI models are constantly optimized and remain aligned with business objectives.

Stay on the bleeding edge of AI

An AI program’s current iteration may not be the same a week from now, so companies must stay up to date with the latest developments and proactively seek new opportunities to leverage the technology. Staying current also means investing in ongoing training for employees to ensure they have the skills and knowledge necessary to use new AI tools and technologies.

Our experts at Dynamic Solutions Group are at the forefront of AI technology, ready to guide you through every step of your AI implementation process. From assessing your business needs to deploying sophisticated AI tools that augment your operations, we can unlock the full potential of AI for your organization. Contact us today to discuss how we can drive your business forward.