by Nathan Whittacre
As businesses continue to navigate the complexities of digital transformation, the integration of Artificial Intelligence (AI) is no longer just an advantage but a necessity. AI offers unparalleled opportunities for innovation, efficiency, and competitive differentiation. In article one of this series, we discussed the history and building blocks of Artificial Intelligence. Article two focused on some implementations that businesses are already making in their operations. Building on our discussion of AI’s importance, this article outlines practical steps your business can take to effectively implement AI technologies.
Step 1: Assessing Your AI Readiness
Evaluate Business Needs and Goals:
One of the biggest issues that I see with companies trying to implement new technologies is that they have a solution looking for a problem. Instead of finding an AI product and forcing it into operations, begin by defining the specific problems you want AI to address. Is it to enhance customer service, optimize operational efficiency, or perhaps bolster cybersecurity? Aligning AI objectives with your broader business strategy ensures that the technology serves your core business needs rather than adopting technology for its own sake.
I recently shared on our podcast that Stimulus is working on implementing AI technology in our inside sales department. We’ve struggled with hiring and managing inside sales development representatives, so we’ve implemented a new technology to use AI for prospect interaction. We’ve had success in the beta testing, including getting a few prospects signed up for one of our monthly webinars. We had a problem and used AI technology to solve it.
Assess Current Infrastructure:
Examine your existing IT infrastructure to determine its capability to support AI technologies. This includes evaluating your hardware for performance adequacy, software for compatibility, and data infrastructure for scalability. Upgrades might be necessary to ensure that systems can handle advanced AI functions or integrations with new AI systems. One of the biggest advantages of AI is automation. If your current infrastructure can’t integrate with the new AI systems, you will have to invest in additional labor to manage manual processes, thus not providing the ROI that you were originally looking to achieve.
Skill Gap Analysis:
Identify whether your current team possesses the skills needed to implement and manage AI technologies. Often, businesses may need to consider hiring new talent or offering training programs to upskill existing employees, ensuring they can manage and work alongside AI effectively. Because AI technologies are often in BETA stage development, it will take expertise internally to use the systems or you can contract with experts to implement the technologies.
Step 2: Planning AI Implementation
Choosing the Right AI Solutions:
Selecting the right AI tools is crucial. Whether it’s machine learning models to predict customer behavior or natural language processing systems for enhancing customer service, each AI solution should be chosen based on its ability to meet specific business goals. Evaluating various AI products for their reliability, support, integration ease, and scalability is essential.
There may be limited “off the shelf” products available for your business goal. One of our customers contacted us about implementing AI to perform diagnostic imagining analysis. For example, a company has developed software for cancer screening. For this client, there isn’t specific software for their screenings, so a very specific application that would have to be developed. This type of implementation would require a large data set to build a new AI model. It is possible to do and would require a development team to create.
Developing an AI Roadmap:
Develop a clear and structured implementation plan that includes both short-term and long-term AI goals. This roadmap should detail the phases of AI integration, timelines, expected outcomes, and the metrics for measuring success. It serves as a guide to keep the implementation on track and aligns all stakeholders with the project’s objectives. Your company should have a “champion” for each AI deployment. This person is responsible to ensure the new technologies are fully adopted in the company’s operations. They should also have the answer to “why” the technology is implemented and stay focused on the key outcomes.
Budgeting for AI:
Implementing AI requires a clear understanding of the financial investment involved. Prepare a detailed budget that includes costs for AI software, hardware, personnel, training, and any unforeseen costs. Estimating potential return on investment (ROI) will also help in justifying the AI initiatives to stakeholders.
Step 3: Executing AI Integration
Partnering with AI Vendors:
Choosing the right vendor or technology partner is vital for successful AI integration. Look for vendors with proven expertise in your industry and a track record of robust support and service. Establish clear agreements on deliverables, timelines, and post-implementation support.
Data Management Strategies:
AI systems require high-quality data to function effectively. Implement data management practices that ensure data integrity, security, and accessibility. This involves setting up systems for data cleaning, integration, and governance to maintain data quality throughout its lifecycle.
As I mentioned before, integration with other systems is key. Take the time to make sure the integrations work successfully. You can use a tool like Zapier to move data between systems easily. It can be programmed to do some AI work also, with built in links to ChatGPT for data analysis.
Pilot Projects:
Starting with pilot projects allows you to gauge the effectiveness of AI in your operations without fully committing all resources. These projects serve as a test bed for fine-tuning AI deployments and can demonstrate early wins to build confidence within the organization.
Step 4: Managing AI Operations
AI Maintenance:
Continuous monitoring of AI systems is essential to ensure they are performing as expected. Employ tools and practices that help track performance, identify issues, and provide insights for improvement. AI systems require regular updates and maintenance to function optimally. Establish procedures for routine checks, updates, and troubleshooting. This proactive maintenance helps prevent downtime and ensures the AI systems evolve with changing business needs. Especially if the AI system is customer facing, check the interactions to make sure that the system is responding how it was designed. Some AI systems can go “off the rails” in the learning process. Ensuring compliance to internal policies and procedures is essential.
Ethics and Compliance:
Adhering to ethical standards and compliance is crucial, especially when AI is involved in data handling or customer interactions. Establish guidelines that comply with legal standards and ethical norms to foster trust and integrity in your AI initiatives. Speak with your attorneys to ensure that you are complying with all data storage and retention regulations. Also, ensure that the AI system has been trained with a broad spectrum of data to ensure that it complies with your company’s ethical standards.
Continuing Learning and Improvement:
AI technology is always changing, so it’s important to keep learning about new developments. You can stay up-to-date by reading articles, going to tech conferences, or joining online groups that talk about AI. It’s also a good idea for teams to learn together through training programs or classes that teach about new AI tools.
Regularly check how well the AI tools you use are working for your business. This means looking at what’s working well and what could be better. Staying in touch with AI experts and companies that make AI technology can also give you early access to the newest tools and ideas. Keeping up with these changes helps your business stay ahead and make the most of AI technology.
Where to go from here?
Implementing AI in your business operations can seem daunting, but with a systematic approach, it is achievable. These practical steps provide a framework to guide your AI journey, ensuring that AI integration is smooth, effective, and aligned with your business objectives. As AI continues to evolve, staying proactive and informed will allow your business to continue reaping its benefits.
In the next article, I’ll discuss the dangers and risks of AI and how to overcome them. I believe that there are serious consequences of ignoring automation and AI in business and similar implications of ignoring this major shift in how the world operates.
Embracing AI is not just about keeping up with technology trends—it’s about staying ahead in a rapidly changing business environment. With these steps, you can ensure that your business not only adapts to the new landscape of AI but thrives in it.
