Artificial intelligence (AI) is redefining the way enterprises operate. Organizations in almost every industry are rushing to embrace the technology, but it’s important to learn how to implement AI before investing in it.
As powerful as AI is, it’s still a tool at the end of the day. As such, successful AI implementation depends on how businesses use it.
Why Should You Implement AI?
Integrating AI into a company has many benefits. After all, there’s a reason why this technology has become as popular as it is. AI is transforming businesses faster and more dramatically than any previous innovation.
One of the most common benefits of AI is that it boosts productivity. It can automate repetitive office tasks to accomplish more in less time the same way robots have improved efficiency in heavy industries. On top of finishing these workflows faster, AI leaves human employees with more time to focus on the work they enjoy, helping them become more productive, too.
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AI also minimizes errors in analysis-based tasks. This accuracy can lead to more informed decisions or even give brands a glimpse into the future so they can prepare and prevent disruption.
As AI technology advances, the possibilities grow. It can now automate or otherwise improve business processes from quality assurance to customer outreach to content creation. These ever-expanding use cases mean generative AI alone could add up to $4.4 trillion to the global economy.
How to Implement AI in Your Business
Leaders who want to capitalize on that potential must first learn how to implement AI in business. The specifics of the implementation process can vary, but in general, AI integration follows these 10 steps.
1. Determine an Ideal AI Use Case
The first step is to decide where the firm should implement AI. Impactful AI project ideas can come from anywhere and apply to almost any function, but organizations should focus on their current pain points. AI systems are most effective when they solve an existing issue within the company.
Start by going over business objectives to see if there’s any process where current workflows fall short. These performance gaps may include inefficiencies, high costs, frequent errors, or a lack of direction and visibility. Once enterprises identify their biggest points to improve, they can consider how AI might help them close those gaps.
Compare these areas for improvement to AI’s known capabilities. Aspects like data analytics and real-time responsiveness are where AI is strongest. Anywhere these strengths overlap with existing problems is an ideal place to implement AI.
2. Define Specific AI Goals
Once brands know where they need AI, they can craft goals and expectations for their AI projects. These targets should be specific and measurable. That way, it will be easier to gauge the AI rollout’s effectiveness and tweak the model as necessary later.
A firm may recognize a virtual audience can significantly improve its return on investment, so it wants to drive online viewer engagement. In that case, it could measure current engagement metrics like comments and likes and set targets for a higher number of these interactions. That way, they could easily measure results after deploying a chatbot or other AI model.
Alternatively, a company may want to reduce bookkeeping errors. Its aspirations should include a target number of mistakes below their current number. Whatever the context, AI project goals should be specific and relate to current corporate needs.
3. Consider Your AI Readiness
One step many overlook when learning how to implement AI and machine learning is to assess the company’s AI readiness. This technology is complicated, and a lack of appropriate skills, experience or data makes it much harder to use effectively.
Businesses can build an AI team internally if their workforce is skilled in software development and has a grasp on how AI works. Those without much experience in data science and other related fields can still use AI, but they’ll require outside help. Similarly, organizations with limited usable data should emphasize data-gathering workflows before integrating AI.
Some staff training and reskilling may be necessary for successful AI implementation. The same goes for expanding IT resources to provide sufficient computing power and data storage space for the AI solution.
4. Compare Available Options
At this point, enterprises should have a plan for where to use AI, relevant performance targets and an idea of how much they can handle in-house. With these factors in mind, they can compare available AI software development solutions to find one that suits their unique needs.
AI solutions come in many forms, even from the same vendor. Ready-made, no-code AI models are ideal for those without in-house expertise or experience. Custom-built solutions are better when a firm has more experience and data or has niche needs. They should also consider their budgets and available computing resources, as different AI systems carry varying costs and IT requirements.
It’s also a good idea to compare AI solutions to the existing software stack. The more an AI system can integrate with the tools and data teams already use, the easier it will be to implement.
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5. Establish Clear AI Policies and Guidelines
After deciding on an AI partner or solution, businesses should take the time to develop usage policies. Because AI is such a powerful tool, it’s easy to misuse. Setting formal guidelines before deploying an AI algorithm will ensure a safer rollout.
These guidelines should cover where workers will use AI and where they won’t, who’s in charge of the AI project, what data the model will access, and how to secure that data. Brands should also develop a specific framework for any AI-powered decision-making. That includes determining how to review AI suggestions and which person has the ultimate authority. A human should always have the final say.
Creating a formal system for checking for and correcting AI bias and related ethical issues is also crucial. A staggering nine out of ten organizations have noticed at least one instance where problems like this have arisen with their AI projects. In light of that trend, companies must anticipate these concerns ahead of time to manage them effectively.
6. Gather Sufficient Data
There’s still work to do before firms implement AI in their website or other processes. Most importantly, they must collect enough data to train their AI model and deploy it effectively after training.
AI tools — especially machine learning models — require a lot of data to deliver consistent, accurate results. Enterprises can gather this information from Internet of Things (IoT) devices, customer relationship management (CRM) solutions, cloud computing platforms and other existing IT tools. Teams can also use publicly available training datasets to fill any gaps, but augmenting these with a brand’s specific data is important.
Businesses should pay attention to data quality, not just quantity. Poor-quality data costs organizations $12.9 million a year on average and is easy to miss. They can avoid these outcomes by cleaning data before feeding it to AI models, only using information from trusted sources and ensuring all training data is relevant to their use case.
7. Build and Train the Model
Once companies have sufficient data, they can begin the model training process. Some machine learning solutions come pre-trained but require adjustments. Other models need more involved training.
Firms should select an AI algorithm that suits their needs, such as a regression model for predictive analytics or a clustering algorithm for image recognition. Then, they input data and correct it as necessary until it can consistently achieve the desired level of accuracy.
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Model training will vary between different algorithms. Supervised models — as their name implies — require frequent manual adjustment, while unsupervised ones will handle much of the correction themselves. Regardless of these considerations, AI teams should regularly review the model to check for bias or ensure explainability according to the enterprise’s AI guidelines.
8. Deploy the AI Solution
Brands are now ready to implement AI in their operation of choice. While it can be tempting to deploy this technology across the entire corporation, it’s best to start with a single use case. That way, firms can spread out the costs and optimize the model while any issues won’t carry substantial consequences.
The teams in charge of the AI project should inform others about any disruption they may experience in the initial stages. Similarly, they should encourage communication to learn what people like about the solution and any issues they encounter.
Many AI models require ongoing access to real-time data. Consequently, businesses should provide this information as necessary and connect the solution to any related IT platforms to ensure it works as intended.
9. Monitor and Adjust
While brands should understand how to implement AI chatbots and related solutions at this point, the project is still not over. Those that want to make the most out of their AI investments should monitor and optimize their model over time.
AI systems are rarely perfect at first, so organizations shouldn’t expect them to be. Rather, they should measure the same key performance indicators (KPIs) they used to set their AI goals to quantify the solution’s performance. These KPIs will reveal if the project has gone as planned or if it may need review and adjustments to achieve the predetermined targets.
Unrealistic expectations are a common struggle for artificial intelligence in small businesses, so it’s important to take a longer-term approach to AI implementation. Just because an AI project fails to meet initial goals initially does not mean it’s a failure. Rather, it means the firm should tweak the model or adjust how it uses it. Eventually, small changes like this can lead to better results.
10. Expand AI Models to New Applications
After some time and adjustment, the AI model should start showing positive changes in its workflow. Once companies see these returns, they can expand their AI initiatives to new business operations.
Enterprises should go through these same 10 steps to implement AI in new use cases. However, they’ll have an easier time doing so because they’ll know what to expect and have a better understanding of their AI talent, data and guidelines.
As their expand their AI projects, they should consider their previous experience with the technology. What worked well? What needed more attention than teams anticipated? Taking these victories and obstacles into account will make it easier to ensure a smoother AI expansion.
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Specific Steps to Implement Generative AI
It should now be clear how to implement AI in small business applications. However, the AI implementation strategy may need to adjust when considering generative AI.
Implementing generative AI is largely the same as any AI project but carries a few unique considerations. Most importantly, organizations should consider how their generative models impact copyright concerns. AI-generated images and text have become a topic of debate in terms of their ethics, so corporations should approach them carefully.
They should only train generative models on content they have the original creator’s informed consent to use. It’s also best to avoid using these models for profit-driving purposes. Instead, use AI-generated content as inspiration or a first draft or apply it to less critical applications like creating featured images for web articles.
How Much Does It Cost to Implement AI?
Some businesses may now wonder how much it costs to implement AI. Pricing varies depending on the specific use case in question, the AI vendor, the size of the implementation and other variables. That said, most AI development projects cost between $10,000 and $49,999 on average.
Enterprises can easily spend less than that on AI implementation, and they can also spend a lot more. The best way to get a more reliable estimate is to work with a trusted AI consulting partner and work with them to get specific insights.
Follow These Steps to Implement AI Today
AI implementation is much easier once brands understand what the process looks like. Get started with the corporate AI journey today and follow these steps to make the most of this technology. Learning how to implement AI is the first action companies can take to bring themselves into the future.
