How To Integrate AI Into Your Business: A 2024 Guide

If you’re an IT professional or executive, the question of how to integrate AI into your business has probably been top of mind since the recent generative AI boom. You know you should be using AI tools to improve your operational efficiency, but you might worry you lack the policies, data quality and implementation strategy to do so effectively.
Though valid, these concerns shouldn’t hold you back. The potential challenges of AI adoption are no match for a thoughtful and formalized AI integration process that helps you spot impactful use cases for AI investment, find and implement the right AI solutions in a compliant and efficient manner, ensure employee adoption, and maximize the ROI of AI. Our six detailed steps will show you how to integrate AI into your business.

KEY TAKEAWAYS

Integrating AI into your business involves assessing business needs, developing a strategy, preparing your data, choosing the right vendor, implementing the AI, and monitoring its performance. (Jump to Section)
Three AI tools every business can easily implement for increased efficiency include Grammarly, HubSpot CRM, and Fireflies.ai. (Jump to Section)

AI business integrations promise benefits for every industry, including retail, healthcare, manufacturing, and financial services. (Jump to Section)

TABLE OF CONTENTS
ToggleStep 1: Assess Your Business Needs for AI IntegrationStep 2: Develop an AI Integration StrategyStep 3: Prepare Your Data Infrastructure for AI IntegrationStep 4: Choose the Right AI Solutions for IntegrationStep 5: Implement AI Integration ProjectsStep 6: Monitor, Evaluate and Iterate Your AI IntegrationFinancial Implications of AI IntegrationTop 3 AI Tools for Business IntegrationCase Studies: Real World Examples of Successful AI Integration 3 Best Courses to Learn AI Integration for BusinessFrequently Asked Questions (FAQs)Bottom Line: Embrace AI Integration For Competitive Advantage

Step 1: Assess Your Business Needs for AI Integration
Before testing any AI tools, identify your business’s pain points and inefficiencies that AI can solve. Note that the majority of AI use cases for business fall into three categories: automating routine tasks, personalizing customer interactions with data, and improving analytics for better decision-making.
The best way to assess your business’s specific needs is to talk with your staff to learn about their current workflows. Which tasks are highly repetitive, boring, or error-prone? Where are they overwhelmed? This employee-first identifies problem areas and involves team members in the process, a key factor in improving the odds that employees will actually use the AI tools you implement.
Once you’ve identified use cases, prioritize projects based on their effect on your larger business goals. For example, if the focus for Q1 is to improve customer support to boost retention and the main issue holding your team back is slow response times, an AI chatbot might be a great first integration. Take into account the cost in terms of time and money associated with the integration. Gather dissenting opinions on these core issues.
Pro Tip: Because AI companies tend to offer solutions tailored for a set of problems in a specific department—an AI CRM solution might offer an AI chatbot, AI lead scoring, and other AI tools for issues relevant to sales teams—you might consider grouping similar problems by department. For example, tag all sales-related issues as “sales.”
Step 2: Develop an AI Integration Strategy
Start with the most impactful artificial intelligence project and develop an AI integration strategy for it. The strategy will be the go-to resource for anyone involved in the evaluation and implementation of the AI tools and will help you efficiently implement a tool that achieves your goals, fits your budget and aligns with your compliance standards.
Your AI integration strategy should include the following considerations:

Objectives: What two-to-three goals do you want to accomplish with your AI integration project?
KPIs: Which metrics will you use to measure the impact of your AI adoption on those objectives?
Integration Plan: What steps are needed to get this integration from tool evaluation to full-functioning integration?
Technologies Required: What software, networks, and apps do you need to accomplish the objectives?
Data Sources Used: Do you have the data available for enacting this integration, or do you need access to more data?
Monitoring Processes: How will you monitor the effectiveness of the AI tool?
Resources Needed: What resources will you need to buy or create to facilitate employee training, a smooth transition, and usage standardization?

This stage may require signoff from upper management. Tie your AI project to overarching business goals and write a summary of how you expect the AI integration to boost metrics that matter to executives. But remember that executives aren’t the only ones you should be wooing—your employees’ opinions matter as well, not to mention their sense of job security, which AI tends to threaten.
Build an AI-ready culture with change management best practices. Consider how you’ll speak to employees about the AI project, enlighten them on the benefits, and think of ways to train them to use the new tools or retrain them to adjust to new processes. This should result in a smoother adoption of AI among your workforce.
Pro Tip: Assign the role of AI Expert to an employee or two with technical savvy and a strong interest in AI. Put them in charge of exploring the AI market, testing new tools, and supporting fellow employees in learning to use the tools you decide to integrate.
Step 3: Prepare Your Data Infrastructure for AI Integration
As a general rule, the better your data, the better the AI performs. For example, integrating ChatGPT into your business on its own will be a value-add, but not as powerful as if you trained it on your company’s products, customers and documents. For your AI integration project, identify which type of data you need. An AI forecasting tool, for example, will require sales data, preferably historical sales data captured in your CRM. Then, check to see if you have that data and if it is properly vetted and prepared.
If you lack the data, you can buy data from third-party sources such as data vendors or licensed databases. Make sure your vendor is ethically sourcing data—if you need contact data for your AI lead routing, make sure it’s compliant with data privacy regulations. If you already have the data, here’s how to get it into top shape for powering your AI:

Conduct Data Hygiene Practices: Ensure data is routinely cleaned, labeled, and formatted correctly, with redundancies eliminated and errors flagged. Some of your data storage tools might already be able to do this automatically.
Implement Data Governance Practices: To keep your data secure and your usage compliant, establish protocols around access permissions, secure storage, and data curation. Check with relevant government regulations around this type of data to inform your governance practices.
Consider Technology Integrations: If your data is stored in several tools, the AI tool might be able to seamlessly integrate with them and extract and use their data for its automations.
Consolidate Siloed Data: Your AI integration might require data that’s currently housed across several tools and databases. Consolidate the data into central data lakes for efficient processing.

Creating a new data infrastructure for your AI project is like building an oil refinery. You’re creating the resources, practices, and technologies to take crude data and turn it into a powerful, usable source of energy for your AI tools.
Pro Tip: Realize that there are many data quality tools and solutions to help you improve the quality of your data. Being fully aware of these solutions and how they support your data infrastructure is an important aspect of getting the most from generative AI and data analytics.
Step 4: Choose the Right AI Solutions for Integration
Choosing the right AI solutions for your organization requires a deep dive into the features and use cases of various AI tools coupled with an ongoing awareness of cost, feasibility, ease-of-use, and integration capacity. When choosing AI solutions, there are a few considerations to keep in mind.
Consider Off-the-Shelf vs. Custom AI Solutions
Your first choice is between off-the-shelf and custom AI solutions. The former are pre-built AI tools you can use out of the box—common examples include SaaS solutions, AI apps, or robotic process automation tools. You can implement them quickly and cost-effectively, and the vendor provides ongoing maintenance, user support, and a community of other users who can help you get the most out of the tool.
If you find that all the solutions are too generic for your specialized problem, then consider a custom AI solution. Custom AI solutions are AI tools developed for your business’s specific needs. Although it can be time-consuming and costly to develop and maintain them, these tools will truly fit your company’s goals. For example, a sales coaching company might create a custom conversational AI that helps customers get feedback and advice based on their past performance, physical profile, and fitness goals.
Evaluate AI Vendors
When evaluating potential vendors, start by exploring online for available solutions and make a short list of two-to-three to evaluate in depth. Read user reviews, watch live demos, explore their features and case studies online, and set up a call or live demo if possible. Here are some factors to consider:

Alignment with Business Objectives: Define the problem you’re trying to solve and ensure the AI solution offers features tailored to that specific issue.
Integration Capabilities: Consider how well the AI tool can integrate with existing systems and workflows. Seamless integration can significantly impact adoption rates and overall effectiveness.
Scalability and Flexibility: As business needs evolve, the AI solution should be able to scale accordingly. Find tools that can adapt to changing requirements and grow with your organization.
Ethical and Legal Considerations: Evaluate the AI tool’s compliance with relevant regulations and ethical guidelines. This includes data privacy, fairness, and potential biases in the AI’s decision-making process.
Total Cost: Beyond the initial or monthly price, factor in costs for implementation, training, maintenance, and potential customization.
Vendor Support and Expertise: Assess the level of support and domain expertise provided by the AI vendor. A strong partnership can be crucial for successful implementation and optimization.

If the vendors offer free trials, use them to evaluate the tool and get a feel for how these types of tools work and what they can do. Ask employees if they’re already using any AI tools to streamline their workflows—for all you know, someone’s already found the ideal solution.
Evaluate Development Partners
As with vendors, you can use many of the same methods to evaluate development partners if you’re going the custom route. Here are some things to consider:

Experience in the AI Domain: How knowledgeable are they in the AI technologies required for your project?
Previous Projects: Have they built custom AI solutions similar to the one you’re trying to create? Check out their portfolio to see if they’ve worked with clients similar to you.
Communication: Are they in a different time zone? How frequently do they communicate and which methods do they use?
Project Management Style: What is the timeline? How do they handle adjustments to project scope and facilitate collaboration with your team? How do they manage data security?

It’s best to get on the phone with the potential developer and ask them questions about the above to get a feel for their compatibility with your business’s needs and goals. Whether you’re getting an off-the-shelf solution or building one with a partner, a diligent investigation process will ensure you find a solution that will cost-efficiently achieve your goals.
Assess Integration with Legacy Systems
If you’re using legacy systems, data silo issues might emerge if they can’t integrate with new AI software. Try to find tools that do integrate. If you can’t, this may be a sign it’s time to migrate the data to a new platform. This involves data extraction, mapping, updating, testing, and migration.
Pro Tip: The rapid pace of change among AI software creates a core challenge in finding the right AI solution for your business. As the AI applications change and your business changes, be prepared to re-evaluate your selection on a regular basis.
Step 5: Implement AI Integration Projects
When implementing the winning AI solution, you’ll work with your vendor or developer to integrate the technology into your existing tech stack and workflow. Here are some tips for implementing AI integration projects successfully:

Start with Pilot Projects: Start small so you can work out the kinks in your implementation process. For example, if you bring on an AI copywriting tool, you might use it first for designing SEO blog post briefs.
Scale Successful Implementations: If the implementation is successful, scale it appropriately to take on more work. To go with the AI copywriting example, you might start using it for ad copywriting brainstorming as well.
Manage Change and Overcome Resistance: Ensure that employees are aware of the upcoming change ahead of time. Keep an open-door policy for their questions and concerns and hold meetings and “firesides” to discuss the benefits of the AI solution and how it will be used. Involving them in the process is the best way to avoid resistance.

Address Skills Gaps and Training
According to the AI Skills Report, 94 percent of IT practitioners say AI integrations will fail if employees are inadequately trained to use these tools. That’s because AI doesn’t work on its own. We aren’t yet in the era of fully autonomous AI agents. The impact of an AI still depends on the person holding the controls.
Identifying and closing skill gaps among your end-users is critical to the success of your AI integration project. Hold initial training sessions for how to use the tool while ensuring employees have the knowledge needed to think critically about how to use it effectively. That might mean sending them to a bootcamp or signing them up for online courses around this domain of AI tools.
You’ll also want to support them with ongoing training and answers to their questions. Create an IT knowledgebase where employees can access the AI-solution-related documents, best practices, protocols, and processes for using the tool, as well as FAQs for common questions. Develop this knowledge base over time as you learn more about how your team is struggling with or mastering the solution; this feedback should be collected in ongoing surveys and team meetings.
Pro Tip: Create a Slack channel called “AI Talk” where employees can share how they’re using the AI solutions, surface bottlenecks, ask questions, and learn from each other. The more they talk about AI and the more they see it in action, the more comfortable they’ll become with it. Ignorance and avoidance are often the sources of existential dread.
Step 6: Monitor, Evaluate and Iterate Your AI Integration
As you continue to use and scale the tool, it’s important to monitor, evaluate and iterate the AI integration to optimize its performance as it relates to your business goals. The following best practices can help ensure success in this stage of the AI integration process:

Track KPIs and ROI: Monitor the critical performance metrics that your AI is meant to impact—for example, an AI sales training tool should speed up a new rep’s onboarding time. Tracking relevant metrics informs you about the areas where your AI project is underperforming and how to improve it.
Continuously Improve AI Models: Remedy the AI integration’s shortcomings by taking a deliberate approach to improving the AI model. This might mean improving data quality and access, or training staff to bridge skill gaps, and you may need to buy higher tiers of the AI tool to open up more functionality.
Calculate ROI for AI Projects: Calculate the value created by the AI integration and subtract the total costs of maintenance, training, resources, and fees.

Pro Tip: Make sure your IT team is continuing to learn about AI in business. They should be staying abreast of the latest development and best practices, and implementing them into your AI workflow. This will ensure that you’re constantly optimizing your AI integration and getting the most possible out of the solution.
Financial Implications of AI Integration
When considering the true cost of your AI integration, you have to take into account initial investment costs, ongoing operational expenses, and the ROI of the integration.
Initial Investment Costs
There are multiple initial investment costs you may encounter when integrating AI into your business. If you’re using an off-the-shelf solution, the initial investment will likely only be the ongoing monthly fee or purchase price and sometimes an implementation or integration fee. You might also need to invest money in training your employees to use the tool.
If you’re building a custom solution, there are going to be more initial costs, such as hardware purchases—like AI servers, which can cost thousands of dollars—and hiring costs for new employees like machine learning engineers, data scientists, software developers and other AI experts. The more custom and complex the solution you’re having built, the higher the costs. For instance, the cost of outsourcing the creation of a custom AI analysis tool can reach into the hundreds of thousands.
Ongoing Operational Expenses
The ongoing maintenance costs of an AI integration can vary significantly based on whether you opt for a custom-built model or a pre-built solution. With a custom model, you’ll likely face higher ongoing costs due to the need for continuous model updates, retraining with new data, and potential infrastructure scaling as your needs grow. You’ll also need to maintain a team of AI specialists to manage the solution, which can be expensive. For instance, you’ll need to keep an in-house IT staff around to troubleshoot any issues that arise with the AI solution.
In contrast, a pre-built solution often has lower maintenance costs, as the provider handles updates, bug fixes, and improvements. Made for mass usability, the solution will likely also require less in-house AI expertise. However, you may face ongoing subscription or usage fees, and there could be costs associated with API calls or compute resources.
Return on Investment (ROI) Considerations
The benefits of AI integration can range from a small boost in operational efficiency to an impressive winning edge over your entire competitive cohort. Calculating your integration’s ROI is best done months after the integration, when the asset is officially in the maintenance and monitoring stage of the asset management lifecycle. Then you can calculate how the AI solution has improved your KPIs, and estimate the connection between those improvements and your business’s revenue, efficiency, and customer satisfaction.
Top 3 AI Tools for Business Integration
Below are three great AI tools that are easy to implement for beginners looking to bring artificial intelligence into their business operations. Almost every type of business can get something out of Grammarly, Hubspot or FireFlies.ai.

Grammarly: Helps Content Creators
Grammarly is not just for catching grammatical errors or spelling mistakes. It’s a full-scale AI writing assistant that helps content writers and marketers create better content faster. Its AI tools include article summarization, full-paragraph writing, sentence rewriting, idea generation, and more. Its use-cases range from helping your team craft better internal emails to powering your content marketing strategy.

Visit Grammarly

HubSpot Sales Hub: Supports Sales, Customer Service, and Marketing
HubSpot Sales Hub is an all-in-one CRM with tons of useful AI features to automate simple tasks and help marketers, salespeople, and customer service reps make better decisions and gain insight into their customers and performance. The AI-powered features include an AI chatbot, predictive lead scoring, AI content and email writing, and advanced analytics.

Visit HubSpot

For more marketing automation, check out our detailed guide on the best AI marketing tools.

FireFlies.ai Assists With Meetings
Fireflies.ai is a meeting assistant tool that will attend your meetings, zooms and calls, and transcribe, record and summarize them, while also leaving efficient notes on the most critical aspects of the call. It can be used for both internal and external meetings. The main benefit it offers is that you’ll be able to focus on your meeting instead of jotting down notes, and after the meeting all employees have access to a brief summary of what was discussed and decided.

Visit Fireflies.ai

Case Studies: Real World Examples of Successful AI Integration 
There are powerful AI use cases for every industry, from food and dining to space travel. Here’s a look at how AI is being used in three of the largest industries: retail, manufacturing, and healthcare.
Retail: Enhancing Customer Experience
For online shopping, virtual shopping assistants like AI chatbots use natural language processing to help customers find products, answer questions and make recommendations in real-time via chat or voice interfaces. This provides personalized, 24/7 support to shoppers browsing online stores.
As for in-store shopping, shoe stores might use a computerized foot screening device to analyze a buyer’s foot and its arch, size, and other specifications to find the perfect fit and shoe for the buyer. I used one recently and learned that my feet are nearly an inch different in size. When it comes to supply-chain, note Sparrow, Amazon’s warehouse robot, which uses computer vision and AI to accomplish tasks like retrieving individual products from storage bins and moving them.
Manufacturing: Optimizing Production Processes
AI in manufacturing can automate repetitive manufacturing tasks. Technology has been doing this for decades. But the use cases don’t stop there. Companies are using AI to improve their decision-making and analyze manufacturing processes to find areas for improvement.
They’re also using it to improve their asset management process. GE, for example, uses its Predix Platform to monitor the health of its equipment and other assets and predict when they’ll need repairs. AI and big data can also help manufacturers via demand forecasting, quality control, and supply chain optimization.
Healthcare: Improving Diagnostics and Patient Care
In healthcare, AI can help medical professionals more accurately and quickly diagnose their patients by analyzing patient data and other intel. Its ability to closely analyze X-ray images is much better than the human eye. AI can also help patients manage and monitor their ailments at home.
For instance, a diabetic patient can use a glucose monitor and connect it to an AI app that makes suggestions to the patient based on their glucose levels. AI healthcare software helps healthcare facilities improve diagnosis and care efficiency and as a result take on more patients, without sacrificing care personalization or moments of quality bedside manner.
3 Best Courses to Learn AI Integration for Business
Investing in AI tool acumen for yourself and your employees will help your business more easily identify issues that AI can solve, find the right tools, and implement them strategically. I recommend three online courses to help you learn more about AI integration for business.
AI For Business
UPenn offers the AI For Business specialization class through the Coursera platform. The specialization consists of four self-guided courses of six to nine hours in length:

AI Fundamentals for Non-Data Scientists
AI Applications in Marketing and Finance
AI Applications in People Management
AI Strategy and Governance

By taking this course you’ll come away with a strategy for implementing AI, machine learning, and big data across your business.

Visit UPenn AI For Business

Artificial Intelligence: Business Strategies and Applications
UBerkley’s intensive online course will give you a foundation in the applications of AI in business. Over two months (four to six hours per week), you’ll learn AI’s current capabilities, technical AI concepts for talking with technical teams, how to leverage generative AI, how to manage AI integrations, and more. It costs $2,950 and is designed for businesses looking for an academic, project-rich education in the fundamentals of AI for business.

Visit AI Business Strategies

Revolutionize Your Marketing With AI
CXL’s short course Revolutionize Your Marketing With AI will teach you about introducing generative AI tools like Chat-GPT into your marketing workflow. It’s designed for beginners and costs $299. Skills you’ll learn include A/B testing, marketing with AI, experimentation, AI-powered website optimization, and AI in the sales funnel.

Visit Revolutionize Your Marketing With AI

Frequently Asked Questions (FAQs)
How Can AI Add Value to a Small Business?
AI can help your small business in a number of ways, from acting as your personal assistant and answering customer inquiries to automating tasks for accounting, content creation, and finance.
How Is AI Used in Business Strategy?
Many businesses use AI analytics tools to uncover insights into their business processes, target audience and competitive landscape, and to inform and refine their business strategy.
Can you use AI to start a business?
While AI won’t build businesses for you while you sip cocktails on the beach, it can help you brainstorm product ideas, conduct market research, and automate many of the tasks involved in starting a business, from building your website to landing your first client via email outreach.
Bottom Line: Embrace AI Integration For Competitive Advantage
Integrating AI into your business can help you boost productivity, reduce human error, and increase the quality of your team’s output across a variety of departments, from HR and finance to sales and marketing. It can also turn your data into business intelligence and help you create a more data-driven organization that regularly outsmarts the competition.
While AI integration is possible for even the smallest of businesses, it’s important to take your time with implementation, train employees, and consider legal, ethical, and data security factors when adding new AI solutions to your tech infrastructure. Even in this fast-paced environment of AI, the business world rewards patience and careful planning.
To find top vendors to hire for AI integrations, see our industry roundup, Top 150 AI Companies.
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