Artificial Intelligence is no longer just a futuristic concept; it’s becoming an essential component in modern software applications. But how do you take the leap from discussing AI to successfully integrating it into systems already in place? If you’re navigating this challenge, you’re in good company. We’ve spoken with Presidents and CEOs who’ve experienced firsthand what it takes to weave AI into existing software infrastructure. Whether it’s ensuring AI delivers actionable insights or implementing it in a way that enhances rather than disrupts, these experts offer thirteen practical strategies. Learn how they’ve managed to make AI work seamlessly, and how you can do the same for your business.
- Design AI with Clear Input and Output
- Enhance Apps with AI APIs and Microservices
- Integrate AI as Helpful Co-Worker
- Upgrade Software with AI and Test Thoroughly
- Conduct an AI Opportunity Audit
- Define Objectives and Select AI Tools
- Leverage LLMs for Sales and Marketing
- Strategically Implement AI in Software
- Connect Apps Using API Calls or Webhooks
- Assess and Integrate AI for Enhanced Efficiency
- Customize AI Features Using OpenAI API
- Prioritize User Needs for AI Integration
- Provide Context for Trusted AI Integration
Design AI with Clear Input and Output
Computers, at their core, are a matter of input and output. Applications, no matter the complexity, take input of some form, process it, and return output. AI functions best when fit into this mold—rather than simply designing “an AI that does everything,” think in terms of inputs and outputs. Designing an AI that “improves sales numbers” is a tall task; an AI that “prequalifies leads,” “helps personalize emails to prospects,” or something similar is a much more reasonable task.
By carefully defining what the input is, and what output is expected, we can determine what AI tools best fit our task at hand; we can directly measure the effectiveness, which lets us make intelligent decisions about which tools to implement. We can also use this approach to test out non-deep learning AI approaches—although LLMs and other generative AI get a lot of press, traditional AI tools like gradient boosting and k-means clustering can more effectively solve some types of problems.
You won’t know, of course, unless you define the problem narrowly and measure carefully.
David Berube, President, Durable Programming
Enhance Apps with AI APIs and Microservices
When integrating AI into existing software applications, one key lesson is the importance of modularity. Rather than reinventing the wheel, we focus on embedding AI through APIs and microservices, which allows us to enhance the app’s capabilities without disrupting its core architecture. This approach ensures that AI components—like machine learning models or NLP engines—can be updated or scaled independently as the app grows.
By treating AI as an integrated but flexible layer, it becomes easier to maintain, iterate, and optimize, all while delivering smarter, real-time functionality to users.
John Xie, Co-Founder and CEO, Taskade
Integrate AI as Helpful Co-Worker
AI is a complex and rapidly changing field with lots of opportunities, but I believe it’s key to understand what AI is really good at right now. In my opinion, the best way to picture AI is like a co-worker that is inhumanly fast at retrieving insane amounts of data for you. While it excels at roughly finding the data you need, at the time of writing, the curation step is best done by a human.
So naturally, any AI integration that offers options for the user to choose from, and possibly in an autonomous way, will be a great success. We’re prototyping a case where AI helps freelancers generate blocks for proposals. At the current state of writing, the AI will never produce the perfect proposal in one click (don’t get me wrong, the results are great, but the desired outcome has so many nuances that it will never be 100% perceived as perfect by the user).
However, if you read the user’s intent and offer options while they are writing the proposal, it enhances the experience and is much more approachable. Instead of typing out this budget item, the AI can finish a sentence for me, given that I have written tons of proposals in the past.
So in short: especially for existing software applications, I would recommend, instead of disrupting processes as a whole, to think about where AI can give meaningful options for the user to accomplish their task in bite-sized pieces.
Thomas Strobl, Founder, Fugoya
Upgrade Software with AI and Test Thoroughly
Integrating AI into existing software is a bit like upgrading from a bicycle to a motorcycle—there’s more power, but you’ve got to handle it right. Let’s say you’ve got Zoho CRM in place. AI can be used to analyze customer data, predict trends, or automate repetitive tasks like follow-up emails.
First, I’d assess whether the CRM and other systems, like Dialpad for communications or Smartsheets for project management, can support the AI tools without things grinding to a halt. From there, I’d look at using something like Zapier to bridge the gap between your existing apps and the AI tools, ensuring they communicate effectively. It’s all about creating a seamless flow, where the AI does the heavy lifting without interrupting your established processes.
Once it’s integrated, testing ensures everything works smoothly, and then it’s a matter of monitoring and fine-tuning the setup as the AI starts learning and adapting. The goal is to make the system smarter, not just more complicated.
Travis Schreiber, Director of Operations, Erase Technologies
Conduct an AI Opportunity Audit
Integrating AI isn’t about forcing new technology onto old systems—it’s about reimagining your entire business ecosystem. In my years of scaling companies, I’ve learned that the key is to view AI as a transformative catalyst, not just a fancy add-on.
Start with an “AI opportunity audit.” This goes beyond identifying pain points—it’s about envisioning new possibilities. We created cross-functional “innovation squads” to brainstorm how AI could revolutionize our processes, not just improve them.
Prioritize integrations based on their “ripple effect.” We developed an “impact matrix” measuring not just immediate ROI, but how each AI implementation could trigger cascading improvements across the organization.
Don’t just train your AI—grow your team alongside it. Our “AI co-pilot” program pairs employees with AI systems, teaching them to leverage AI’s strengths while providing the human touch AI still lacks. Build in ethical safeguards from day one. Our “AI ethics board” vets every integration for potential biases or unintended consequences.
Remember, this isn’t about replacing human smarts—it’s about supercharging them. Create a symbiosis where AI handles the grunt work, freeing your team for creative problem-solving and big-picture thinking.
In today’s landscape, winners aren’t just adopting AI—they’re becoming AI-native organizations. That’s the real power of integration. It’s not about keeping up; it’s about leaping ahead.
Solomon Thimothy, President, OneIMS
Define Objectives and Select AI Tools
Integrating AI into existing software applications involves several key steps. Initially, it’s crucial to define clear objectives for what AI is expected to achieve, whether it’s improving user experience, automating processes, or providing data-driven insights. Choosing the right AI tools or frameworks, like TensorFlow or PyTorch for custom models, or leveraging pre-built services like OpenAI or NLP Cloud for more straightforward integrations, is the next critical decision. This choice depends on the application’s requirements, the complexity of the task, and the available resources.
Then an important question is whether it’s best to use pre-trained models or fine-tune your own model. If opting for custom solutions, developers train models on their preprocessed data, adjusting parameters to optimize performance. For many businesses, however, using pre-trained models and adapting them via APIs can be more cost-effective and quicker, allowing for integration of capabilities like natural language processing or computer vision without starting from scratch.
After model development or selection, the integration phase involves embedding these AI models into the existing software architecture. This step requires careful consideration of how AI functionalities interact with existing features, ensuring that they enhance rather than disrupt the user experience. Rigorous testing is essential here to debug and refine the integration, ensuring reliability and performance meet expectations.
Lastly, post-integration, continuous monitoring and updating of the AI components are very important. AI models might need retraining with new data to maintain accuracy, or updates to comply with new regulations or ethical standards. This phase also involves gathering user feedback to iteratively improve AI functionalities. Moreover, ethical considerations, such as avoiding bias in AI decisions and ensuring privacy, must be woven into the fabric of AI operations.
By following these steps, businesses can not only enhance their existing software with AI but also ensure these integrations are sustainable, scalable, and aligned with both technological advancements and user expectations.
Julien Salinas, Founder and CEO, NLP Cloud
Leverage LLMs for Sales and Marketing
We’ve developed a comprehensive approach to integrating AI into existing software applications, particularly for sales and marketing teams. Our process leverages the power of large language models (LLMs) and custom AI solutions to enhance productivity and streamline workflows.
We start by gathering all existing digital assets from our clients—email templates, sales guides, marketing materials, and any other relevant documents. This data forms the foundation for training our custom AI model. We then use LangChain and the OpenAI Embedding API to analyze these documents and populate a vector database on Pinecone. To expand our dataset, we employ Apify to crawl the client’s website and those of key competitors, adding this information to our Pinecone database.
The next crucial step is fine-tuning the AI model. We provide custom instructions that focus on the client’s brand voice, industry specifics, Ideal Customer Profile (ICP), and target audience. We also incorporate best practices for CRM systems like HubSpot, ensuring the AI can generate relevant email templates, sales playbooks, and marketing content.
Once our dataset is complete, we create a custom AI bot that utilizes advanced LLM models like Anthropic Claude 3.5 Sonnet, OpenAI 4.0, and Mistral Large. We integrate this bot with Slack, allowing team members to easily interact with the AI for various business tasks. The bot can generate recommendations for email templates, sales playbooks, social media posts, and proposal templates based on the specific needs of the team.
To make this integration even more powerful, we create specialized Slack channels for different deliverables, each with a fine-tuned version of the AI bot. This allows for more focused and efficient content generation. We also develop comprehensive documentation and Slack Canvases to guide users on how to effectively use these AI tools.
The result is a self-service system where sales reps can easily create new email templates or access AI-generated content, all while populating the HubSpot CRM with a growing library of high-quality, customized digital assets. This integration enhances productivity and ensures consistency in messaging and strategy across the sales and marketing teams.
Daniel Lynch, Digital Agency Owner, Empathy First Media | Digital Marketing & PR
Strategically Implement AI in Software
As the owner of a web design, marketing, and IT support company that’s increasingly leveraging AI, I’ve found that integrating AI into existing software applications requires a strategic approach. Here’s how we typically do it:
- Identify Opportunities: We start by analyzing our existing applications to find areas where AI can add value. This could be automating repetitive tasks, improving decision-making, or enhancing user experience.
- Choose the Right AI Solution: We evaluate whether to use pre-built AI services (like AWS, Google Cloud AI, or Azure Cognitive Services) or develop custom AI models. For most of our applications, we’ve found that pre-built services offer a good balance of functionality and ease of integration.
- API Integration: We often use APIs to integrate AI capabilities. For instance, we’ve integrated natural language processing APIs into our customer support chatbots.
- Data Preparation: We ensure our existing data is clean, structured, and suitable for AI processing. This often involves data normalization and transformation.
- Gradual Implementation: We typically start with a small pilot project, testing the AI integration in a controlled environment before full-scale deployment.
- User Interface Updates: We modify the UI to accommodate new AI features, ensuring they’re intuitive and user-friendly.
- Performance Monitoring: We set up monitoring systems to track the AI’s performance and impact on the overall application.
- Continuous Learning: We implement feedback loops to continuously improve the AI’s performance over time.
A recent example was integrating AI-powered content recommendations into a client’s e-commerce platform. We used a recommendation API, gradually rolled it out to a subset of users, and saw a 15% increase in average order value. This success led to full implementation across the platform.
Josh Matthews, Director, LogicLeap
Connect Apps Using API Calls or Webhooks
There are multiple ways of integrating AI into existing applications.
The most popular way is using API calls to connect two or more apps; for instance, OpenAI’s GPT-4 and your custom chatbot programmed in JavaScript. Usually, we need to visit the official website of the provider and find the API documentation that describes how to connect to their service or technology. These documents have all the needed information, and in case you need help, you may also contact the support team that will assist you.
Another way of connecting AI with software apps could be using webhooks if we talk about custom integration. Note that not all AI tech providers allow custom integration, so you just might not be able to use their services correctly.
Let’s research the difference between Webhooks and APIs. Webhooks are automated messages sent from one system to another when a specific event occurs, providing real-time data without needing continuous requests. APIs, on the other hand, allow systems to interact by sending requests and receiving responses, giving more control over when and what data is exchanged.
Serhii Uspenskiy, CEO, Springs
Assess and Integrate AI for Enhanced Efficiency
In our organization, we have top AI specialists and software developers who integrate AI into existing software by assessing current systems and identifying key areas for enhancement, such as automation and predictive analytics. Using APIs, machine learning models, and NLP tools, we seamlessly incorporate AI upgrading features without disrupting major workflows. Our focus on compatibility and scalability ensures smooth integration, empowering businesses to leverage AI’s full potential efficiently.
Our software developers are highly experienced with the software they work on, implementing the impacts of AI in their projects and technology. This process is done through APIs and custom AI models, ensuring smooth integration without disrupting existing workflows, while improving overall functionality and efficiency.
Gautam Rai, SEO Expert, Bigohtech
Customize AI Features Using OpenAI API
We’ve integrated an AI assistant to provide users with personalized weather insights. By leveraging the OpenAI API, we process complex meteorological data to generate user-friendly recommendations, such as suggesting the best times for outdoor activities or advising on weather-related precautions.
We customize the API’s responses to ensure they align with the application’s specific use cases, utilizing different prompt engineering techniques. Utilizing the OpenAI API allows us to add these advanced features efficiently without developing and training our own AI models.
Oleksii Schastlyvyi, CEO, Rain Viewer
Prioritize User Needs for AI Integration
Integrating AI into existing software applications is more than just adding a flashy new feature; it requires a strategic approach that prioritizes user needs and enhances the overall experience. AI specialists must begin by thoroughly understanding their users’ needs. Conducting user research and performing a comprehensive user experience (UX) analysis are essential steps, as clearly outlining what users are trying to achieve helps pinpoint where AI can make a meaningful impact.
Once the user needs are understood, the next step is to identify relevant AI capabilities. This involves exploring AI technologies that align with those needs, such as natural language processing, machine learning algorithms, or predictive analytics. Designing for explainability is crucial in AI integration. Users should understand how the AI features work and how they can benefit from them. Providing clear communication through tooltips, tutorials, or onboarding sequences can help explain AI functionalities, especially if they involve complex processes or come at an additional cost. Transparency builds trust and encourages users to engage with the new features.
Seamless integration into user workflows is another important aspect. AI features should be integrated where they naturally fit within the application’s workflow, avoiding generic “AI buttons” that feel out of place. The AI should augment the user experience without complicating it, ensuring that new features are intuitive and require minimal learning curve. Ensuring discoverability without intrusion is key. Introducing AI features through gentle prompts or highlights that don’t interrupt the user’s task helps make users aware of the new functionalities. If the AI feature is a paid addition, it should be presented in a way that informs without pressuring the user, avoiding aggressive upselling that could hinder the user’s ability to accomplish their goals.
In conclusion, successful AI integration enhances the user experience by thoughtfully addressing user needs without disrupting existing workflows. By focusing on explainability and discoverability, and by respecting the user’s goals, AI specialists can add real value to software applications. The objective is not just to incorporate AI for its own sake, but to solve real problems and improve user satisfaction. This strategic and user-centered approach ensures that AI becomes a meaningful addition to the application, rather than a superficial enhancement.
Magnus Høholt Kaspersen, PhD, Partner and AI Expert, Creative Oak
Provide Context for Trusted AI Integration
The key item to think about when integrating AI into your product is context. Providing a solid contextual foundation for customers using their business data, while modeling and understanding the fundamentals of any organization, is critical input to an AI capability. Establishing the fundamental business relationships within the organization and providing the AI with a comprehensive view of the entire business is the basis for trusted AI.
You should also look for ways to enhance user productivity and insight within your application, not replace it. Natural language interaction with your application, for example, still requires human input and decision-making. However, having a conversation with the data within your software application, using an AI chatbot for example, makes it much more accessible and adopted than other forms of interaction. It also dramatically reduces the learning curve and time to value for users.
Beyond providing a very rich context, designing an AI product that will dramatically reduce or remove hallucination is also fundamental to user adoption and trust. There have been numerous discussions on the Internet about how often chatbots come up with a completely fabricated, nonsensical, or inaccurate answer that is not based on any fact—a.k.a. an AI hallucination. By leveraging a customer’s own data purely within their own context (which is not shared in any way with other customers), allows the underlying models to guarantee accuracy.
AI should only be used to extract appropriate context from the question being asked and any other information the AI application identifies as relevant. As a result, the only thing that might occur is a slight misinterpretation of the question being asked; however, the data used to answer the questions will always be 100% accurate.
It’s also key to reassure your users that the AI model will not be trained with their data. It’s essential that no customer data is sent to an LLM or made public, and only non-confidential information is used when interacting with AI applications. Their data needs to be safe and secure at all times.
David Mennie, Chief Product Officer, Klipfolio
