AI in Digital Product Development: A Step-by-Step Implementation Guide
As AI grows in ubiquity, business leaders and innovators are exploring how to implement AI in a product or service they already offer or realize their vision for a new AI product.
However, AI-powered product development isn’t for the faint of heart. Implementing AI requires meticulous planning and a systematic approach to ensure it performs well and delivers the proper output.
The challenges stem from the multifaceted nature of AI. Artificial intelligence covers a lot of ground, from AI-powered chatbots to generative AI (gen AI) such as ChatGPT to robotics and predictive analytics. Which flavor of AI you’re trying to leverage will impact the technology used, the data needed, and the complexity involved.
One aspect that doesn’t vary much is the process of implementing an AI solution. This article offers a step-by-step guide to AI implementation.
Step 1: Define Goals
As with any project, the successful implementation of AI starts with asking, “What problem are we trying to solve?” You can’t hit a target if you don’t know what it is.
Identifying the problems AI can solve for your business involves carefully assessing business processes and objectives. What inefficiencies need solving? How can gen AI enhance customer experiences? What decision-making processes can automation improve?
Project goals should be precise and measurable to ensure that you can track the impact of the AI solution. Examine AI case studies to see what might be possible for your business.
Identified problems become objectives: improving operational efficiency by a defined percentage, reducing customer service response times, or increasing the accuracy of sales forecasts. Determining these metrics gives you specific targets and helps avoid scope creep.
Defining the project goals ensures that your AI initiative is focused, measurable, and aligned with your business goals.
Step 2: Assess AI Readiness
Many business leaders fail to achieve maximum ROI from their AI implementation due mainly to a lack of readiness in four key areas: strategy, data, technical infrastructure, and corporate culture.
In an Ernst & Young survey, 83% of senior business leaders said their AI-powered product development would have been faster if they’d had a stronger data infrastructure in place. Sixty-seven percent said their lack of infrastructure is actively hindering their AI implementation.
Assessing your AI readiness before launching your project prevents you from wasting time, effort, and budget on an AI product that will fail to meet your goals.
Conducting a thorough AI readiness assessment is its own process. Taazaa built a free AI-powered tool to get you started, which gives you a benchmark readiness score and a few key insights. Best of all, it takes less than five minutes to complete.
Assessing your AI capabilities across the four readiness pillars positions you to address weak areas, improve AI performance, and maximize ROI.
Learn More: Before You Invest in AI, Evaluate These Four Areas
Step 3: Select the AI Technology
The technology you build your AI upon must be able to handle the tasks you want it to do. Predictive modeling, natural language processing (NLP), and computer vision are all “AI,” for example, but have different engines under the hood.
To ensure greater efficiency and performance, choose the type of AI model architecture and methodology best suited to your AI strategy and the intended task.
You also need to evaluate the infrastructure and platforms that support AI. Will you be able to leverage the flexibility and cost effectiveness of a cloud service provider, or do your security and privacy needs mandate that you host the AI on-premise? What about open-source libraries? Their prebuilt algorithms and model architectures can reduce development time and cost.
Step 4: Build an AI Team
When it comes to building a team to develop and implement your AI solution, there are two paths. You can either hire the talent you need or partner with a custom AI development company.
Hiring the talent puts the entire team in-house, giving you greater visibility into project progress and fostering a tight collaboration between leadership and the development team. The downside is twofold: first, AI development talent is in high demand right now, so hiring is challenging; and second, that high demand means the talent you find today might jump ship for a better offer next month.
The alternate path is finding a trusted development partner. While you lose the advantage of having everyone under one roof, the right development partner can provide all the experienced talent you need without the hassle of hiring, onboarding, and equipping anyone. If you find a partner that has built an AI solution like the one you envision, their experience can help accelerate implementation and avoid pitfalls.
Regardless of which path you take, your team should include domain experts with intimate knowledge of the business needs. These experts will make sure all AI outcomes align with your strategic goals.
In addition to the usual software development roles (data scientists, project managers, developers, testers, etc.), AI implementations have unique roles. Ethical AI specialists or compliance experts help ensure that your AI adheres to data privacy laws and ethical guidelines, for example. The specific roles depend on what type of AI you’re implementing; an AI with computer vision will need a computer vision engineer, one with machine learning will need a machine learning engineer, and so on.
Step 5: Create an AI-Positive Culture
When you announce you’re implementing an AI solution, you might find that it makes some employees uncomfortable. They may be uneasy about the new technology, fear losing their jobs to AI, or simply dislike change.
To create an AI-positive culture, start by promoting openness, creativity, and curiosity around AI. Encourage teams to consider how to implement AI in a product that improves their role or their department. Support a pro-innovation mindset by clearly communicating your vision for AI’s role in the business, focusing on its potential benefits and speaking to any voiced (or unvoiced) fears.
Step 6: Manage Risks
Any software that has access to or uses sensitive data poses risks related to data privacy, security vulnerabilities, and unintended consequences. AI has those same risks, and introduces new ones, such as model bias.
Thorough risk assessments during AI-powered product development can mitigate these risks. Your development team will need to identify areas where the model’s predictions might go wrong, introduce unintentional bias, or be vulnerable to data breaches. Robust data protection practices like data anonymization, encryption, and access control help protect sensitive data.
In addition to mitigating AI functional risks, you also need to ensure that the humans who use AI are acting ethically and within regulatory standards. This involves establishing ethical guidelines that cover principles like fairness, accountability, transparency, and respect for user autonomy.
Many regulated businesses create AI ethics committees or review boards to oversee AI projects. These groups evaluate user impact, ethical problems, and compliance with data protection laws to mitigate legal and reputational risks.
Step 7: Test, Test, Test
Before you go live with your new AI tool, rigorous testing using separate validation and test datasets is needed to evaluate its performance and ensure the model can generalize effectively and perform well on new data.
Testing gives you another chance to check for biases or systematic errors that might degrade performance and introduce risk. Through careful testing, you gain confidence that your AI solution is ready for users.
Testing continues after deployment, as well. Data patterns and business needs change in the real world, possibly affecting your AI’s performance. Consistently testing your AI models in real-world situations is vital for identifying erroneous outputs or biases. It allows you to adjust and retrain models to improve accuracy and eliminate bias.
Continuous monitoring, assessment, and feedback loops enable you to track performance over time, detect subtle changes in the accuracy of its data or predictions, and retrain your AI as needed. Regularly scheduled model retraining ensures the AI tool stays up to date and maintains its accuracy as it adjusts to new patterns.
Step 8: Plan for Scalability
The ultimate goal of any AI-powered product development is to grow the business. Therefore, you need to ensure your AI grows along with you, which makes scalability essential.
Scalability is the ability of your AI system to handle increasing amounts of data, users, or processes without degrading performance. When determining how to implement AI in a product, take care to choose an infrastructure and frameworks that can support scalability. It often involves cloud services, but it can also mean leveraging distributed computing or using a modular architecture.
Cloud platforms offer on-demand resources and tools that make managing increased workloads easier. Their flexibility lets you add more data, users, or capabilities over time, as our business needs change. A scalable solution maximizes long-term value and reduces the risk of needing expensive upgrades down the line.
To keep your AI relevant, accurate, and up to date, plan to regularly retrain models with new data to keep their performance from degrading. This maintenance plan should also include monitoring models’ output for biases or errors.
You’ll most likely hear about it if something goes wrong, but make gathering feedback from users and stakeholders part of your maintenance plan. Their input will help you refine and improve the system based on real-world usage.
Build the Right AI Product
Everyone seems to have AI fever, from small businesses to massive corporations. It’s all anyone’s talking about, and most of that talk revolves around how to implement AI in a product that delivers the maximum ROI.
The key is building the right AI product the right way. The right AI product helps you achieve your business goals, whether those involve increasing productivity, reducing costs, or maximizing efficiencies.
And the right way to build it starts with shoring up the four pillars of AI readiness, selecting the right tech stack, and putting together the right team—either by hiring talented AI experts or partnering with an experienced custom AI development company.
If you decide to go the partnership route, consider Taazaa. We’ve got all the talent and experience needed to build secure, scalable AI solutions that help you achieve your business goals. Contact us today!
