Six tips for integrating gen AI into business processes

Every business is looking for new ways to gain a competitive edge. Introducing the latest tech into business processes is a step in the right direction, and this is where generative artificial intelligence comes in.

Generative AI (gen AI) has the potential to redesign business processes, improve customer satisfaction, and increase productivity – if you know how to use it.

In this blog, we explore six common challenges of integrating gen AI into business processes – and how to overcome them.

1. Entering the unknown: Gen AI is in its infancy. This means many people don't understand gen AI's potential – or its limitations. While many business leaders are keen to use it, they often have vague objectives or unrealistic expectations. This means many gen AI projects are likely to fail before they even start.
2. Controlling costs: In a rush to explore gen AI, many businesses overlook the need to invest in infrastructure, software, and ongoing maintenance to effectively support it. This means costs quickly rise as the return on investment drops.
3. Poor input, poor output: When training gen AI models, you must consider data quality, quantity, and diversity. Some businesses don't spend enough time on the data step, meaning results can be unreliable, unrealistic, and unpredictable. Or worse, biased.
4. More models, more problems: With so many gen AI models to choose from, this can lead to confusion. Unfortunately, an incorrect model may have a negative impact on the outcome, have a higher cost than expected, and not meet your business requirements.
5. Defining success: Unlike testing traditional software, where the expected result is clearly defined, the output of gen AI is variable – there's no clear definition of what is "right." Success is also somewhat subjective, so agreeing on key performance indicators can be tricky.
6. A short-term mindset: If you've managed to integrate gen AI into a business process, it can feel like a triumph. But the work doesn't stop there. Models must be monitored and maintained for long-term quality, performance, and alignment with organizational goals. This is also essential to keep bias out of gen AI models.

From our work with clients across industries, we've identified some ways to overcome these challenges.

1. Align gen AI goals to business objectives: You must create clear and realistic goals for your gen AI project. But more importantly, these goals must support overarching business objectives. Technology and business strategy must be aligned for true transformation, so start by getting your business and technology leaders together to develop a united front.
2. Assess feasibility and return on investment: Once you have an idea of what gen AI can do for your business, a feasibility study will help separate dreams from reality. This will also evaluate your potential return on investment by estimating the productivity gains, cost reduction, revenue boost, and other benefits that gen AI can bring.
3. Understand data requirements: If you don't have a wide variety of data and analytics expertise in-house, then you must seek external support. Data is the most crucial ingredient for gen AI – any gen AI model will only be as good as the data that goes into it. Experts will help you understand where your data resides, how to use it, and how to plug any gaps.
4. Identify the right model: Selecting the most appropriate gen AI model upfront will prevent bigger problems down the line. This should be done by a specialist, someone who understands not only the technology but also the nuances of your processes, industry, and overarching business objectives.
5. Test and validate: With gen AI, there must be careful and ongoing governance to determine what good looks like. The output of gen AI models must be thoroughly tested and properly validated against predetermined outputs. Only through this test and validate approach can you be confident that your gen AI model is meeting its goals.
6. Continuously monitor: To futureproof your investment, continually monitor gen AI models for quality and performance. Feedback should be collected from an array of users to determine if the model is working. Based on this feedback, you'll likely discover that your gen AI models need occasional fine-tuning.

Much of the success of gen AI integration into business processes depends on having the right blend of business and technology expertise. If this expertise doesn't fully exist within your organization, now is the time to seek support. As we're already on the gen AI journey with many of our clients, we would be happy to help you get started.

For help introducing AI into your business processes, reach out to Nuno [email protected] and Igor [email protected]