When you look around the business world, it feels like practically everyone is adopting artificial intelligence (AI). It turns out that feeling is correct. In 2017, just 6% of companies reported using AI. By 2024, that number had risen to 72%.
In the midst of this revolution, organizations might be tempted to rush into AI implementation. But in reality, how well a company implements AI is more important than how quickly they can pull it off. Organizations don’t need AI for the sake of it – but in order to solve existing problems and improve performance or efficiency.
For an AI implementation to be successful, companies need:
- A clear “business case”
- A practical framework for implementation
- Metrics that will define success
In this blog post, we’ll explain how to build these three components — so you can help clients implement AI in a way that will be truly transformational.
Making the “Business Case” for Implementing AI
Implementing AI requires an upfront investment. In order to justify the outlay to decision-makers within the organization, it’s important to understand from the start how AI will ultimately pay for itself. Your clients can do this by determining which AI use cases they can leverage and conducting a detailed ROI analysis before deployment.
Outline AI Use Cases
As AI improves, the number of potential use cases increases – and the potential value of the technology explodes.
Here are some ways that AI could provide value to an organization:
- Increase efficiency. When employees can use AI to conduct research and extract information from internal resources (like employee handbooks), they save time and increase productivity.
- Reduce costs. Solving problems with AI is often cheaper than alternate solutions like hiring an additional employee or working with an outside firm.
- Improve customer experience. AI is already being widely implemented in contact centers – largely because AI solutions standardize key elements of the customer experience.
- Enhance revenue. By optimizing sales and marketing strategies, AI can lead directly to a substantial revenue increase for businesses.
Whenever a client is interested in implementing an AI solution, advise them to pinpoint exactly where they can expect to derive value from the technology.
Conduct an ROI Analysis
Once a business has settled on AI use cases they plan to leverage, it’s time to narrow the focus and conduct a proper ROI analysis.
This analysis should include:
- Investment requirements. For a successful implementation, the company will likely need to invest in an outside AI solution – as well as the talent to integrate it effectively.
- Expected returns. This can be tough to quantify, especially when an organization hopes an AI solution will save money by eliminating “soft costs” in other areas – but it’s still worth conceptualizing and estimating the savings and revenues that AI could bring. .
- Timeline to value. Most returns on an AI investment won’t be immediate, and it’s important to understand ahead of time how long it should take to reach peak value.
Completing this ROI analysis allows an organization to implement AI with a set of clear expectations.
Preparing a Framework for Implementation
AI solutions are not “plug and play.” For an implementation to be successful, an organization needs to be properly prepared. To help your clients get ready for a new AI solution, guide them through the process of laying the IT foundation and properly allocating resources.
Secure the IT Foundation
There’s a lot you can do in advance to prepare a company for an AI implementation – and much of it has to do with data. For generative AI, data is the major input that defines the capabilities of the entire system. So for the AI solution to be successful, an organization’s data needs to be in order.
When preparing data for an AI system, focus on:
- Data architecture (so the data is stored somewhere it will be accessible to the AI system)
- Data quality (so the data is relevant and up to date)
- Data security (so sensitive information is out of the reach of the AI system)
You should also ask if the organization has the IT infrastructure to support the AI solution they’re planning to implement. In particular, they’ll likely need to evaluate how they’ll get the necessary compute power. This could come from private cloud, public cloud, specialized low code/no code platforms, or even an on-premise system – as long as the system has the GPUs (graphics processing units) infrastructure that AI systems usually require.
Allocate Resources
Talent is often the main resource necessary to implement an AI solution. Some organizations might already have experts on hand who can build and integrate an AI system, often using a platform like Azure Machine Learning. But in many cases, it’s best to outsource this work to an AI solution provider. Alternatively, companies can invest in an IT solution that already incorporates AI technology – which will require a monetary investment, but should also involve less in-house organizational realignment.
Whether a business creates its own AI solution or brings one in from outside, additional investments are usually necessary to make sure everyone in the organization is ready to effectively leverage the new technology. Often, this involves AI workshops in which employees learn how AI can help them do their jobs. Without this type of training, AI can become a frustration or a distraction instead of a revolutionary tool.
A Metrics-Based Roadmap for Success
Whenever a business implements a new technology, it’s best to prepare metrics and KPIs so they’ll know how to measure success – and AI is no exception.
The specific metrics will depend on a business’s objectives. That said, you should suggest to your clients that they measure KPIs in these key areas:
- Efficiency metrics (to show how time and resources are being allocated)
- Performance metrics (to test the accuracy and usefulness of the AI system)
- Financial metrics (to determine if the ROI is meeting expectations)
Gathering this data will allow the organization to modify its AI systems – which, in turn, should improve performance and optimize ROI.
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