How to Make an AI Implementation Strategy Work for Your Business

AI implementation can work wonders for your business if you know how to apply it. Throwing money aimlessly at an AI strategy that won’t yield any results is the last thing you want to do. How can you adopt artificial intelligence and maximize value? Serhii Pospielov, AI Tech Lead at Exadel, explains how to build an AI implementation roadmap in a cost-effective way and provides real-life examples of effective AI implementation services.

AI Implementation in Business: Fast Facts

• As noted in a NewVantage 2022 research, 9 out of 10 top businesses invest in AI on a regular basis.

• According to a McKinsey survey, high-performing companies gain profits due to artificial intelligence.
• A Forbes report highlights that 2022 could see the “collective shift away from point solutions toward holistic platforms that offer a suite of business solutions.”

Business Value of AI Implementation

Incorporating an AI strategy can be highly beneficial for your business. According to Harvard Business Review, AI solutions can:

• Automate business processes: administrative and financial tasks that are usually done manually
• Generate insights with data analysis by means of algorithms and detect patterns in vast volumes of data for more accurate predictions
• Engage with customers and staff through chatbots, intelligent agents, and the like.

As of 2022, the McKinsey report highlights the top use cases for AI implementation where business process optimization, AI-based products, and customer analytics top the list.

Following the hype is a slippery slope and can cost you a pretty penny if the implementation is done wrong. As you start out on your AI implementation journey, you should consider some of the challenges you may face.

AI Implementation Challenges

AI development does have a few challenges that come along with it. As per your AI strategy, you could face a few hurdles on your way to successful AI implementation.

Trying To Do Everything at Once

The main stumbling block for businesses is the rush to apply AI implementation in multiple business processes at once, which can be complex and resource-consuming.

A 2021 McKinsey report recommends using  AI implementation to reimagine just one crucial business process or function.

Cybersecurity Concerns

Cybersecurity also remains a challenging part of AI implementation. Organizations lack a unified cyber standard covering their processes. AI systems can become targets for hackers. The more complex an AI system is, the more vulnerable it may be. When broken, hackers can feed the algorithm false data, which will make the algorithm report false data to you in turn. Having clear security guidelines and measures could spare you a good deal of trouble and bottlenecks.

AI Bias

AI can become biased, favoring one data type over the other. The type of bias and its overall possibility depends on the data you feed to the algorithm. If you fail to feed the algorithm overarching data sets, it may distort the output results.

Ethical Concerns

AI implementation can contribute to business process automation, jeopardizing human talent at times. Nonetheless, you can have the best of both worlds without having to resort to large staff layoffs. AI can be a way of resource augmentation, handing over manual work to an algorithm while freeing up your employees’ time for more important tasks and operations. You need to strike a balance between AI and the workforce, merging both organically.

Explainability Issues

The concept of explainability in AI mainly touches upon two kinds of algorithms: white-box and black-box. White-box algorithms have high explainability, which means that AI developers and subject matter experts can clearly understand how the algorithm works and why it has arrived at certain outcomes. On the other hand, there are black-box use cases where it’s practically impossible to explain the rationale behind an algorithm and why it produces results as they are. Both have the right to exist. However, the transparency of algorithms is a crucial tradeoff for increasingly regulated industries.

Failure to Scale AI

Another stumbling block in AI implementation is the limitation of some AI solutions which may not allow you to scale up. Before you commence any AI implementation, you need to analyze your operations with a forward-thinking mindset or entrust this audit to a proficient vendor.

Once you’re aware of the challenges, it’s time to plan your AI strategy, taking it slow and easy.

AI Implementation Steps

A recent survey by Deloitte AI Institute explores the leading AI practices for potentially AI-fueled organizations and states that AI implementation must start with a carefully planned AI strategy. Consider AI implementation and prioritize investments only when you can align your AI strategy with the overall company objectives. Explore how you can build an AI strategy for your business and set your AI implementation for success.

Define a Business Opportunity AI Can Address

Answer the following questions to see where you stand with AI implementation:

• What are and where are the obstacles?
• In which areas of the business do data and technology prevail?
• Which areas are flexible enough for fast, innovative implementation?
• What resources do you have, or need,  to implement AI?

You’ll probably see a few areas where AI implementation is beneficial. Nonetheless, you should prioritize AI endeavors using the following criteria:

• Which is the best way to increase profits without unnecessary investment?
• Where can business processes be optimized in order to set the organization up for success?
• How can more data-driven processes be launched?
• How can AI implementation improve interaction with customers and employees?

Form an Expertise-Driven AI Team

You can go far if you’re backed by experienced and expertise-driven AI specialists. To form an AI implementation team, you can turn to proficient in-house staff who can see the process through while predicting and overcoming any challenges. If you don’t have such a team, you can hire an AI implementation team that has the expertise and a set of necessary capabilities to create an AI-based solution.

Gather Data to Train the Model

Once you have your team up and running, you need to gather and clean the data you’ll be using to train and test your AI model. With the appropriate AI technique or algorithm, you’ll train and test the AI model and then deploy it in a production environment to further learn about, and monitor, its performance. After gathering feedback, you can see how helpful and scalable your AI implementation is. It’s vital to run a data maturity audit to understand whether your business has the capacity to leverage everything AI has to offer. After all, your AI implementation is only as good as the data you use to train your model. After the assessment, you’ll have a good idea of whether you can use AI in the first place.

AI PoC to Prove Your Idea Works

The whole idea of AI implementation is to let you rethink your business through an AI lens. Find a business outcome you strive for and see where AI fits right in. Good planning precedes seamless implementation, so you should have a detailed plan with excessive description. The idea is to start small, though. The discovery stage or proof of concept (PoC) is crucial to help you decide whether you need AI.

With AI proof of concept (PoC), you can build a model in just a few days or a couple of weeks, monitor the results, and gather feedback to see if the prototype really works. Consequently, you aren’t investing heavily right away, but rather a fraction to finalize your decision based on the immediate deliverables you receive.

How to Implement AI: A Case Study from Exadel’s Portfolio

A non-profit healthcare network based in New York turned to Exadel for AI implementation. Being the largest healthcare provider with 23 hospitals and more than 700 outpatient facilities, the organization needed to maintain its medical storehouse well. They asked Exadel to create an AI-based module for asset management. Eventually, the module would read labels and recognize serial numbers and expiration dates, as well as other critical information.

The Exadel team developed an AI-based asset management module that can process serial numbers, brands, models, humidity, temperature, and other relevant data for the stored medical goods. The module also detects symbols and makes connections between the symbols and the surrounding text in order to make an appropriate decision. In a nutshell, the solution uses AI to work with any image type.

This optical recognition module uses three different methods. The barcode scanner involves a convolutional deep learning neural network that finds standardized barcodes and retrieves the required information. Then the program finds the text on the page that corresponds with predefined patterns. All these methods are used in parallel, and then the module combines results for better accuracy.

The embedded AI-assisted tool has automated manual work, reduced human errors, and streamlined business processes within the organization. Thanks to the solution’s compatibility, it’s easily integrated with existing systems and software, which ultimately contributes to cost reduction.

Final Thoughts

A successful AI implementation business strategy relies on solid business goals and is tied to actual KPIs. Did you know that in 2010 Jeff Bezos asked every Amazon manager to research how to use AI in their particular field? This initiative made Amazon a worldwide leader in AI implementation later on. Your AI implementation may be different. Assess your business needs, plan your way to AI transformation, and learn from the results you get. Your business’s success depends on what you do today.