6 steps to implementing AI into your business

6 steps to implementing AI into your business

Philip White

26 July 2022 - 11 min read

Machine LearningAI
6 steps to implementing AI into your business

Artificial intelligence technology offers vast possibilities to businesses across the world. Key benefits include the potential to significantly reduce costs, improve efficiencies and allow businesses to be more agile.

Businesses are recognising the potential of this technology and AI adoption is continuing to increase steadily. In last year’s State of AI survey, 56% of respondents reported that they had adopted AI in at least one business function. This statistic has risen from 50% in the previous years’ report, which highlights the growing interest in this technology. 

The UK industry is seeing great growth in particular. with funding for AI projects increasing by 30% QoQ. This statistic follows on from the UK government's announcement of a ten-year National AI Strategy in September last year. 

The aim of this plan is to “boost business use of AI, attract international investment and develop the next generation of tech talent”. With these plans now in motion, support for AI research, development and investment is stronger than ever. 

As businesses consider investing in artificial intelligence technologies, it’s important that they understand how to implement AI into their operations. First understanding exactly whether AI is right for your business and then taking the necessary steps to ensure that this tech is adopted with minimal disruption and optimal long-term success. 

Defining Artificial Intelligence and Machine Learning 

The term “artificial intelligence” refers to any system that is capable of problem solving and learning in the same manner as a human. 

The characteristics of these systems differ from those of rule-based systems. In traditional systems, processes are usually controlled by software that is based on rules and instructions. These systems won't execute their functions if they encounter triggers they don't recognise, which can make them quite rigid in the face of changing customer behaviour. 

Systems that use AI, in contrast, are capable of adapting as new information becomes available. This behaviour is similar to humans, who change their behaviour or develop different skills when they receive new information.  

A subset of AI, machine learning involves applying learning algorithms to behave in particular ways without being explicitly programmed to. Businesses can make better decisions using machine learning models, which provide highly accurate and sophisticated analysis of data sets. 

1. Think whether AI is right for your business 

While your business might wish to implement AI right away, it is important to research what this technology can do first. Specifically, what functions it has that are appropriate for your industry and the needs of your business.

In industries such as retail and food, for example, physical interactions are still highly valued. Fully implementing AI in place of human employees may, therefore, be less appropriate for this customer base. 

However, AI services don’t need to completely replace humans. Businesses will see benefits from working with their machine learning model, that is, using its predictions to inform their own decision making. 

It is likely that businesses will benefit from using AI to improve efficiencies in some business functions, rather than a complete overhaul of all physical operations. This strategy is already evident in customer service call centres. 

Using machine learning algorithms, the customer service team is able to efficiently organise callers according to their needs. Employees have more time to respond to more complex issues, since AI is able to handle simple requests. 

The best way to understand what AI could mean for your businesses is to work with an AI/ machine learning consultancy. AI/ML consulting will give expert advice on what kinds of solutions would work for your business and how to invest in AI.

2. Work out problems that AI will address

Refining your understanding of AI also means working out what specific problems it will address for your business. In the tech industry, artificial intelligence and machine learning are buzzwords, which makes them easy to dismiss sometimes out of concern that they may be introduced unnecessarily. 

However, no technology should be implemented without a thorough analysis of the problems that it will aim to solve. As with any tech investment, work out what business problem are you trying to achieve and what outcome are you hoping to see. Working in partnership with your ML provider, you will be able to find the most suitable algorithm for addressing this business problem.

For example, a manufacturing company may be suffering from extended downtime in their system and, as a result, spend a lot of time and money fixing failing equipment. Working with this pain point, a ML consultant may suggest a regression algorithm which would use existing data around equipment attributes to predict when it will fail. Using these predictions, the manufacturer can then take corrective action early enough to prevent bottlenecks or downtime in their system. 

Whatever solution your business decides, you need to ensure that you have the operations in place to support your ML model. In order to accomplish this, you need to manage your model continuously and anticipate its evolution as your business evolves.

3. Assess your data

With supervised learning, the inferences that an AI system makes are heavily influenced by the labelled data that it is given. The quality of your business' data is, therefore, crucial to the performance of your AI system. Weak points in data collection can be identified in early scoping phases, where businesses will work in collaboration with an ML consultant to see what types of data they want to capture.

In this scoping phase a prototype can be used to identify potential weak areas of data gathering and highlight where businesses could be focussing their efforts. Businesses might want to capture a certain area or receive predictions for a certain scenario, however their current data collections processes may be holding them back. 

For example, a residential construction company might want to make more informed decisions about the property features they should offer to customers. However, they will be unable to get the best results if they are not already collecting data around each property feature type and how customers are engaging with these features. If the algorithm that tells the computer what to do with the data is imperfect, the computer may give a bad output.

A bias is one example of a bad output. Biases in AI are when a model produces results that are prejudiced or unfairly focused on a particular inference. Biases usually occur because the data that is provided to the model is not is not comprehensive enough to render reliable results. 

As one notable example, Amazon had to remove a machine learning model that it was using for recruitment after it was revealed that the algorithm had a bias against female candidates. In order to train the model, CV patterns were observed over an extended period of time in applications submitted to the company. However, a majority of the applications were from male candidates, reflecting trends in the tech industry.

It is important to remove these biases as early as possible because biases can have significant, real-world implications for your business. The data should represent all the characteristics you wish to produce in your inference, but without bias.

Your business should also be aware that data will need to be surrendered - and sometimes made anonymous — before an AI/ML provider can build the model. Taking this action is the first step in ensuring that your machine learning model is developed securely and without bias. 

4. Consider infrastructure 

There are no constraints on a companies’ infrastructure when it comes to ML/AI adoption. However, a company’s system should have the capacity to support the data and files needed to build a sophisticated machine learning model.

On-premises data infrastructures typically lack the performance and scale needed to manage growing data volumes. This infrastructure also requires time-consuming management and capacity planning. 

This type of ageing infrastructure is also ill-suited to machine learning and other analytics technology. Scalable data systems like the cloud are better alternatives that can handle the changing requirements and increases in the volume of data. 

As your project gets underway, businesses should also seek powerful infrastructure that is capable of running ML frameworks. Software like ML.NET, TensorFlow and PyTorch are some examples of machine learning software libraries. 

5. Develop in iterations 

Machine learning development is iterative by nature. It requires evaluating your algorithm and your model’s inferences at each phase of development. This iterative quality is what lends AI so well to an agile development approach. Both require continuous improvement and feedback stages, where a client and software provider work in collaboration to reach the best results. 

A key part of iterative, agile software development is feedback — both from the client and the software provider. Feedback stages are, particularly, important in AI projects as your model needs to learn to get better. 

Under an agile approach, proof-of-concepts and prototypes are highly valuable for gaining initial feedback and reducing the risk of biases before the model is used in operations. Machine learning needs solutions for retraining a single result and should be introspective. 

This means that a model should become increasingly adept in recognising potential inaccuracies or flaws in the data that it is being given. The algorithm should explain flaws with the model itself, like if a specific feature is not being taken into account at all.

It’s important that a business is just as willing to learn from their machine learning model as the model is learning from the business’ data. The algorithm may expose gaps in your business’ data. Examples of a data gap may include a demographic that your business is not fully capturing or a metric that you are not fully reporting.  

The best way to integrate AI effectively into your business strategy and digital transformation is to establish a strong vision of what problems the solution will solve and follow through with an agile approach. An agile approach requires collaborative teamwork across all levels of an organisation which will open your model up to more diverse and thorough assessment. 

6. Measure performance

It is critical to measure the performance of any technology that is implemented. AI is no exception. Measuring the performance or outcome of your machine learning model is an important aspect of understanding how successful your initiatives for your project have been. 

Some businesses may be seeking labour and cost savings, whereas others may look to improve user experience or increase customer satisfaction.

ML models have attributes such as accuracy, execution times, and recalls. Therefore, you may not wish to consider the model with the highest accuracy as necessarily most appropriate for your business. This model could be biased or may work differently when dealing with unseen data. 

Your business may also conclude, for example, that a model with lower accuracy is fine, if there are enough employees who can make intuitive decisions based on the data.  

 If you aren't getting the right kinds of inferences or business results, then you need to retrain your model. In retraining, you optimise your model to provide more useful results. This outcome can only be achieved by the consistent improvement that comes with learning. For this reason, it is a good idea to work with your AI provider to develop a plan for retraining your models. 

As your AI journey progresses, your model may also degrade due to changes in user behaviour or the environment. Maintaining your AI model's optimal performance requires analysis of such changes. 

Permutation Feature Importance is Microsoft's tool to interpret machine learning model predictions in ML.NET. PFI indicates how much each feature contributes to a model’s prediction. Using this, the business can evaluate the methods they use to collect data more thoroughly. In addition, those who are building the model can reduce training time by focusing on a subset of more meaningful features.

The key is to keep looking for opportunities to get more accurate results from your machine learning solution, so that it is agile enough to respond to the latest market and customer data. Once your first intelligent system runs successfully, you'll be ready to develop more AI solutions and continuously improve your business offerings.

Make smarter decisions 

AI is a tool that can help you make smarter decisions in your business. Whether it’s improving operational efficiency or generating new sales leads, AI has transformative potential across many industries. 

To tap into these benefits, this article has shown 6 key steps for ensuring that your AI projects are developed and implemented with maximum success. 

Audacia is a UK based software development company and digital transformation service provider who can offer a range of solutions for your business. We have significant experience working across a number of industries and organisational cultures to help businesses reach their goals. 

Audacia is adept at delivering AI consulting & machine learning services for leading organisations, with particular experience in healthcare and manufacturing. 

To find out about AI opportunities that we offer, speak with us today on 0113 543 1300, or email at info@audacia.co.uk.

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Philip is the Managing Director of Audacia and is responsible for the company's overall strategy and culture.