How to Implement Artificial Intelligence in Your Business and Measure Its Impact

Artificial intelligence has been considered a godsend to several industries today. Not only does it improve business productivity, but it also allows human resources to focus on creative endeavors instead of being stuck with monotonous work. If you have been looking to improve the operational efficiency of your business, chances are that you would have stumbled across an AI solution that could potentially revolutionize your business operations. However, since AI is still relatively new, implementing AI solutions can be extremely intimidating and challenging.

Businesses are leveraging artificial intelligence through various approaches. In fact, global spending on cognitive and AI systems is expected to reach $57.6 billion this year. One of the most common business applications of AI is business intelligence, which involves the use of algorithms to identify trends and create insights from a company’s database. Using AI for business intelligence is expected to be one of the fastest-growing areas in the field over the next ten years. The benefits associated with the use of business intelligence include infrastructure cost reductions and enhancing operational efficiency.

Another popular method through which organizations are leveraging business intelligence is through the implementation of business dashboards. Many software companies are developing analytical dashboards that are capable of collecting data from other sources to help managers to make informed decisions. AI can also help optimize business management and enhance business security.

The applications of AI don’t stop here. Businesses have started using this emerging technology in several other areas as well. For example, AI solutions can also be used in business for-

•Voice and face recognition

•Targeted advertising and re-marketing

•Chatbots, online support, and virtual assistance agents

•Predictive analytics and customer service

•Web search suggestions

•Email spam filters and categorization

•Automatic job scheduling

•Service personalization

•Product recommendations

•Fraud detection

•Content personalization

•Language recognition

One of the primary incentives for AI adoption by businesses is the advantage that they stand to gain over their rivals. However, there are many other drivers that come into play for AI adoption. Some of the main driving forces include:

•Consumer demand

•Specific business requirements

•Internal experimentation

•Executive decision making

In this article, we will take a look at how to implement Artificial Intelligence in your business. We will also discuss the various methods through which you can measure the impact of AI solutions.

Here, we’ll help you gain better insights into the below topics:

•Benefits of implementing AI in business operations

•Can AI adoption be risky for your business?

•Real-life use cases of AI

•How to implement an AI solution?

•How to measure the impact of your AI solution?

Benefits of implementing AI in business operations

Artificial intelligence solutions can go a long way in boosting customer engagement and retention, developing trust, and elevating levels of confidence exhibited by consumers towards a brand. Some of the major benefits that businesses observe after the integration of AI into their business operations include:

•Accelerated and improved business decision-making:  Artificial intelligence has the capability to use predictive analytics algorithms to anticipate future trends and events, which subsequently allows businesses to act proactively. This helps managers improve the speed and accuracy of their decision-making.

•Better productivity and increased operational efficiency: Solutions that incorporate AI can offer key insights on various aspects of business operations including decision-making, customer service, product design, etc. In fact, Gartner estimates that AI will recover 6.2 billion hours of employee productivity in 2021.

•Cost-efficient: AI integration can significantly lower business costs. Companies that invest in AI can often witness savings from the automation of routine tasks, process enhancement, and improved accuracy. Analysts estimate that the use of AI solutions in the retail sector presents a $300 billion cost-saving opportunity.

•Eliminating human error: Humans are inevitably prone to making mistakes. On the other hand, AI solutions are known for processing inputs in a rule-based manner with unrivaled speed and accuracy. They make decisions based on previously accumulated data, which makes their outputs and decisions much more accurate, thereby reducing the chances of expensive mistakes.

•Can work around the clock: AI solutions can continue to work without breaks, unlike their human counterparts. They don’t fall sick, take holidays, or experience any lag in productivity. One of the main advantages of using AI solutions is that they consistently deliver the same quality of service.

•Enhanced customer experience: Solutions that incorporate AI can make it easy for brands to offer an omni-channel, convenient, and consistent experience at every single consumer touch-points.

•Promotes human creativity: Although many people are afraid that AI will soon take over their jobs, this scenario is far from the truth. AI solutions are developed to help humans, not replace them. Businesses who automate routine and repetitive tasks will experience much higher levels of employee productivity as their human resources will now be able to focus on high-value creative tasks.

Can AI adoption be risky for your business?

As a business owner, your primary concern during the adoption of new technology will be the risk that it poses for your business. AI adoption offers several benefits, but like most emerging technologies, there are some risks associated with it as well. Some of the limits and risks of AI use in business can prove to be:

•The complexity of technology

•Employee’s skill shortage

•Inconsistency or unavailability of data

•The cost of technology and outsourcing development skills

•The potential loss of data or codes when AI programs are updated and upgraded, for which restoration is costly and time-consuming

Fortunately, there are a few practices that you can follow to mitigate these risks during AI transformation. While adopting an AI solution, makes sure to keep these points in mind:

•Pace your transformation process: During AI adoption, remember to take things one step at a time. Start out with small initiatives and see how they perform. Evaluate the results and weigh the pros and cons before you decide to go further with AI integration. By properly understanding the risks involved, you will be able to make an informed decision, which will increase the likelihood of successful implementation.

•Map out a concise project plan and outline the risks involved: During such a process, it is important to have a clear plan in mind. Write down your project requirements, goals, and risks. This can help you get an idea of the task that you intend to undertake. Prepare yourself for worst-case scenarios and take every possible outcome into consideration.

•Establish a POA: A plan of action can do wonders during AI integration. Contingency plans, employee training, and by-the-book procedures must be established to offset any malpractices and risk-factors of AI.

Real-life use cases of AI

•Improve customer services: In-store AI conversational tools that can answer customer queries at the time of occurrence would significantly improve the user experience of the customers.

•Automate workloads: Computer vision-based AI-driven attendance management system helps us to automate attendance tracking and thereby reduce the workload of teachers.

•Optimize logistics: AI-based generic algorithm that can map, plan, route, and schedule the resources which can help us to improve the efficiency of resource allocation and job scheduling processes.  

•Increase work efficiency: Computer vision-based signature forgery detection system using deep learning algorithms can help us to automate the cheque clearance process.

•Vehicle tracking: Machine learning-based computer vision technology can help us to identify and monitor all of the vehicles that enter and leave a business facility.

•Predict behavior: Computer vision-based in-store heatmap analytics solution helps us to gather in-store customers’ traffic data from the nearby CCTV cameras.

•Manage and analyze the data: Deep learning computer vision algorithms that have been educated on traffic camera footage can help us distinguish between people who wear helmets and those who don’t.

How to implement an AI solution?

•Step 1: Identify your business problem

Is there a particular process that you find inefficient or lacking? Are you facing a problem with customer service or employee productivity? It is important to clearly define the business problem that you are trying to solve by adopting an AI solution. This will help when you need to pitch the idea to the company’s stakeholders. Evaluating your business operations and understanding your needs and requirements is the most fundamental step in AI transformation.

During this step, it is also important to have an idea about the type of data you will be dealing with and whether this data can be used or not. For instance, consider a company that wants to improve its customer service with an AI solution. The first thing to consider is, will such a task be possible or not, based on the available data. This is quite different from traditional software implementations, where the main question is how the implementation will be carried out, not whether it is possible. This is because each business has its own unique set of data. The success or failure of AI implementation depends completely on this data.

•Step 2: A feasibility study

After you have defined the project requirements, you will have to look into whether the project is feasible or not. This step will require a certain amount of AI knowledge and experience. This is why getting the help of an experienced AI development company will be a good idea. In this stage, research must be conducted using the data sets provided by the organization to develop several models that are able to verify the information that can be extracted from the supplied data. The solution that is developed in this step is not the final solution. It is merely a report that outlines the results of the study and the possible next steps for the project. It also offers a structure to the analyzed data.

•Step 3: Ideation and data discovery

After the feasibility study, you will be able to hone in on the aim behind your AI project. This is where you will look at what kind of data sets will be needed by the AI solution. Most companies don‘t have much clarity regarding their data, which may result in the poor performance of the AI algorithms. Gather your data from relevant sources and store it in a secure manner. Eventually, this data will need to be explored and transformed.

•Step 4: Data audit

After the secure storage of the data, it is important to verify its coherence and validity. In some cases, there may be a few key characteristics that can be missing from the required data. Any issues or errors with the data must be eradicated in this step so that the accuracy of the final AI computations isn’t compromised. Also, data stratification should be verified against the data from the feasibility study.

•Step 5: Data preparation

After the audit results have been received, you will need to prepare the raw data for processing. This stage involves data categorization, selection of important features, scaling, dealing with missing information, and other operations that aim at the creation of complete, flawless data sets for the analysis. This is crucial to building accurate predictive models and ensuring high-quality output information.

•Step 6: Proof of concept

The goal of the PoC is to describe the AI model, including its architecture and tuning, and prove the efficiency of the selected method. As an outcome, the PoC produces a report that provides guidelines for model implementation.

How to measure the impact of your AI solution?

There is a major discrepancy between research into AI and the tangible results that businesses experience in the real world. This is why measuring the impact of an AI solution and defining the potential return on investment of the AI project is important. Generally, businesses measure this impact by developing a pro forma. Most service providers will offer pro forma development as a service included with ML and AI development. Essentially, a pro forma refers to a method of calculating financial results using certain projections or presumptions. Some of the primary practices that businesses use to measure ROI include:

•Defining key performance indicators (KPIs): Generally, this is an essential step in ML training as there is usually a metric that you’re trying to optimize. Organizations use KPIs at various levels to assess their success at reaching targets. High-level KPIs tend to focus on the overall performance of the business, while low-level KPIs may focus on processes in particular departments in the company.

•Having a benchmark of comparison: After you have defined the performance metrics, you can maintain a holdout group to get a better understanding of how the AI solution is performing. This refers to a set of customers who will experience the traditional customer service, as opposed to the AI-enabled scenario. By comparing the satisfaction of the holdout group with regular customers, you will be able to gauge the level of impact that the AI solution has.

•Monitoring overtime: Most AI algorithms are constantly updated as more training data is made available. This means that the models can change greatly over time and need to be monitored to ensure the targets are being met and the metrics for success are sustaining data changes. Establish a process to monitor models, and re-train and measure impact frequently.

Implementing artificial intelligence into business operations can be a very complex challenge. More often than not, you will need a reliable development partner to walk you through the process.

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