Why do you need AI services for your business?
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.
Today, most customer interactions require human interaction, including email, social media conversations, telephone calls and online chat. However with AI, your company can automate many of these communications. By analysing data from previous communications, computers can be programmed to accurately respond to customers and deal with their enquiries. Further, when AI is combined with machine learning, the platforms interact even more, becoming even better at communicating with the customer.
AI will enable your company to provide your customers with personalised marketing, which in turn increases engagement, help to enhance customer loyalty and improve sales. Another advantage of AI is that it is able to identify patterns in customers’ browsing habits and buying behaviour, thus enabling your company to craft highly accurate offers to individuals customers.
Cloud-based AI apps are so advanced that they can quickly discover important information and relevant findings while processing big data. This will give your business insights into previously undiscovered information, which will give you a major advantage in the marketplace.
AI is great in the sense that it can predict outcomes based on data analysis. For instance, it detects patterns in customer data that show whether the products currently on sale are likely to sell, and the volume in which they will do so. It can also predict when demand for such products will decrease. This is fundamental information in helping your company purchase the right stock – and in the right amounts.
It’s important for your business leaders and your project managers to start by spending time on clearly defining and articulating the particular problems or challenges you would like AI to solve; the more specific the goal is, the better chance of success for implementation of AI.
The next step, once the use case has been clearly defined, is to ensure the processes and systems already in place are capable of capturing and tracking the data needed to perform the required analysis. A considerable amount of time and effort is spent on data ingestion and wrangling, so your company must ensure the right data is being captured in sufficient volumes and with the right variables or features such as age, gender, or ethnicity.
It is crucial firstly to carry out a quick data exploration exercise in which you can validate your data assumptions and understanding. Doing so will help to establish whether the data is telling the right story based on your organisation’s subject matter expertise and business acumen.Such an exercise will also help you to understand what the significant variables or features should (or could) be, and the kind of data categorisations that should be created for use as input for any potential models.
Rather than concentrating on the end goal the hypothesis should achieve, it’s important to focus on the hypothesis itself. Running tests to determine which variables or features are most significant will validate the hypothesis and improve its execution.Business and domain experts will be involved, as their continuous feedback is critical for validation and for ensuring all stakeholders are on the same page. Indeed, as the success of any AI model is dependent on successful feature engineering, a subject matter expert will always be more valuable than an algorithm when it comes to deriving better features.
The definition of performance measures will assist in the evaluation, comparison and analysis of results from multiple algorithms which will, in turn, help to further refine specific models. Classification accuracy, for example, i.e. the number of correct predictions made divided by the total number of predictions made, and multiplied by 100, would be a good performance measure when working with a classification use case.As with testing the hypothesis, business and domain experts will be involved to validate the findings and ensure that everything is moving in the right direction.
Once the model has been built and validated, it must then be rolled out into production. Beginning with a limited rollout of a few weeks or months, upon which your business users can provide continuous feedback on the model behaviour and outcome, it can then be rolled out to the wider audience.The right tools and platforms should be selected to automate the data ingestion, with systems put in place to disseminate results to the appropriate audiences.
Once a model has been published and deployed for use, it will be continuously monitored as, by understanding its validity, our SoftElegance team will be able to update the model as required by your business.The market dynamics may change, for example, or your enterprise itself and your business model. Models are built on historical data in order to predict future outcomes, but as market dynamics move away from the way your organisation has always done its business, so the model’s performance can change.