Month: September 2019

Blog Informative

Preparing for Your Machine Learning and Artificial Intelligence Transformation

Machine Learning (ML) and Artificial Intelligence (AI) has use cases across multiple industries and business functions, but how do organisations ensure that they get the most from the implementation of ML and AI? Companies who reap the rewards of these tools usually undergo a common process to prepare for the changes that come with the application of new technologies and practices.  


This post aims to briefly cover the important steps that organisations undergo before realising the many benefits of Machine Learning, in other words, these are the critical aspects in a strategy for organisations starting their AI transformation journey.  


First: Where Are You? 

How will you know where to go if you don’t even know from where you are starting? 



Organisations wanting to implement Machine Learning and AI solutions need to clarify what their AI goals are – realistically. By assessing current skills of employees (both in IT and other departments), facilities or infrastructure, available budget, and free time to complete end-to-end ML solution delivery, organisations can develop a strategy. This strategy will include aspects such as: 


State of Data 

Organisations will need to assess current processes related to data – how is it being collected, cleaned, organised, accessed, and processed? In order to for ML models to produce accurate and useful outcomes, data needs to be in good shape and properly supported. 


State of Infrastructure 

85% of organisations are ill-equipped to begin their AI transformation. Artificial Intelligence and Machine Learning models process large amounts of data in real-time, therefore the infrastructure supporting it needs to have fast and modern processing abilities. 


Company Culture 

With the implementation of a revolutionary technology, employee mindsets need to be on-par. Leaders within the organisation need to be able to shift their decision-making to embrace data-driven thinking. Teams need to be open to adopting this new technology as well as educated about its usefulness to them (not just to the company at large). 


Internal Expertise 

Once experts have delivered the sought Machine Learning capabilities, there may need to be internal team members to continue maintaining the quality of data and observing data patterns for ongoing success. Most organisations do not have the internal capabilities to ensure ongoing success of the technology. This means that organisations need to consider support from external experts (or Managed Services). 


Second: Goals 

Now you know where you are, where to next?  

In order to determine what needs to be implemented, organisations need to decide what they want to achieve from the technology. A successful AI and ML implementation journey includes setting goals – what needs to be optimised, automated, or improved? Once these goals have been determined, the most important aspect about bringing them alive is: ensuring the process behind achieving these goals is optimised, rather than the goal (or job) itself. This may sound confusing at first but ultimately it means automating the process (for example, segmenting customers into target groups for marketing) rather than automating the job (the marketing co-ordinator’s job of deciding on targeted campaigns).  


Third: Enterprise Support 


Just as implementing AI and ML in an organisation cannot be done by one person, the AI strategy mapping cannot either. Keeping the core goals of the company in focus whilst determining AI and ML goals requires interdepartmental planning and collaboration. This usually means leaders from different teams and departments get together as do data engineers and data scientists (internal and external) in order to establish the state of all processes across the company for better goal setting.  


Final Notes 

An AI and ML transformation journey can only begin with a deep and guided company strategy – including the current state of things and the goal state of things – and this needs to be all-encompassing and all-inclusive in order to prepare an organisation properly for the start a new technological achievement. Skipping this step will likely result in time, resources, and money wasted – leaving all those involved feeling confused and frustrated.  


Being such a vital step of reaching AI and ML goals, the company’s AI and ML strategy needs to be done well. This is best achieved by ensuring those in charge of the strategy are experienced and experts. Intellify is Australia’s leading AI and ML consultancy – delivering end-to-end machine learning solutions from strategy, to enablement, to deployment, and ongoing support. Contact us to begin your AI transformation and stay ahead of the curve.   

AWS In the Media News & Articles Why Choose Us

Intellify – now an AWS Advanced Tier Partner

We are proud to announce that we are officially APN Advanced Tier Partners
Intellify is dedicated to constantly improving and upgrading our skills to ensure our consultants are always up to date with the latest that AWS has to offer. Watch the video below to find out more about what this means for your organisations and for our customers.

Contact us here to learn how our award-winning Machine Learning solutions and support can optimise your business functions.

Blog Use Cases

Use Cases of Machine Learning in the Telecommunications Sector 


Machine Learning (ML) and Artificial Intelligence (AI) are becoming a key implementation in every industry with high value and efficiency being provided across the board. The telecommunications sector is no exception and is at the forefront of adopting Machine Learning to optimise operations, increase revenue, transfer and visualise data, enhance customer relations, and improve marketing and sales strategies.  


Data transfer, exchange, and analysis is key in the telecommunications sector with the amount of data increasing every day. This is why all systems and methods relating to data need to be relevant and accurate.  

Below are a few of the use cases where Machine Learning and Artificial Intelligence have provided benefit in the telecoms industry: 


Product & Service Recommendation Engines 

 Recommendation engines in many industries come up in our digital life. It would not be effective to market product recommendations without taking customer preferences into consideration. By analysing historical customer data (their behaviour and preferences), Machine Learning algorithms are able to predict which products a particular customer will most likely purchase.  

This is done through filtering. Collaborative filtering makes assumptions about a customer’s preferences based on behavioural similarities to other customers (assuming that people with similar profiles have similar interests). Content-based filtering makes assumptions based on previous customer product history – recommending items similar to those they have already purchased. Customers are more likely to upgrade or repurchase products that align with their needs or preferences. This is why telecom companies have implemented this ML method to increase revenue.  


Anomaly Detection & Fraud 

 Due to the fact that the industry attracts a significant number of users every day, this brings high chances of fraudulent opportunities. Fraud, such as fake profiles, theft, illegal authorisation, and account cloning, have a negative impact on the customer-company relationship.  

This is why telecommunications companies are implementing unsupervised Machine Learning algorithms to spot abnormal characteristics or activity in customer behaviour. 

By analysing past data of customer behaviour, ML algorithms are able to visualise and present these anomalies to analysts in real time. This is especially efficient as it allows telecommunications companies to alert their customer of suspicious activity almost immediately thus protecting the customer-company trust relationship. 


Customer Segmentation for Effective Marketing 

Successful marketing in any industry relies on accurately segmenting and targeting groups according to their preferences and characteristics.  


The key to success for the telecommunication companies is to segment their target markets and target marketing content accordingly to each group. This golden rule is relevant to the various areas of business. Machine Learning algorithms can create highly accurate customer segments based on shared customer characteristics. This allows telecommunications companies to better strategise and reach customers with more relevant marketing material (and increase their chance of a sales conversion)  


(Did you know that ML customer segmentation can be done in real-time. As customers change preferences and patterns, they are re-segmented to different groups based on whichever characteristics are most prominent. This means targeting the customer with irrelevant marketing material is less likely to happen – and saves time for those who once needed to re-segment groups manually).  



Optimised Product Development 

Companies in the telecommunications industry need to provide their customers with products that satisfy their needs and wants in order to continue to keep customers loyal and to generate revenue. Machine Learning algorithms can analyse data across different departments to ensure customer feedback is taken into account to provide the most customer fulfilling products are created. By analysing data such as customer feedback, marketing intelligence, and product stock, ML algorithms can present the most data-driven product suggestions for development. 



Data science and its benefits in the telecommunications industry is not a new occurrence. However, Machine Learning has taken efficiency within the industry to the next level. Those companies that have implemented ML in their processes are experiencing fewer issues to resolve, control or occur from happening at all. 

With the right partner and expertise, telecommunications companies are experiencing these benefits for themselves. Intellify is AWS’ Partner of the Year in Data, Analytics, & Machine Learning and has worked with telecommunications companies in the past on bringing Machine Learning capabilities and culture to their organisation. Contact us to learn more about how we could help you do the same based on your own requirements.