Make your business more productive with Google Workspace’s built-in AI features

15 February 2021

Save time with AI features

Last year, Google announced a better home for work. G Suite was given a revamp and is now Google Workspace. This reflects the new collaborative interface that has been adapted to a new, remote way of working. 

We have discussed the collaboration and productivity benefits of Google Workspace many times, but the built-in AI features, that we take for granted on a daily basis, are what set it apart from its competitors.

2020 was about enabling remote work. Google’s latest productivity tools with built-in AI allow this remote work to be our best work.

Many of us may have heard about AI, but have not realised we are using it every day when working with Google Workspace.

These built-in AI features are what makes Google Workspace ahead of the competition, and save time when writing an email, as one example. Google has integrated Artificial Intelligence (AI) in their collaboration and productivity tools. Their goal is not only to increase individual productivity but also efficiency when getting your work done. 

Below are some examples of the built-in AI features that you may not have considered:

Smart Reply on Mobile

Ever typing out an email on your mobile device and suggested words and phrases appear before your eyes? This is the built-in AI in action.

Smart Suggestions on Google Docs, Sheets, Slides, & Forms

Similar to the suggestions in email, this feature is also present in Google Docs, Sheets and Slides. As you are typing, Google will suggest words, saving you time.

Cloud Search

Cloud search makes searching through your organisation’s data/documents easier. Cloud Search utilises Machine Learning to surface the most relevant suggestions or responses. What Google search does for the web, Cloud Search does for enterprise search and for your business.

Learn more about cloud search here. 

If you want to understand these features, and how they can save your business hours of time, sign up for our upcoming webinar.

18 January 2021

Alastair Lumley – Account Director, Netpremacy

A new year brings new trends and motives. Be that your own personal ambition to cut down on alcohol for January or not eating meat. Whether it’s to get out and exercise more or whether it’s career progression, we all have aspirations at the start of the year…..and so does the tech industry.

One of the big topics this year I can see having sat on a number of webinars and read numerous blogs is Ai but more importantly the ethical use of Ai.

Ai is becoming such a big part of what we do, whether it’s generating a personal suggestion stream on Netflix….or deciding if your credit rating is good enough to provide a loan. This is why it’s as important as ever to ensure that it’s used in an ethical and correct way. There are boundaries we need to set when using Ai, and these need to be understood correctly and adhered to. So much work has been done globally to improve equality, we need to ensure that everything we do meets this standard.

What is Ai?

Ai can be defined as:

  • A computer or a robot controlled by a computer being able to perform similar tasks as any other highly intelligent living being

  • Simulation of human intelligence processes by computers which includes learning, logical reasoning, or problem-solving

If used correctly it can have a number of benefits:

  • Offering better Customer Service through tailored, personal recommendations

  • Protecting business through analysing historical data & mistakes and using technology to learn from those mistakes

  • Automate the manual process and improve operational efficiency

  • Reducing Human Error improving results & outcomes

  • Holding us all accountable based on the data!

If you would like to learn more on Ai, we’ve created a White Paper that goes into a deep dive on the subject.

Ai in Fintech

If we look at the Financial Industry and how Ai can be applied, it really opens us up to some great products which is why the Fintech industry is thriving and one of the most exciting to be involved in.

Use Cases for Ai in Finance :

  • Fraud Detection: Analyse anomalies against historical data to detect fraudulent activity both for the business and consumer

  • Credit Scoring: More accurately provide a credit rating improving the application process resulting in more reliable & accurate decisions

  • Personalised Products: Can we use Ai to offer personalised financial advice based on our spending patterns, age, salary etc..

  • Focused Saving: Using Ai to generate automatic savings based on our spending patterns.

  • Business Protection: Are we taking on customers who are a risk?

  • Better customer service by analysing support calls & monitoring trending queries getting ahead of the curve

These aren’t necessarily new use cases and by no means an exhaustive list, some are already being executed extremely well by the major players in Fintech but also up and comers as well. However with such a broad range of use cases and its increasing involvement in one of the most integral industries, surely we need to make sure Ai is being used in an ethical way?

So what is Ethical Ai & how do we ensure we are using it correctly?

Ethical Ai is pretty much what it says on the tin, Ai that adheres to certain rules, responsibilities & accountability to ensure the decisions it makes & the suggestions it produces are free from bias and unethical thinking, making sound & accurate decisions that have a benefit.

We have to remember the power technology now has, how much of an influential part of society it has now become, and ultimately how much we need to control it. It’s therefore important to ensure as we develop these Ai algorithms & machine learning models, they are based on sound ethics and principles to ensure they are not biased, prejudiced or operating unfairly.

Fortunately, a lot of research has been done into Ethical Ai & principles to stick to, and as a Google Cloud Premier Partner we learn from the best, from people who are at the forefront of innovation.

Google has a number of pages that go into ethical AI and the principles to adhere to but I wanted to highlight the key ones that they stick to as if it works for Google, it should be somewhat a golden standard for the rest of us.

1. Be socially beneficial.
2. Avoid creating or reinforcing unfair bias.
3. Be built and tested for safety.
4. Be accountable to people.
5. Incorporate privacy design principles.
6. Uphold high standards of scientific excellence.
7. Be made available for uses that accord with these principles.

Why is it so important we adhere to Ethical Ai in Finance?

The Financial industry is one of the most regulated industries in the world, and one that has to constantly be held to account, surely our use of Ai should follow suit? If we get it wrong it can have catastrophic effects as we’ve all either witnessed, studied at school, or read in the news.

If we generate Ai models that start to become corrupt, use unethical data sources and make decisions that are wrong, we are not learning from our mistakes and merely leaving ourselves open for an even bigger crash in the future.

This is why it’s imperative that the Ai models we create learn from our own mistakes, we don’t use it to justify supplying products to those who aren’t suitable for them. We used it to analyse the vast amount of historical data we have, make unemotional, unbiased decisions that ultimately benefit us all in the long run.

Used correctly Ai is an extremely powerful tool and without a doubt one of the biggest enablers in every industry. This is why it’s so important we stick to the principles above, technology is meant to make a difference, not open us up to a faster way to repeat the past and at a potentially larger scale.

As a Google Cloud Premier Partner, this is something Netpremacy can help with. With a team of experienced Data Engineers and Scientists, Netpremacy has helped deploy numerous Ai projects. This has helped develop our understanding of Ai projects, how to correctly develop algorithms to ensure they remain true, the data is clean and the results are fair.


If you’d like to know more about Ai reach out to me on alumley@netpremacy.com to see how Netpremacy can help with your Ai journey.

02 December 2020

The energy and utility sector is something most of us take for granted. We pay them for warm showers and to heat our homes, and no doubt we will all be reaching for the thermostat over the long winter months ahead to keep us extra cosy. However, behind the scenes, these companies have to embrace digital transformation and harvest massive amounts of data in order to grow, evolve, and meet the demands of the future. 

The industry is constantly up against challenges; from pressure to reduce emissions, increasing demand, and providing competitive prices to their consumers. Google Cloud has a wide range of industry-leading and energy-specific solutions aimed at helping these businesses digitise faster, and respond to these demands. 

Why data is important for digital transformation

The most valuable asset many companies have is data, and in order to be successful, energy providers must adapt to change, stay current, and realise this. They need to be able to store, read, and analyse data properly, in order to gain a competitive advantage and solve complex business challenges. 

Google’s BigQuery can help businesses bring different data sets together in order to gain those insights, and Google’s AI-powered solutions are the key to unlocking analytics capabilities within your data sets. In this case study, you can learn how Energyworx, an energy supplier in the Netherlands, uses GCP and smart analytics to harvest their data to better plan for future energy demand, and find ways their customers can save on their electricity use. 

If you would like to learn how to use smart analytics to help your business grow and evolve, download our Smart Analytics White Paper here.

A deeper dive into AI and ML energy-specific solutions

Powerful AI models use data analytics to provide actionable insights, and these models can help inspections at scale. This is extremely useful for energy and utility companies who have large areas they need to monitor, such as energy grids, wind farms, or solar panels. Google Cloud’s Visual Inspection solution reduces inspection times without compromising on safety or accuracy and makes organisations more efficient and sustainable. 

Businesses also have the ability to build their own custom Machine Learning model with minimal effort and little to no Machine Learning experience. 

AES is a Fortune 500 global power company that distributes sustainable energy in 15 countries. Their asset inventory is phenomenal, with a value of over £33 billion. They use Machine Learning and drone technology in their wind farms to monitor, manage, and maintain their assets. 

This was the perfect solution, as it was in keeping with their greener vision and they were able to massively scale using Google’s powerful infrastructure. Learn more about their custom made solution powered by Google Cloud’s AutoML Vision here.


For most modern energy and utility companies, contributing to greater customer satisfaction is also an important identifier for success.

AI models like Contact Centre AI allows agents to assist with customer requests more efficiently, leading to shorter call times and improved outcomes, with much quicker resolutions. Learn more about Google Clouds CCAI and how companies are using this technology to modernise.

A smart future for the energy industry

Smart meters are the latest innovation to tackle the biggest challenges facing the industry today. By 2024, almost 77% of EU households will have one installed*. 

This new technology brings multiple benefits such as delivering automated, real-time readings, giving consumers access to their own data; and being able to identify faulty appliances, reducing downtime, and enabling repair staff to be efficient and effective with their time. They also improve the awareness of energy consumption, allowing individual households and businesses to reduce their energy use and generate savings.

Of course, with all new technology advancements, comes an influx of data. Energy companies need a scalable data warehouse to be able to import, process, and anaylse this data. Google Cloud’s BigQuery streams data in real-time and can predict business outcomes with powerful built-in ML models; plus it runs on Google’s infrastructure and is a managed service, meaning companies spend less time developing technology and can focus on their service instead. Read how Halsfund, the largest power company in Norway, uses GCP and BigQuery for the new smart meter network here.

Being a successful energy and utilities company today requires the right technology to solve big business problems. However, in order to truly succeed, you must make the right decisions for your business by making the most of the information you gather.

Now you know what you can do with your data, are you using it to the best of your advantage?

Speak to one of our data experts and we can help you implement this technology and show you how to use it. Contact us here.


*By 2024 almost 77% of EU households will have a smart meter installed, European Commission 

 

15 October 2020

Why the retail industry needs to become more digital

Retail is always changing to keep up with current technology, and as consumer trends evolve over time, retailers need to continue to keep up. The biggest trend and shift in consumer behaviour at the moment is that more people are shopping online. Year on year, the number of online sales is increasing and the outbreak of COVID-19 has acted as a catalyst, which saw 32.8% of all UK retail sales being made online in May this year* – the highest since records began.

It is important that retailers adapt to the ever-changing environment, otherwise, they risk losing touch with their customer base and falling behind their competition. However, the technology we have available to us allows the adoption of new, digital strategies, much quicker than ever before. Now, the customer journey is much more complex, behaviors online can be tracked and used to personalise experiences, anticipate demand, and predict spend.

Adapting digital strategies with AI and machine learning

Artificial Intelligence (AI) and machine learning models are fast becoming an important part of how retail businesses should be run. These models can analyse big data in an intelligent, scalable, and automated way, and find patterns to help businesses understand what their customer really wants. 

Analysing and understanding these patterns properly allows you to make important decisions, which can positively impact business outcomes. The powerful algorithms analyse consumer behaviour, such as items viewed, purchase history, what people search for, and the behaviour of similar shoppers. Machine learning takes this one step further and can predict a consumer’s behaviour often before they even know what they want themselves. Using this data allows you to place the right product, in front of the right shopper, at the right time in the buying cycle, which will ultimately increase conversions and your ROI. 

Use AI to deliver exceptional customer service

The power of AI and machine learning work at all stages of the customer journey, and they are changing the way businesses interact with their customers. Whether that’s displaying products they might like or using Contact Center AI (CCAI) to increase operational efficiency and deliver exceptional customer service. 

CCAI ultimately contributes to greater customer satisfaction; shorter call times improved outcomes, and much quicker resolutions. Machine learning models are able to find the answers to customer queries, so agents can assist with customer requests more accurately, and at a much quicker pace. 

Learn more about Google Cloud’s CCAI and how customers are using this technology to their advantage.

Delivering the omni-channel retail experience 

Just because online sales are increasing doesn’t mean physical sales should have less of a focus. Infact, retailers who deliver an omni-channel experience not only enable customers to convert on every channel, but all aspects of the customer journey is streamlined, leading to more sales and increasing engagement. This is known as the “halo effect”. Shoppers can decide what is the most important factor when making a purchase; convenience, speed, price, ease of return, or the in-store experience. This in turn contributes to brand loyalty, repeat custom, and improved customer experience. 

AI and machine learning can determine how customers interact and move around the brand ecosystem. Retailers can now forecast behaviours across offline and online channels, which brings them closer than ever to predict business outcomes, giving them a competitive advantage.

Read our next blog post “Is your retail business ready for a surge in online sales?” to learn exactly how Netpremacy can help you implement Google Cloud’s AI and machine learning technology into your business.

*Internet sales as a percentage of total retail sales (ratio) (%) https://www.ons.gov.uk/businessindustryandtrade/retailindustry/timeseries/j4mc/drsi