Nowadays, many businesses are going through hard times with constant pandemic breakouts imposing economic, logistical, and technological challenges globally, making companies want to adapt rapidly. With face-to-face meetings being changed to video conferences to stay in touch, different cutting-edge technologies like artificial intelligence (AI) and machine learning (ML) are taking the next big step in helping humanity to augment.
In fact, AI and Machine Learning are so powerful that they’re projected to improve productivity by as much as 40% by 2035.
Companies, big and small, strive to remain agile, experimenting with the new techs to obtain bigger ROIs. And so, this article will elaborate on what impact AI and ML make across industries and how system analysts, software engineers, and other computing professionals can integrate them to drive innovations.
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How Google is Boosting Search Experiences with AI
During its recent Search On 2021 event, Google announced it was starting to incorporate artificial intelligence and machine learning into its search experiences. These upgrades and new search features allow users to find more helpful information and determine the overall relevance and reliability of the information gathered.
Here’s a look at two ways Google is using AI and machine learning to upgrade its platform.
MUM enhancements to Google Lens
With Google Lens, you’ll soon be able to ask Google a question about what you’re looking at by pointing your phone at an object of interest. For example, at Search On 2021, Google demonstrated how a user can take a photo of a broken bike part and ask, “how do you fix this.”
The tech giant can make this happen using their new MUM algorithm (MUM stands for Multitask Unified Model). This “super algorithm” drastically improves the search game by giving users a way to perform complex searches by adding images as a form of input, in addition to using keywords. What’s more, Google MUM can explore different languages to provide the best answer to a given search.
Google expanding the ‘About This’ result panels
Building on its relatively new “About This Result” feature, which Google launched earlier in 2021, the company will soon be adding more panels to offer valuable insights about any search result. Users can eventually expect to be able to:
- Find out more about the publisher by seeing what a given source has to say about itself;
- Check what others have to say about a website through reviews, news, and other content that 3rd parties have written about the site;
- Discover additional information in the “About the topic” section, where users can see related results on the same topic from other websites.
This change will help brands to position strategically, providing users with valuable insights about companies and what they can expect from dealing with a particular business.
The Rise of Data in Predicting User Behavior
An entire consumer lifecycle can be analyzed and predicted if the behavioral data is leveraged. Predicting behavioral factors is more than just buzzwords – cutting-edge analytics and ML provide you with opportunities to better connect with the consumer base and grow your business. The problem is that users’ behaviors are constantly changing, making it hard for software engineers to keep up.
Here are a couple of examples of how companies are using AI and machine learning to predict user behavior.
AI-Enabled UX Design Frameworks predict user behavior
The financial software company Intuit built its “Aid Assist” tool to help small business owners navigate the densely packed CARES Act in the US. Their software engineer team used Knowledge Engineering to create personalized pathways for each user based not only on their data but also on their emotional states as predicted by the software. These unique pathways provide the user with only the most relevant information found inside the CARES Act and deliver guidance according to how they interact with the content.
So, instead of overwhelming users with a highly technical and complex document, tools like Aid Assist use artificial intelligence to power “generative design frameworks” that give users a highly personalized experience.
Machine learning-based forecasting tools like Alembic provide reliable predictions
Part of the problem when predicting user behavior isn’t so much that the data isn’t available but that it’s increasingly difficult to combine data from website analytics, social media, sales performance, and other sources into meaningful and actionable insights.
Machine learning-based platforms like 
make it much easier for businesses to not only summarize all their customer information but also optimize their marketing campaigns and strategies across multiple channels. By pulling and tracking data from a wide range of sources over significant lengths of time, Alembic can accurately predict how users interact with companies at all touchpoints on the customer journey.
Using Machine Learning to Spot Trends in Real-Time
For any business or industry, staying on top of changes in consumer trends and demands is an ongoing cycle that can take a couple or even several years of product research and development. In today’s climate, however, especially following the abrupt onset of the COVID pandemic, trends are shifting so quickly that it’s almost impossible to keep up.
Enter machine learning. Predictive analytics tools like AI Palette leverage machine learning-based engines to help major companies pick up on new emerging trends when they arrive on the scene.
These innovations help software engineers to take consumer research to the next level. By observing what’s currently popular among consumers, hardware designers and businesses overall can predict what consumers will demand in the near future and provide the right kind of value in their products and services.
How AI is Accelerating Content Creation
High-quality content is vital for any business looking to scale. But what if you are just starting out having a small team of software engineers and database administrators who want to focus on the product itself first? That’s where AI-generated content can help.
Software like Jarvis or Outranking is “automatic” copywriting tools that use artificial intelligence to craft content, making it easier and faster for businesses to write ad copy, landing pages, and blog articles.
Other tools like SurferSEO help businesses to automate their content strategies by providing data-driven recommendations for keywords and article topics.
That said, AI- and ML-generated content still requires a final touch from humans. Humans know best what other humans like to read and are looking for. Consequently, there will continue to be a need for “manually operated” search engine optimization tools.
Human-Powered Search Engine Optimization Tools
Many powerful search engine optimization tools don’t fully rely on the likes of AI algorithms to inform content strategies. search engine optimization tools like SE Ranking, Ubersuggest, and QuickSprout evaluate suggested keywords, “spy” on what competitors are up to, and track backlinks to deliver valuable content to users to maximize performance on the SERPs.
These “human-powered” search engine optimization tools, in addition to more ML- and AI-based platforms can help companies maximize the value they give to their customers and optimize their sites’ content.
The Bottom Line
Artificial Intelligence and Machine Learning technologies have already become a central part of our day-to-day lives and are making a significant impact across different industries now.
That is why software engineers and businesses overall have to be agile, utilizing ML tools and AI technologies to improve productivity, encourage changes and grow their bottom line.
At the end of the day, though, the power of Machine Learning and AI depends on how humans can apply those techs to boost various methods and approaches.
