In 2019, market intelligence firm IDC projected that global spending on AI systems would grow by more than 2.5 times by 2023, from $38 billion to $98 billion.
That was before the pandemic disrupted the way the world does business and sped up the adoption of AI technologies everywhere. The AI ecosystem is progressing at an unprecedented pace.
That’s why every entrepreneur who wants to stay ahead of the competition should stay up-to-date with AI trends. I will mention 10 trends to keep an eye on through 2021 and beyond.
Factors That Have Affected AI Trends
COVID impact on AI has been the most recent disruptor. And, just as no area of business remains untouched by the pandemic, no market sector remains untouched by AI.
With so many people working remotely, companies have started to look for ways to maintain some normalcy in their daily operations. AI has been instrumental in achieving that goal.
And it’s not only operations that have benefited from a pandemic-driven reliance on AI. According to Alisha Mittal, of the Everest Group, organizations have also leveraged it to improve the stakeholder experience.
Combined with the preexisting drive for innovation and AI adoption, these factors have given tremendous importance to the following 10 AI trends.
1. Heavier Reliance of IT on AI
The return on investment from AI solutions in the IT sphere has already been significant. Many organizations are seeing those ROIs and pushing to implement their AI efforts at larger scales.
There is an increasing number of AI systems that can predict common IT issues and, for the most part, correct them without human intervention.
These systems will become more refined and capable over time. They will grow in market share and allow teams to reallocate costs more efficiently.
2. Augmented Analytics Reshape Business Intelligence
Exploring and analyzing complex data sets is one of the most time-consuming tasks data analysts perform.
Companies can leverage AI to optimize critical portions of the workflow. That way, they can scale their BI practices and come up with useful predictive and prescriptive models faster.
This will reduce the need to have data scientists on staff, which will help relieve the persistent data science talent gap. BI teams themselves will be able to handle the workload more directly.
3. Advances in Smart Manufacturing
The pandemic showed businesses and consumers how fragile supply chains can be. There is an urgent need to transform operations and make them more resilient.
As the world transitions into Industry 4.0, AI can uncover hidden patterns in the data and help manufacturers make smart decisions in real-time. These patterns will be analyzed in conjunction with historical data to create optimized and automated workflows.
As an example, the cannabis industry is experiencing a wonderful technology boom due to deregulation efforts around the globe, many US states are allowing for consumer sales either recreational, medical use or both, and the US government taking a closer look at descheduling and decriminalizing marijuana at the Federal level.
These steps have resulted in an industry vacuum providing startups low barriers of entry to compete for market share. Luckily, we have seen quite a bit of automation and manufacturing enhancements of late along with the ability to easily manage and assess large quantities of data.
The cannabis space has already been able to take advantage of home automation efforts where entire growing facilities are completely managed from a mobile device such as an iPhone for temperature control, water/nutrient supply lines, dehumidification, and lighting across multiple rooms. Soon, we’ll see this space adopt machine learning & AI to assist with keeping optimal growing room conditions based on that location’s historical best yields being reported, etc.
One should expect to see more tweaks to systems as AI becomes easier to deploy along with no/low code platforms allowing everyday users to create complex assessment criteria with ease. There will be a pattern of shortage reductions and quality increases through advances in disruption prediction, robotic process automation, and real-time quality monitoring.
4. Emergence of AIOps
The increasing complexity of IT operations for the past several years demands new management methods.
New AI platforms offer improved analysis and visualization of operations data for IT operations teams. This empowers them to improve processes and decision-making in data-driven ways.
5. AI and Cloud Collaboration
AI is poised to benefit both providers and consumers of cloud services, driving even wider cloud adoption in the coming years.
Managing and monitoring cloud resources generates enormous amounts of data. Artificial intelligence can then analyze the data to help improve services and strengthen weak areas.
6. Growth of a Newcomer: AIoT
The Artificial Intelligence of Things, or AIoT, is a relatively new concept. It comes from the combination of two established technologies: AI and the Internet of Things.
This trend uses the data generated from people’s usage of IoT devices to automate business processes without human intervention.
Giants such as Google and Microsoft are already starting to implement the technologies involved in this concept.
7. Higher AI Accessibility for Non-Techies
The emergence of no-code AI platforms is a boon for companies without advanced data science skills.
Thanks to automated solutions, teams without those skills are starting to gain access to powerful data-enabled capabilities.
There is still work ahead in developing full-fledged automation platforms that don’t require any custom code. We’re already seeing the advantages of that method, though.
8. Increased Innovation Through AI-Powered Automation
AI is helping all industries make better decisions at lower costs, freeing up resources for innovation.
There is a strong focus in enterprise on using AI to automate decision-making and optimize efficiencies. AI can also help gain deeper insights from data within all organizational functions.
AI solutions are moving farther and farther away from the need for constant human input.
As this focus expands from early adopters to businesses at large, innovation will spread at an unprecedented rate.
9. Natural Language-Driven Systems
Siri, Alexa, and other systems that rely on natural language processing (NLP) and automated speech recognition (ASR) keep getting smarter.
That’s not going to stop. NLP and ASR will keep improving and streamlining the customer experience. One day, they may be able to decode a sentence the way a human would and offer more natural, human responses.
10. Responsible and Ethical AI
Rapid technological progress often pushes against the boundaries of ethical conventions. That doesn’t mean AI is inherently unethical, but it does mean that we need to have a dialogue about the potential implications of its use.
The challenge is incorporating unbiased diversity, representation, and relevance in the problems we recruit AI to solve.
After all, for all its sophistication, AI is still a product of the human mind, run by computers that can’t make moral judgments. It’s not yet capable of gaining ethical awareness and self-correcting.
Terms such as responsible AI and model interpretability are showing up more often outside of academic circles.
That’s an important step in ensuring fairness for everyone whose lives are affected by AI technologies.
And, in today’s world, that’s everybody.
Accessibility for Everyone
As AI trends continue to develop, it’s increasingly important for business leaders to remain aware of how the rapid changes in technology may impact the industry.
Fortunately, advances toward making these technologies more accessible to all are bringing down the technical barriers and reducing the learning curve for non-technical leaders.
More and more, it’s possible for everyone to leverage the power of AI for business. This is a golden opportunity.
If you’d like expert assistance growing your startup either through consulting within our network or looking for a board member, advisor, etc then please contact me.
