Enterprises are continuing to move to the next level from experimenting with AI to adopting AI in large-scale solutions across their business. With the lessons learned from the paradigm shift of business operations in 2020 to newfound opportunities for technology modernization, we can expect to be pleasantly surprised with the advancements companies will make in 2021.
Enterprise AI Trends in 2021
Artificial Intelligence (AI) had been gaining a lot of traction long before the Covid-19 pandemic’s impact on the world. Previously, IDC had predicted that worldwide spending on AI technologies will be $37.5 billion in 2019. Global investment in AI Startups has steadily risen from $1.3 Billion in 2010 to over $40 Billion per year since 2018. While COVID-19 has had an initial impact on reduced enterprise spending, it has definitely contributed to the increased potential value of AI and accelerated its adoption in enterprises. McKinsey’s State of AI survey reveals that 50% of the participants report their organization adopting AI in at least one business function.
The promise of AI is compelling and many companies are now turning towards it to exploit the longer-term opportunities in leveraging intelligence derived from their own data. In 2021, we’ll continue to see the adoption of AI for a more strategic and transformative impact.
We gathered a collection of Enterprise AI Trends that we believe CEOs and organizational leaders will focus on in 2021.
1. Accelerated adoption of AI as a Service
Some of the major barriers to adopting Artificial Intelligence across enterprises large and small are infrastructure and resource investments, with smaller firms finding it much more challenging. AI as a Service or AIaaS provides businesses the power of AI without the need for dedicated in-house resources, infrastructure, and large investments. With enterprises being able to recognize the value of AI, and the availability of AI service providers, it is an ideal situation to create early adopters of AIaaS. The extremely competitive landscape in the market with AI-driven solutions and products is providing more revenue opportunities for businesses that have already invested or adopted AI-based solutions in their offerings. In such a situation, time is of extreme essence for companies to adopt and scale AI to gain a competitive advantage in the market.
A recent survey highlights that organizations that use a 3rd party solution to manage their machine learning operations (MLOps) spend an average of 19-21% less on infrastructure costs and have seen improved outcomes in comparison to those that build and maintain their own ML systems from scratch.
Especially for small and medium-sized businesses, AI as a Service provides advanced infrastructure at an affordable cost, a reasonably faster time frame, and most importantly creates opportunities for revenue without a huge cost and time overhead. It will enable them to compete against larger businesses, without the financial outlay and new resource requirements.
2. A stronger push for scaling AI
In 2021, we can expect organizations to step up their game from building and executing experimental AI systems to building them to support their businesses at scale. Many companies have struggled in Scaling AI from a Proof of Concept (POC) stage or development of use cases into full-scale solutions integrated with the business. However, the rapid digital transformation of businesses due to Covid-19 has made it necessary for businesses to quickly shift from experimentation to scaling AI in businesses to drive growth, employee engagement, customer adoption, revenue, and customer satisfaction. Companies that lack expertise internally to scale AI will look to partner with Enterprise AI solution providers.
In another survey conducted by McKinsey, titled ‘Global AI Survey: AI proves its worth, but few scale impact‘, it was observed that companies that adopted AI have seen benefits such as revenue increases and cost decreases across various functions in the business, with adoption of AI in Sales and Marketing seeing the highest revenue returns and adoption of AI in manufacturing yielding highest cost decreases. Such results are only possible with proper scaling of AI operations across the various business functions and will enterprises to adopt them at a faster pace.
3. Greater focus on unstructured data
We can expect enterprises to leverage machine vision and natural language processing (NLP) to structure their unstructured data such as images, emails, customer interactions on websites, social media, apps, etc., into the next year. NLP is highly relevant and extremely valuable for companies that have large volumes of data generated by customers or through services like chatbots.
The other side of the coin when it comes to leveraging unstructured data is Robotic Process Automation, where companies can leverage unstructured data such as images, sound and video to develop physical robotics in industries where automation can help with manufacturing, packaging and transporting goods.
Many organizations have been struggling to optimally leverage RPA due to its limitation in processing unstructured data. Uber recently has recently offloaded its self-driving car unit to Aurora Innovation who are going to be focusing exclusively on self-driving technology. Self driving cars might be the most mature and advanced form of RPA where machines may have to interact with humans autonomously, however, there have been lesser yet still impressive advancements in RPA to encourage enterprises to continue to invest in this space. The most well known of such examples is the robotic assembly of Tesla automobiles. It’s a reality that many industries aspire to achieve which is driving the innovation as well as investment in AI driven Robotics.
4. Increased adoption of AIOps
Artificial Intelligence for IT Operations or AIOps is the process of leveraging Machine Learning and Analytics to support complex technology operations and provide the intelligence for automating and enhancing IT-related processes.
MarketsandMarkets Research estimates that the AIOps market globally will grow to $11.02 billion by 2023 from $2.55 billion in 2018, at a compound annual growth rate of 34%. An AIOps solution enables IT to improve key processes and make decisions through improved data analysis and artificial intelligence. Enterprises will look for AIOps providers to empower cross-team collaboration through data correlation, drive digital experience, and integrate seamlessly into the whole IT operations management value chain.
5. AI in Cybersecurity
Businesses with digital platforms have always been susceptible to cyber attacks. In 2019, there were over 1500 data breaches and over 164 Million sensitive records exposed.
In 2020, Twitter, Zoom, MGM Resorts, and Mariott witnessed significant cyber-attacks and data breaches this year. With 43% of all cyberattacks directed at small businesses, it’s more imperative for businesses to invest in stronger and smarter cybersecurity solutions. In 2021, we can expect the emergence of AI-driven cybersecurity products to build predictive capabilities and strengthen the defenses of enterprise data.
When it comes down to enterprise AI in 2021, the possibilities are endless. Enterprises are continuing to move to the next level from experimenting with AI to adopting AI in large-scale solutions across their business. With the lessons learned from the paradigm shift of business operations in 2020 to newfound opportunities for technology modernization, we can expect to be pleasantly surprised with the advancements companies will make in 2021.
At Qualetics, we specialize in playing the role of the Intelligence Strategy partner by providing guidance to our clients in identifying the best use cases for their data and seeing through the implementation with our AI platform. Reach out to us should you see your organization benefiting from opportunities in adopting and scaling AI.
