Accelerate competitive advantages with AI/ML at scale
The engines of digital growth
Artificial Intelligence (AI) and Machine Learning (ML) are the twin engines accelerating the next digital transformation revolution. As in many previous trends, winners will lead by embracing change, leaving behind slower adopters.
Worldwide revenue of AI is expected to grow by 16.4% this year to reach $327.5B. The market will continue to grow at a CAGR of 17.5% through 2025, reaching $554.3B.1
Progress never waits for anyone. The time to adopt AI/ML at scale is now. There are some promising signs that enterprises are moving in the right direction. Nearly 43% of respondents to a recent industry survey said that AI/ML matters “way more than we thought.” In addition, 76% of companies prioritize AI/ML over other IT initiatives and 83% of them increased AI/ML budgets year-on-year.2
Why care about AI/ML?
Valid reasons could be discussed for not adopting AI/ML at scale, but these are beyond the scope of this blog. This article attempts to shed some light on the competitive advantages that AI/ML infuse into businesses to get them ahead of the competition.
More efficient marketing
This area is one that most of us equally love and hate. Companies already own massive troves of consumer data and AI models know each consumer at a personal level that was previously impossible. AI/ML models can study available data and learn buying history and behavioral patterns. With that knowledge, AI/ML can provide companies objective insight into what product and service to market and why market them. Furthermore, AI/ML can identify which consumer segments to sell to, when and where to market the offerings.
The value of AI/ML models lies in their ability to continuously study massive data sets and make objective recommendations based on findings. Most of us have experienced a suggestion for a different product popping on the screen while pondering about buying another item. The suggested offering is the courtesy of the AI/ML model of your vendor.
While many consumers may not view AI/ML interactions positively, the models make marketing more efficiently targeted and deliver significant business results to organizations that deploy them at scale. Efficient marketing identifies customer-brand touchpoints and interactions with the right targeted messages. AI/ML replaces the old shotgun approach with a practical, focused, and more economical one.
AI/ML models significantly improve business productivity and employee satisfaction. The models can take over mundane, low-engagement and some administrative tasks. This lets employees focus on handling complicated core tasks requiring expertise and delivering higher value to the business. In other words, AI/ML will make the workplace more attractive and engaging to employees. Managers and employees will focus on business aspects that require their full potential, improve measurable contributions and lead to more satisfaction. While that may sound idealistic, it is not.
Many enterprises that found greater success in adopting AI/ML and improving business outcomes sought the help of third-party MLOps solutions. Three reasons guide third parties’ use:
- Buying a third-party solution is up to 21% less expensive than building your own.
- Companies’ environments are getting more complex for internal resources, with 71% of organizations having hybrid environments. Complex deployments make it harder for internal teams to deploy and manage solutions.
- Users of third-party solutions conserve up to 49% of the time of the company’s data scientists.3
Improved security and safety
AI/ML benefits shine in the area of cybersecurity. With the ubiquity of cyberattacks and the increase of malicious breaches, AI is the best-suited solution for managing cyberattacks. Monitoring, detecting and responding to cyberthreats plays to AI/ML strength and their ability to sift through large data sets and identifying anomalies with a high degree of accuracy. As cyberattacks continue to arrive every few seconds, human IT resources can no longer effectively inspect incoming traffic. AI models can handle the incoming traffic, identify actual or severe threats to hand over to IT teams. The collaboration leaves human resources focused on severe threats and more valuable security tasks. AI models have a significant advantage over their human colleagues; they do not get stressed out over incoming threats. Enterprises need AI/ML solutions to scale digital operations with robust security.
The ability of AI/ML models to identify anomalies in the system can extend to many use cases, including fraud detection. That ability is particularly significant in protecting digital businesses and consumers. Many of us probably remember getting a call from your credit card security team inquiring about a small charge on your card in a place that is thousands of miles away from you. AI/ML models set red flags that trigger credit card verification calls. Consumers don’t mind this side of AI applications.
AI’s ability to sift through massive data sets is beneficial to organizations in regulated industries. Maintaining auditable continuous compliance is necessary for meeting regulatory requirements. AI/ML models are used to examine digital traffic, identify data sets that may have inadvertently stepped over the line, and alert compliance teams to correct potential problems.