Post written by
AJ Abdallat is CEO of Beyond Limits, the leader in artificial intelligence and cognitive computing.
As disruptive technologies such as artificial intelligence (AI) fundamentally alter the way we live and do business, C-suite attitudes toward IT spending and utilization are shifting. Once considered a cost of doing business, technology is now viewed as a business driver that’s critical to an organization’s ability to perform core functions, even in industries far removed from Silicon Valley.
However, many executives still struggle to determine the ROI to justify investments in AI and machine learning, even as AI becomes increasingly crucial to 21st century business decision-making.
The Changing Role Of Technology In The Enterprise
Except for the IT industry itself, C-suites have historically viewed IT expenses as a cost of entry to do business in the digital age, not revenue-generating investments. Then came new technologies such as mobile, cloud computing and the internet of things (IoT). Suddenly, virtually every enterprise became a de facto software company, and firms that clung to pre-digital ways of doing business were left behind. Executives realized that investing in technology directly contributed to their organizations’ goals — and bottom lines — by minimizing costs, enhancing efficiency and powering innovation.
However, amid this shift in organizational attitudes toward technology investments, AI and machine learning (ML) have been overlooked. A study by TechRepublic found that while organizations recognize there is value in AI and ML, most remain uncertain about how investments in this area can benefit them. The study revealed 56% of respondents predicted that implementing AI/ML solutions would be more difficult than previous IT projects. This isn’t surprising, considering 53% said their users had unclear expectations for enterprise AI/ML projects.
Maximizing The Potential Of AI Means Maximizing ROI
Big data processing and image recognition can be achieved through machine learning, neural networks or deep learning. These technologies are good at handling large datasets and making complex mathematical calculations, but they cannot handle missing/incomplete data and have no situational awareness, nor can they explain their thinking.
As the CEO of a company that combines numeric and symbolic AI to form cognitive AI with humanlike powers of reasoning, I’ve seen firsthand how cognitive AI systems consume and comprehend vast amounts of data generated by large-scale industrial facilities and factor in human expertise to make recommendations at system-wide or device-specific levels. This combination of humanlike reasoning and automation helps business leaders make difficult decisions and identify opportunities. The resulting increases in production efficiency and reductions in waste can generate new revenue and profits, especially in high-value industries. That’s revenue from AI (RAI).
Steps To Achieving RAI In The Enterprise
Cognitive AI is great for solving complex issues where industrial processes involve a continuous flow of data. Examples include energy, refining, power generation and process manufacturing — enterprises where operations never stop, and where IoT alone is insufficient because systemwide oversight is required. However, to realize a return on AI investments, fundamental prerequisites are necessary:
• Data must exist — and it must be able to be leveraged by both humans and machines. While clean data is ideal, cognitive AI systems can consume unstructured data, analytics, IoT data, sensor data and more across silos.
• Codified expert knowledge is instrumental to unlock the value in data. Human-inspired knowledge bases enable the system to compare recommended courses of action against best practices developed by people. Over time, the system becomes smarter.
• Explainable AI is essential for detailing recommendations in a clear manner with transparent information, evidence, uncertainty, confidence and risk, which can be understood by people and interpreted by machines.
• A digital-ready company culture must exist with an innovation-minded team that’s empowered to implement structural transitions kicked into high gear by the adoption of AI technology. It’s vital this culture places importance on data-driven decision-making instead of defaulting to legacy approaches. This is where your AI investment will either thrive or fall flat.
All of this boils down the most important factor in achieving a successful return from AI investments: trust. The value of AI systems may be measured in terms of increased efficiency, decreased waste or accelerated speed. The greatest value lies in providing humans with actionable intelligence so they make better decisions. Keeping humans in the loop via explainable AI is key to developing a symbiotic relationship with the technology and building trust in its results.
AI Is Generating Major Revenue In Major Industries
Today, energy companies use cognitive AI solutions designed to reduce refinery operational costs, improve new product time to market, slash subsurface analysis from months to hours and increase upstream production. For example, the Telegraph reports (paywall) that BP is using cognitive AI solutions designed to increase offshore oil production.
Energy isn’t the only vertical seeing tremendous results from the strategic use of cognitive AI solutions. A report from Morgan Stanley provides quantitative examples of ROI from AI applications:
• Machine learning is analyzing wind farms to make power predictions 36 hours in advance, enabling providers to make supply commitments to power grids a full day before delivery and increase the value of wind energy output by 20%.
• In Australia, mining companies are using autonomous trucks and drilling technology to cut mining costs, improve worker safety and boost productivity by 20%.
• If U.S. utility companies used AI-powered asset management software, costs could be cut by $23 billion annually, reducing outage frequency, overall footprints, installation times and copper cabling usage.
• A European automaker built a “fully digitized” factory and significantly reduced manufacturing time while boosting productivity by 10%.
Achieving the maximum ROI from AI projects requires a shift in organizational mindset. By seeing AI as a business driver rather than overhead, enterprises have the opportunity to view AI as a valuable asset that helps business leaders make more informed decisions and deliver new revenue from AI to the bottom line.