This month is a big month for Artificial Intelligence (AI). 77% of IT Employees said that they have prevented more breaches as a result of AI-powered tools. 8% of them observed that AI detect threats before their security teams.
Cylance®, Inc. announced the results of its inaugural report. “Artificial Intelligence in the Enterprise: The AI Race Is On”. The survey polled 652 IT decision makers in the US, UK, Germany, and France. It found that optimism concerning the worth of AI-powered solutions in the enterprise is high and plans to continue investing in the technology are widespread. The report outlines, however, AI is already creating a big impact on the enterprise, analyzing trends in security, operational efficiency, marketing, employee perceptions, and other areas.
How to Ensure AI to ROI
” Executives were 1st created the leap of religion in AI. It has been the first to begin experiencing the rewards, particularly in the prevention of cyber attacks. Over the next year, I only expect to see this trend to be accelerated. Cylance will continue to advance AI for cybersecurity with our technology in its third generation.” said Daniel Doimo, President, and COO at Cylance Inc.
Key Discoveries include:
Organizations are already investing in AI. This will be the only increase. Nearly all of the IT decision makers surveyed and said that they are currently planning to invest in AI-powered solutions in the next two years. 60% already have AI in place. Additionally, 79% say AI has a top priority for their boards and C-suite executives.
AI is moving like the needle for security teams. 77% have prevented a lot of breaches of their use of AI-powered tools. 81% say AI was detecting threats before their security teams could. 74% say they won’t be able to cope with the cybersecurity skills if they don’t adopt AI. Also read AI’s Silent Takeover on Humanity
AI is a competitive advantage. 87% of IT decision makers see AI-powered technology as a competitive advantage. for their IT departments. 83% are specifically investing in AI to beat competitors.
AI is living up to its promises. 86% say that the AI has lived up to their promises. 64% of IT decision makers expect to examine ROI from their investments in AI in fewer than 2 years.
AI opportunities abound. 93% say it’ll produce new job opportunities. 80% saying AI will lead them to hire new workers and retrain existing employees.
Innovations of AI:
Facebook has already announced that it will dramatically increase its investment in AI research and development. It doesn’t fall behind as a technology innovator. Amazon as a section of its NYC Summit 2018 declared new capabilities for its artificial intelligence machine learning and computer services on the AWS cloud. This includes a new way to build new models. Google Cloud Next ’18 conference next week is expected to lift the lid on a number of new AI initiatives.
AI Should Understand AI
Three companies, along with Microsoft, are looking to gain dominance in a market that is still only starting to grow. To do that, they need to offer enterprises. It is a way of using and incorporating AI into their business. DNA will not disrupt business processes and business strategies. What do these companies need to offer? Pascal Kaufmann is a neuroscientist and AI entrepreneur and founder of Switzerland-based Starmind found a technology that applies neuroscientific principles to AI development. It identifies experts on any subject within an organization. It also connects them to fellow members of the organization.
Kaufmann said that AI has the most impact if the three conditions are met: 1. It is not a pure automation play. Simple statistics could do the job without AI. 2. The data set is made of both large and small data. 3. Self-learning algorithms should operate in a way that they outsmart human beings, providing the foundation for a business case.
The ROI [Return On Ivestment] of an AI technology can be quantified best. It is benchmarked with human workers doing the same job. For example, even if the machine was 10 times slower, being 20 times more cost-effective would already result in a convincing business case.
How AI Generates ROI
“When you do AI right, it generates value. ROI for the enterprise is an excellent premise. However, the full potential of AI hasn’t been attained. Many conventional AI systems are merely machine learning, or neural networks, or deep learning. They’re good at handling large sets of data but lack situational awareness or the ability to navigate around missing or incomplete data. They get stuck.” said AJ Abdallat, CEO of Glendale.
The example of a machine learning system can be trained to identify photos of chairs. AI systems acquire cognitive reasoning abilities, an evolutionary leap beyond conventional AI. They use a human-like ability to understand, correlate, learn, teach, reason, and solve problems faster than conventional AI solutions.
How AI Models Help
At the heart of all AI is a model. Mac Steele, director of San Francisco-based Domino Data Lab said that organizations fail to achieve the promise of their models. They assume models should be managed like assets, data, and software when they are quite different. To be successful in building, deploying and sustaining models at large scale, companies need to develop an organizational capability of model management. Leading companies have built a strategy comprised of five elements. Steel outlines the elements as follows:
Model Technology – The software tooling and infrastructure stack that gives data scientists the agility they need to build and deploy models.
Model Development – Business processes and systems that allow data scientists to rapidly develop models, and drive breakthrough research.
Model Production – The mechanism of operationalizing data science research projects to a live product or output that affects the business.
Model Governance – The flexibility to constantly monitor the activity, performance, and impact of models and data science initiatives across the organization.
Model Context – At the heart of Model Management, Model Context encompasses all knowledge and insights generated while building or using models.
