How Can Companies Benefit From AI?

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How Can Companies Benefit From AI?

Here's what 22 executives who are familiar with AI and all its variants said when we asked them, "How can companies benefit from AI?"

· AI Zone ·
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To gather insights on the state of artificial intelligence (AI) and all its variants — machine learning (ML), deep learning (DL), natural language processing (NLP), predictive analytics, and multiple neural networks — we spoke with 22 executives who are familiar with AI.

We asked them, "How can companies benefit from AI?"

Here's what they told us:

Fraud and Risk Mitigation

  • Every piece of software can benefit — security, marketing, IT infrastructure, fintech. A lot of data and a cloud infrastructure enable the application of supervised learning to discern patterns. Just don’t go too broad. Start small and expand.
  • Depends on the vertical. Oil and gas to know where to drill next — features the next best area. Finance gets real-time risk exposure information and fraud detection. We enable more sophisticated behavior-based intelligence for cybersecurity across many industries. Done fast with high-velocity data feeds. Using natural language processing (NLP) to see what’s in an email. Use machine learning and deep learning in Facebook to identify who the people are in photos that are uploaded. Train a machine learning model with more data. Connected and self-driving cars with auto correction resulting in safer driving.
  • Be more secure; know what they have at risk. Know the relative value of data. Behavior drives insecurity. Know which solutions are better than others. Having a mathematically sound notion of measuring security = resilience. The capability to limit the impact of attacks. Assume certain parts will be compromised. How to limit the impact on the rest of the environment in a particular time and space. Based on internal controls. If you restart a server, perhaps you need a two-man, two-factor authentication? However, this doesn’t scale. AI is the superpower of automation. We need to distribute to the rest of industry.
  • ROI can be obtained with basic predictive and prescriptive analytics. Operational business intelligence (BI) is using ML to predict business outcomes. Amazon is using ML to drive its recommendation engine. Insurance companies are able to predict the risk of drivers based on data. Real-time analytics on how you drive. Amazon is driving significant changes in retail.
  • Companies can benefit greatly from AI, especially when it comes to adding a layer of cyber protection to their business. Cybersecurity is a growing threat for enterprises, and AI can not only respond to cyber threats but can also detect a threat before it has the chance to infiltrate a system filled with sensitive information.

Customer Experience

  • Improve both the employee engagement and the customer experience (CX) in every vertical industry.
  • Take massive data sets, analyze, and provide highlights. Here’s something of interest where you might want to dig deeper. Improve customer service and CX.
  • With an analytics portfolio. More sophisticated operations research in transportation and logistics. Manufacturing is using IoT. Challenging analytics in nature helps with SDC to advance process control. Smart customer interaction. Propensity and customer analysis with the promise of building smart applications.
  • Look at the pipeline of things the business is doing. It starts with learning. Dealing with a lot of data and customer interactions. Companies need to think about how they can become more data driven to make better, faster business decisions. More focused on the CX than using data. Look at the lines of business data to understand which customers to go after and how to automate doing so. Also, use AI/ML to help with the operational aspects. Reduce downtime and millions in lost revenue due to customer downtime. Predict and prevent downtime rather than react to it. We’ll take six months of a client’s historical data and tell them where they had performance problems — what happened and why two hours before it occurred. This is typically an “aha” moment for the client in how they can leverage AI/ML in operations and security. Ensure systems are running smoothly and self-healing.
  • By thinking about problems differently. Financial analysts will know what product is most relevant and should be recommended. Any decision maker is more informed and inspired. What to do with the insight you are gaining.

Productivity and Efficiency

  • Recognize the capabilities AI provides using software and automated technology. Start with the problem, not with the technology. What problems do we have that we can automate now?
  • Start by using ML. Agriculture is using drones to scan fields for crop damage and then applying AI to extrapolate the findings and estimate the total damages. Among logistic regression and anomaly detection, the most prevalent are ML and the use of TensorFlow.
  • AI can replicate day-to-day processes with a greater level of accuracy and can do them without downtime. For example, AI can be used as a primary channel of interaction with customers around the clock, a capability that most companies don’t provide customers now. Or they can provide a simple chat based customer service using natural language processing. Robotic Process automation can be used to improve their overall efficiency of processes.
  • Our clients use our AI technology for three main reasons: 
    1. Increasing margins, not only by monetizing increased efficiencies yielded but also by cutting administration costs through intelligent legacy data clean-up and other data governance activities. 
    2. Increase efficiencies. As they begin to automatically distil key information from their content, they minimize the arduous work that would have otherwise have to be done manually and get to the value effort quicker. Their staff can then be re-focused on more productive tasks in the business. 
    3. Risk mitigation. Our clients can apply AI to data governance tasks, for example, to automate the determination of whether contracts deviate from accepted norms and risk profiles.
  • AI is improving the users’ experiences, with contact on their terms. For the internal user, it’s developing a more efficient workplace where tasks can be automated to increase efficiency and productivity in the business, increase the amount of time an employee spends on work to drive outcomes, and reduce the time spent on tasks. For the customer, it means a seamless experience and an easier way to engage or transact. The airline industry is perfectly positioned to effectively adopt AI to enhance the customer experience to better help travelers. Imagine being able to open a chat on your phone when your flight is delayed to figure out how it will change your connecting flight — without having to wait in line or on hold for customer service. That’s a change I would welcome.
  • Like AI, predictive analytics is very broad. It’s the next trillion-dollar economy. Amazon’s recommendation engine is dominating predictions of what people will buy next and inventory management. We solve asset failure and yield and efficiency optimization. This is projected to have a $630 billion impact by 2025.
  • Enormous opportunities with the benefit of scaling extending advanced capabilities to a broader audience. Rely on a group of data scientists to build and score the model. Only so many models can be built manually. Use ML to automate part of the process to make it easier and consumable for LOBs. Automate 60-80 percent of the process to free up business professionals. Make people more productive.


  • Make informed decisions. Keep fraudsters out. Accelerate processes. Make recommendations. Marketing automation and optimization. Very few exciting additional things. A lot of business practices can now be more well informed. Unlike before, you can afford the time and money to look at the data to make an informed decision. You cannot do this unless you have a culture to collect, measure, and value data. Achieving this data focus is a huge benefit even without AI. A lot of businesses will continue to operate on gut feel rather than data. They view data as a threat versus an opportunity. These businesses ultimately will not survive
  • AI/ML eliminates the need for site surveys. Cloud for processing and running powerful algorithms. Virtual wireless estimates throughput speed.
  • Amplify knowledge across the organization. Amplify human expertise so drug development takes place and get to market faster, CSRs are able to improve brand loyalty and customer satisfaction, and maintenance teams are able to reduce the downtime of airplanes while also reducing incidents.

How else do you see companies benefitting from AI?

Here’s who we talked to:

  • Gaurav Banga, CEO, and Dr. Vinay Sridhara, CTO, Balbix
  • Abhinav Sharma, Digital Servicing Group Lead, Barclaycard US
  • Pedro Arellano, VP Product Strategy, Birst
  • Matt Jackson, VP & National General Manager, BlueMetal
  • Mark Hammond, CEO, Bonsai
  • Ashok Reddy, General Manager, Mainframe, CA Technologies
  • Sundeep Sanghavi, Co-founder and CEO, DataRPM, a Progress Company
  • Eli David, Co-Founder and Chief Technology Officer, Deep Instinct
  • Ali Din, GM and CMO, and Mark Millar, Director of Research and Development, dinCloud
  • Sastry Malladi, CTO, FogHorn Systems
  • Flavio Villanustre, VP Technology LexisNexis Risk Solutions, HPCC Systems
  • Rob High, CTO Watson, IBM
  • Jan Van Hoecke, CTO, iManage
  • Eldar Sadikov, CEO and Co-founder, Jetlore
  • Amit Vij, CEO and Co-Founder, Kinetica
  • Ted Dunning, PhD., Chief Application Architect, MapR
  • Bob Friday, CTO and Co-founder, and Jeff Aaron, VP of Marketing, Mist
  • Sri Ramanathan, Group VP AI Bots and Mobile, Oracle
  • Scott Parker, Senior Product Marketing Manager, Sinequa
  • Michael O’Connell, Chief Analytics Officer, TIBCO
ai, business, machine learning, supervised learning

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