Where Are the Biggest Opportunities for AI?
Where Are the Biggest Opportunities for AI?
Here's what 22 executives who are familiar with AI said when we asked them, "What are the most common issues you see preventing companies from realizing the benefits of AI?"
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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, "What are the most common issues you see preventing companies from realizing the benefits of AI?"
Here's what they told us.
- Part of everyone’s day and life. Amazon Dots now collect all of the conversation you have. More pervasive with industrial IoT, connected cars, connected cities. A fusion of consumer-based use cases from mobile devices and speakers. The Echo Dot is the highest-selling speaker in the world. ...all with the objective of making people’s lives simpler and easier.
- There are a gazillion well-known fraud control marketing recommendations but there are also a gazillion cheap learning opportunities. When you have a new field, you have “easy” and “impossible” problems. The vast majority are easy, what’s left are the hard and then the impossible problems. We’ve lowered the tree so that we have a vast number of easy problems — AKA “low hanging fruit.” Reflected intelligence is more important and valuable than AI. The computer reflects the cleverness or actions of one or more people whereas AI must create intelligence. I worked on a music recognition engine breaking out three clusters of heavy metal music. Black metal, death metal, and heavy metal were not very different acoustically; however, were significantly different with regards to social interactions.
- Bots that are always learning and making people’s lives simpler and easier.
- Huge opportunities in development and integration for business processes and technology. Making smart robots, virtual agents, data analysis, and machine learning. A massive shift in how we live. Design buildings with less material, more resilience, less cost, and less environmental impact. Self-driving cars that are able to fix themselves.
- Massive for what’s on my network, who’s on my network, what devices they are accessing the network with, what apps the devices are running, whether or not those devices are secure and have the latest security updates and patches. This is very complex for a large company and AI can handle quickly and easily. Contextualize why the problem exists and what we can do to prevent it and mitigate any damage.
- Tremendous opportunities for disruption. Voice assistance. Move from a keyboard to voice interfaces. Ability to translate and work across geographies. Additional capabilities to explore data and make predictions with prescriptive analytics.
- While people are excited about Alexa, Echo, and autonomous cars, I think enterprise software (logs, security, password management, databases, performance monitoring) will see the most transformation now that all are in the cloud with a SaaS model. Data will be available from thousands of clients to which you can apply AI at a massive scale. Able to detect bots entering passwords and prevent fraudulent behavior.
- We are helping companies become software factories. Every industry is software driven and want to deploy faster using AI/ML to look at the data and pattern to tie to intelligent automation for self-service. Low/no touch versus humans to augment decision making. Personalize based on each user and team in the DevOps process.
- We see a shift to “conversational” user experiences as opposed to interactions using screens. Systems will have to leverage the power of AI to enable those conversations with users. Ultimately, we believe that digital assistants and chatbots will replace traditional and cumbersome websites, applications, and IVRs. Machine learning will also help prevent fraudulent transactions.
- The democratization of implementation. We enable SMEs (subject matter experts) to train and configure AI without data scientists or analysts.
- With technological advances in biometrics becoming a popular method of conducting business, AI will continue to evolve to protect sensitive information. Fingerprint and voice-enabled services are a common way for individuals and businesses to access information and data, which means that AI technologies will have to detect any new cyber threats that can side step these personalized methods.
- Deeper reasoning. Inductive reasoning with real-time context. Informational deductive reasoning. The holy grail is abductive reasoning from literature — ability to understand conditional expressions and abstractive abductive reasoning where we are able to recognize correlations.
- The opportunities are endless. Just beginning. AI/ML have the biggest opportunities in industrial IoT right now. That may change in a year.
- Tech road map domain expertise in data science algorithms, putting a face on AI that users (marketing or IT) can interact with.
- Engineering with equipment optimization. Make equipment produce more, run more smoothly. Able to show gains in the hundreds of millions of dollars. Sales and marketing with customer information, marketing automation, and insights on the customer journey.
- Any task with any combination of high data volumes, repetitive or mundane, labor intensive or time pressured is a prime target. These are likely to be regulatory driven (like ISDAs, MiFID II, or GDPR) or efficiency-driven (for example, due diligence) and can open up opportunities that were previously not viable using conventional approaches. Target verticals would include financial services (banking, insurance, asset management), legal and corporate legal, large consulting services organizations.
- Manufacturing, heavy equipment (trucking and logistics), utilities, and healthcare equipment.
- There are opportunities in every industry. We see the greatest opportunities in financial services, healthcare, manufacturing. In manufacturing, IoT and ML are used to predict failures so that companies can take action before the failure of a piece of equipment and reduce downtime.
Where do you see the greatest opportunities for 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 and 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
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