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 keys to a successful AI strategy?"
Here's what they told us:
Identify the Business Problem
Have the Necessary Data
- Watson for the enterprise. Metrics and metadata. Non-domain expertise. Anomaly detection, great dataflow, and databases. Foundation for real-time data processing. Domain expertise.
- Don’t be afraid of exploring your data. Get all of your data together in a data lake. Linking data adds value to data. Think of graphs connecting entities and associations. Come up with hypotheses. Ask and answer questions.
- Make software smarter and easier to use. Enable AI application development. Provide digital mesh to embed AI from the core to the edge. This enables DevOps to inject AI into the infrastructure for real-time analysis. Ability to do smart ingestion, quick data wrangling, shifting, pivoting, splitting, and shifting. Look for columns that are similar and need to be aggregated. As you are doing smart data wrangling you have a record of everything you’ve done. Ability to get recommendations. Suggested recommendation to use AI behind the scenes to help users. Smart ways of provisioning nodes in clusters. Identify resources to tasks. Allow injection of ML into AI on the edge can create apps for embedding or calling with REST interfaces.
- Be clear on the business outcomes you want to achieve. Have the right data and a sufficient amount. How do you know what’s sufficient? We employ NLP to the data and the queries to determine if we have a good chance of success. Data scientists, ML experts — we use independent software vendors as needed for our clients. We have an ecosystem of partners. Projects are experimental by nature and may fail. There are no guarantees of success but by testing you learn a lot more than you knew going in.
- Take a vertically-oriented approach and customize to the domain. Use different elements of AI based on the problem you are trying to solve: Attributes of the product category with NLP (i.e. three-fourths sleeve, V-neck, crew-neck, boot-cut); Once you have the attributes understand what drove the consumer purchase (three-fourths sleeve, brand preference, fabric preference) learn the relative importance of attributes with ML. As you understand consumer preferences you are able to present content based on the attributes preferred by the consumer. Different features and attributes are ranked based on consumer importance.
Have the Right Tools
- Having the right tools. Knowing what you want to accomplish. Having specific goals. Starting simple. Having the right data to accomplish your goals. Machine learning can learn to live with dirty data. For example, acoustic data varies based on if it’s coming from a mic, versus a phone, versus something with a lot of background noise. AI/ML has to deal with real data and real data is dirty.
- Problems are the same but the tools and the skills are different. What AI solution you use depends on the nature of the problem — gigantic data set to mine; build an autonomous vehicle; everything in between. The right tool and technology depends on what you are trying to accomplish. Think about how the application of technology provides a competitive advantage. Know what technology to use when then how to effectively use the technology without having a Ph.D. in statistics. Know how to leverage your domain knowledge. You cannot apply one solution to solve every problem. Empower developers with abstractions, tools, and platforms to solve problems with unique solutions. Understand how different versions of AI can be applied to different problem types.
Keep Your Models Current
- Do an assessment to figure out where AI can be beneficial. Identify what steps your most tedious and time-consuming processes are. Identify where you have the greatest risk exposure for failing to fulfill compliance issues. Determine how you will implement the resources you have. Continuity — continuous management to deal with all of the changes, evolution of skillsets, and new technologies. You must commit to ongoing support of your AI initiative or it will fall apart quickly given how fast everything is changing.
- It’s important to focus on constantly improving the infrastructure that powers AI. This will lead to more accurate and faster results. Having software run efficiently is a key benefit. We prune 95 percent of unnecessary processing threads so there is less data to analyze. We average results of about 99 percent on detection rates (vs. 80 percent amongst the competition).
What are your keys to a successful AI strategy?
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