The Innovation Ultimatum. Steve Brown

where to develop your voice agents: in-house, using third-party frameworks, or on existing voice platforms. Google, Amazon, Apple, and other tech giants enable developers to create apps for their voice services. Google calls these apps “actions” while Amazon calls them “skills.” The make-versus-buy decision will vary by brand. Most will build a presence on the major tech platforms. For competitive or security reasons, some brands may elect to build a dedicated voice agent that is embedded inside their apps and websites. Banks may prefer their own voice agents for security reasons. Bank of America's Erica virtual assistant is a good example.

The future of voice may become more open. Today's main voice platforms, Google Assistant and Amazon Alexa, resemble AOL and Compuserve, the walled garden online services of old. The rise of the internet, with open standard web pages and browsers, broke down the walls of the online services and opened up the online world to anyone who wanted to participate. Efforts like the Open Voice Network seek to build standards for online voice akin to HTML for webpages and enable brands to build their own voice capabilities. Time will tell if this will fell the initial duopoly of the tech giants.

      Understand and Mitigate for Bias

      When AIs are trained with data that represents examples of human behavior, they learn unwanted human biases that are reflected in that data.

      Amazon receives a small mountain of job applications each week. To triage resumes, Amazon built an experimental AI to surface candidates worthy of an interview. There was only one problem. According to a report by Reuters, it quickly became clear that Amazon's AI was biased against women. The problem with the AI had nothing to do with the way it was coded. The AI reflected historical bias in Amazon's hiring process. The unconscious bias of hiring managers had been codified in the recommendations of the AI, which learned to favor male candidates. Amazon canceled the project before it was ever deployed and reviewed its hiring practices. Sometimes AI holds a mirror up to our own humanity.

We should hold our AIs to even higher standards than we hold ourselves, and should strive to create AIs that don't reflect negative human bias. The state of New Jersey built an AI as part of their plan to do away with the bail bonds system. Cash bail has been a part of the U.S. court system for centuries but is criticized for penalizing poorer defendants. According to a 2013 study by the Drug Policy Alliance of New Jersey, 75% of the New Jersey jail population were people awaiting trial. The average wait time for trial was 314 days. Almost 40% of the people awaiting trial were there because they couldn't afford $2,500 or less in bail. Under New Jersey law, everyone must be given bail, no matter the crime, so rich people always avoided jail and bail bondsmen made good money. In January 2017, the state of New Jersey replaced their bail system with an AI that created a public safety assessment (PSA) for each defendant. The PSA was used as a guide by judges. The PSA predicts the chance defendants will commit crime while awaiting trial and whether they will appear for their court date. The new system reduced the number of people in New Jersey jails awaiting trial by 30%. The AI was trained with information about 1.5 million previous defendants in 300 jurisdictions. Race and gender information was deliberately removed from the training data. Developers also removed all information on the name, education level, employment, income, and home address of defendants. Any of these data can be a proxy for race and gender and thus might disadvantage some demographic groups. The way we train our AIs matters. We should expect our AIs to operate ethically, fairly, and without bias. Leaders must ensure that their teams work hard to strip bias from all the AIs they build.

      Gather Data Today to Feed Your AIs of the Future

      AIs have a voracious appetite for data. Build data pipelines to source the data today that you will need to train the AIs of the future. Your AI strategy informs your data strategy. Your data strategy informs your sensor strategy, your partnership strategy, your hiring strategy, and your IT strategy. Figure out what data you will need and where you will get it from. Think of this as another strategic “make versus buy” decision.

      Data storage is relatively cheap. You never know what operational, customer, market, and other insights tomorrow's AI might find hidden inside today's data. It goes without saying that every business should adhere to all relevant data privacy and data retention laws. Wherever possible, businesses should gather and archive as much data as possible, particularly data that comes from machine operations, sensors, and market research. Today's data is tomorrow's AI fuel.

      Develop New Products and Services

Use AI to spark innovation across your entire product line. Enhance and simplify human interfaces using voice and super sensors. Create new value and build products that intelligently manage and maintain themselves. Use generative design to accelerate research, push the boundaries of product design, and speed service delivery. Marry human talent with collaborative AIs to build new service offerings at new price points.

      Final Thoughts

      At the beginning of the twentieth century, every business had to grapple with electrification. In the late twentieth century, every business had to understand and embrace digital technology. To remain competitive, every business must now build a comprehensive strategy that embraces artificial intelligence in every aspect of their operations. No exceptions. No delay.