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Artificial intelligence (AI) offers an incredible opportunity for enterprise organizations to streamline operations, improve the customer journey, implement new user interfaces and generally build a smarter, faster business. But, as we’ve seen in recent years, the rapid dash to integrate these new technologies is creating major liabilities that can have substantial negative impacts on businesses that aren’t prepared.
Before investing heavily in new systems, a few questions need to be answered: How accountable, compliant and traceable is the data that your organization’s AI collects and analyzes? Data misuse, non-compliance with new privacy regulations in Europe and California and over-reliance on third parties are all issues that must be addressed. Many companies have already learned the hard way what can happen when they are not.
I am the CEO and co-founder of an enterprise software company called Bouquet.ai that delivers an AI-chatbot through natural language processing and the co-founder of Squid Solutions, which provides usage analytics to publishers around the world. Below, I would like to share my top considerations for keeping data safe and building trust as you implement AI into your organization.
How do you ensure accuracy in AI?
For AI to be an effective addition to your enterprise, it needs to be accurate. Inaccuracy can create more barriers to efficiency and damage trust far more than if there were no automation solution in place at all.
It’s easy to think that AI has reached a level where it can be implemented to replace previously manual functions across the enterprise. But technology has a way to go yet. In fact, more than half of studies analyzed in a recent review of academic research on AI show an inability to replicate results. Training data used in machine learning algorithms, for example, is often not made available for replication, and many of the most interesting studies that have hit the headlines may not be replicable under different circumstances. In short, AI can do some amazing things, but not every time.
This is why AI implementation is such a careful process in so many organizations. If you plan to integrate chatbots or conversational voice UI for customer service and marketing efforts, you need to ensure accuracy in natural language processing to avoid frustrating, inefficient experiences. If you are using AI for sales and business analytics, the underlying data needs to be as accurate as possible to avoid issues with how AI interacts with that data. These are all common issues that, if not addressed, will lead to trust issues both internally and externally.
Ensure privacy for the data collected.
Another major concern when implementing AI in your enterprise is data security. If you are collecting and working with customer data, it’s imperative that you have systems in place to protect and stay compliant in the management of that data. There are several things to keep in mind here.
1. Keep source data local: The more data is moved, the more likely it is to be at risk of outside access. Keep all of your source data local, and keep as much processing local as possible. Don’t send to various online services to supplement your internal deployment.
2. Identify and classify data: Determine the level of confidentiality data has and classify accordingly. More than half of data breaches occur internally, so a strong data access model is needed to ensure high-value data remains safe.
3. Ensure compliance: Review all legal measures with which your company must be compliant based on where you do business and what data you will collect. The General Data Protection Regulation in Europe, new privacy regulations in California and new regulations being discussed in other large target markets should all be taken into consideration.
Data privacy is a major undertaking. There are dozens of steps to establish, map and implement processes that will protect data according to how it is being used. If your organization is preparing to leverage AI to better utilize and even monetize data, spend time focusing on privacy first.
Build trust with your customers.
Trust is the cornerstone of everything you do. Once lost, it can take years and large sums of money to regain even a fraction of the trust you once held from customers. That’s why the implementation of a large-scale AI system into your company needs to be handled carefully.
We’ve discussed the two major factors in building and maintaining trust: accuracy and privacy. To freely give you data and be comfortable in how you plan to use it, customers need to know that:
1. You won’t lose that data or otherwise allow it to be used for anything other than what they’ve agreed to.
2. The interactions they have with your AI-assisted systems will be accurate and frictionless.
Just look at the impact of the Cambridge Analytica scandal on Facebook. A third-party SaaS service accessed and misused the personal data of millions of Facebook users for the purposes of influencing the 2016 U.S. Presidential Election. The result was a massive blow to Facebook and the trust the public had in a company that stores an immeasurable volume of data from its users.
On a smaller scale, businesses continue to struggle with inaccurate chatbots that make it difficult to get an answer, and nearly impossible to reach a human agent. These situations can drive away customers for good if they interfere with the experience significantly enough. Trust is lost, and so is business.
Data safety and system accuracy are vital.
For AI to be a useful tool that improves the user experience, streamlines operations and improves return on investment, it needs to be accurate, safe and built in such a way that the data collected is protected. Any organization preparing to implement a new solution must take the time to ensure all three of these things are done and that the enterprise is ready for such a drastic shift in both policy and operations.
January 2, 2019 at 07:46AM
Forbes – Entrepreneurs