How the AI-to-Data Continuum is Forever Changing Work
How the AI-to-Data Continuum is Forever Changing Work
The upheaval brought on by COVID-19 makes previous shifts seem glacial in comparison. Yet, perhaps no change was as sudden as the need to minimize human contact.
Consumer and business behaviors changed virtually overnight, remote work was preferred (if not required), and digital spending became the new normal.
Customers today aren’t just buying convenience goods online; they’re shopping for expensive electronics, getting cars delivered to their doors, and buying houses without ever stepping outside.
Above and Beyond
Advanced technologies will revolutionize business during the next few years as enterprises navigate this change, renew their focus on talent, and address a host of new challenges.
One of the key levers to address this new reality is the use of AI, automation, and big data.
By tapping into these innovations, businesses can forever change the way they work, and how customers work and engage with them.
For example, AI can analyze huge volumes of data, predict what’s likely to happen next, and put the next best action in motion.
Automation can take over manual processes to free up human personnel for more complex, value-creating work. When combined, these technologies create a connected intelligence fabric for the organization that can differentiate it from the competition.
Free for All
Such solutions, implemented properly, can empower companies to free themselves from former constraints. Therefore, to ensure advanced tech lives up to its potential, enterprises should follow these four steps for their technology-adoption initiatives.
1. Rely on a Holistic Top-Down Strategy
AI, automation, and big data are often viewed as disparate technologies, and at many companies, attempts to implement them are stuck in individual silos. For example, an organization might automate a few internal processes, use big data to personalize some of its marketing outreach, and construct a chatbot using AI to help alleviate customer service burdens.
These pieces are all useful and can inch the business forward, but they’re all built to meet narrow objectives.
A holistic approach centered on organizational transformation is where the magic happens.
Look at Amazon, and you’ll see that AI is squarely able to increase sales, provide superior digital experiences, and remain operationally agile. For example, the natural language processing behind Alexa devices makes voice ordering easier.
Amazon’s recommendation engines suggest products that consumers most want or need. Its forecasting capabilities help power the company’s one-day delivery feature that offers consumers nearly instant gratification. Instead of serving piecemeal individual functions, AI, data, and automation are being used across the company in service of a sweeping goal.
2. Implement Measurable Solutions
Solutions built around AI, data, and automation must be measurable to determine their business value.
Organizations should be able to measure the progress of their intelligence journey.
At the same time, the measurement framework should come into play prior to implementation so companies have an idea where an initiative will have the greatest impact. Leveraging AI and big data technologies can be expensive, so the ability to accurately measure business value should be a strategic imperative.
3. Drive Culture and Change Management
Organizational leaders are often eager to implement technological solutions, viewing them as a way to make problems go away using nothing but an injection of capital. But, unfortunately, that attitude overlooks the behavioral changes that these solutions often require both for customers and employees.
Employees need to start thinking in new ways about things like working with virtual agents, leveraging digital labor and bots, security, data accessibility, and privacy.
Certain challenges can’t simply be engineered away. New behaviors and human touch are just as essential as technology to successful digital and enterprise transformations.
4. Prioritize Security, so Trust is Deserved
It’s critical to ensure that data is adequately protected, but there are also legitimate concerns about data usage.
AI tools are commonly referred to as “black boxes” because while people can see inputs and outputs, they often have little knowledge of exactly how those outputs are generated.
This lack of clarity means AI engines need clear mechanisms to prevent biased results and be explainable. In addition, AI engines need to be designed to be trusted. When these safeguards are in place, it can give users confidence that the systems are trustworthy and working to benefit the larger consumer base.
The Time is Now
Advanced technologies like AI, big data, and automation are fundamentally transforming the approach to work, but only a few organizations are tapping into these innovations in a holistic way. The rest of the pack will need to catch up quickly to avoid being left behind.
The four steps above will help kick-start integration efforts that produce maximum business value.
Image Credit: hitesh choudhary; unsplash; thank you!
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