The turmoil brought on by COVID-19 makes the above changes seem glacial in comparison. However, perhaps no change was as sudden as the need to minimize human contact.
Consumer and business behaviors changed virtually overnight, remote working was preferred (if not necessary), and digital spending became the new normal.
Advanced technologies will revolutionize business for years to come as companies 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 artificial intelligence, automation, and big data.
By leveraging these innovations, companies can forever change the way they work and the way customers work and engage with them.
For example, AI can analyze large volumes of data, predict what is likely to happen next, and trigger the next best action.
Automation can take over manual processes to free up human staff for more complex and 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
These solutions, properly implemented, can allow companies to break free from the above limitations. Therefore, to ensure that advanced technology lives up to its potential, companies must follow these four steps for their technology adoption initiatives.
1. Rely on a comprehensive top-down strategy
Artificial intelligence, automation, and big data are often seen as disparate technologies, and in many companies, attempts to implement them are stuck in individual silos. For example, an organization can automate some internal processes, use big data to customize part of its marketing reach, and build a chatbot using AI to help ease customer service burdens.
All of these pieces are helpful and can drive your business forward, but they are all designed to meet narrow goals.
A holistic approach focused on organizational transformation is where the magic happens.
Take a look at Amazon and you will see that artificial intelligence is capable of increasing sales, providing superior digital experiences, and remaining operationally agile. For example, the natural language processing behind Alexa devices makes voice ordering easier.
Amazon’s recommendation engines suggest products that consumers want or need the most. Its forecasting capabilities help drive the company’s one-day delivery function offering consumers near-instant gratification. Rather than serving individual functions piecemeal, artificial intelligence, data, and automation are used throughout the enterprise in service of an overall goal.
2. Implement measurable solutions
Solutions built around artificial intelligence, 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 must come into play before implementation so that companies have an idea of where an initiative will have the greatest impact. Leveraging artificial intelligence and big data technologies can be expensive, so the ability to accurately measure business value should be a strategic imperative.
3. Promote culture and change management
Organizational leaders are often eager to implement technology solutions, seeing them as a way to make problems go away using nothing more than an injection of capital. But, unfortunately, that attitude overlooks the behavioral changes that these solutions often require for both customers and employees.
Employees must start thinking in new ways about things like working with virtual agents, leveraging digital workforce and bots, security, data accessibility, and privacy.
Certain challenges cannot simply be eliminated. New behaviors and the human touch are as essential as technology to successful business and digital transformations.
4. Make safety a priority, so you deserve the trust
Ensuring that data is adequately protected is critical, but there are also legitimate concerns about data use.
Artificial intelligence tools are commonly referred to as “black boxes” because, while people can see the inputs and outputs, they often have little knowledge of exactly how those outputs are generated.
This lack of clarity means that AI engines need clear mechanisms to avoid biased results and to be explainable. Additionally, AI engines must be designed to be reliable. When these security measures are implemented, users can trust that the systems are reliable and work to benefit a broader consumer base.
The time is now
Advanced technologies such as artificial intelligence, big data, and automation are fundamentally transforming the approach to work, but only a few organizations are leveraging these innovations holistically. The rest of the herd will need to catch up quickly to avoid being left behind.
The four steps above will help drive integration efforts that produce maximum business value.
Image credit: hitesh choudhary; unpack thank you!