Please make understand AI in simpler terms for CFO, CEO, CHRO, COO and other c - level executives with a non-technical background.

Artificial Intelligence (AI) involves using computers to do things that traditionally require human intelligence. This means creating algorithms to classify, analyze, and draw predictions from data. It also involves acting on data, learning from new data, and improving over time. Just like a tiny human child growing up into a (sometimes) smarter human adult. And like humans, AI is not perfect. Yet.

The difference between AI and regular programing? Regular programs define all possible scenarios and only operate within those defined scenarios. AI ‘trains’ a program for a specific task and allows it to explore and improve on its own. A good AI ‘figures out’ what to do when met with unfamiliar situations. Microsoft Word cannot improve on its own, but facial recognition software can get better at recognizing faces the longer it runs.

It's a peak time for Artificial Intelligence to be regulated in the organization. What do you think should be an ideal approach for c level executives to introduce it. What should be done to minimize the risk? Please share your experience on how you get Artificial intelligence introduced and regulated in your organization. 

Let’s look at the broad meaning of the terms ‘Risk’ and ‘Compliance’. Risk management is predicting and managing risks that could hinder the organization from reliably achieving its objectives under uncertainty. Compliance refers to adhering with the mandated boundaries (laws and regulations) and voluntary boundaries (company's policies, procedures, etc.)

Regulating AI can be tricky. The trouble is that regulations designed to breathe life into the AI dream could in fact do the opposite if not approached with care and due diligence. 

With AI predicted to be beneficial across a wide spectrum of different applications, it would make more sense for the applications themselves to be regulated. The application requirements for AI in healthcare is different from banking, for example – the ethical, legal and economic issues around issuing medication to patients is far different from the transaction of money!

On the other hand, regulating AI as a whole will mean that its use will be more limited in certain industries over others, which means that there would be barely any point in businesses implementing automation or AI into their business models.

The important considerations should be ‘the level of human involvement’ in the AI solution as well as the decisions made by AI should be by and large transparent, fair and explainable. There-in lies the balance.

What all traditional CEOs should know about Artificial Intelligence in order to revolutionize the business model including technologies, human assets? What machine skills they must equip yourself to get involved as well informed CEO's involved in AI.

The center piece for reaping the investments in AI is data. AI algorithms are trained using large datasets so that they can identify patterns, make predictions and recommend actions, much like a human would, just faster and better. Multiple AI technologies such as machine learning, deep learning, neural networks drive a variety of AI Applications. For the CEO, the investment should be about leveraging AI to impact the bottom lines tangibly. As an example, Netflix applies machine learning to your viewing history to personalize the movie TV show recommendations you see. Netflix also analyzes what you and people with similar preferences watched in the past, and even auto generates personalized thumbnails and artwork for movie titles, to entice you to click on a title that you’d otherwise ignore.

All to ensure that you stay glued to the screen while your brain melts. 

A policy framework is required to strike a balance between ethics and operations. Where actually ethics get compromised. What c - level executives should do at a strategic level to see those ethics do not get compromised.

C-level executives, including CTO and the CEO, will play an essential role in determining the way a company uses its AI campaign. One litmus test CEOs can run to determine the ethics in their AI usage is to find out whether they would feel comfortable if the way they use AI would be made open for everyone to see.

The whole C-suite, including the stakeholders and managers, should be involved in the process for setting up an ethical framework. It is necessary that you mark ethics as an important part of your AI strategy, and treat the two as the same. Your AI strategies for ethics should revolve around having diversity in your team with a skilled workforce and transparency in your data usage. You should easily be able to tell the world about your use of AI in the organization. 

Secondly, you must test all training models before implementation to identify all potential biases in them. Your training models need to be tested and regulated before widespread implementation across the organization so that biases are limited and irregularities are made scarce. 

Data and AI governance can significantly help set the tone for your AI campaign, as it will ensure proper data collection and storage. Your data will be perfect for you to work on. 

How organizations new to AI must start introducing AI. How organizations must determine that this is the time that they must go for AI. What can be the elements of an efficient change management program involving people and processes in order to deploy AI and preparing teams to work differently?

To make sure investments in artificial intelligence (AI) pay off, it’s important to pair AI deployments with organizational change management programs. As enterprises increasingly adopt AI technology, managing the transformation should be top of mind. AI technology brings the promise of productivity, engagement and next-level efficiency. But as with any sort of workflow change, stakeholders will need to have a complete understanding of the changes in order to continue being productive. 

Few pointers to keep in mind here are:-

Education Is the Key to Adoption - Organizations that experience the smoothest transitions start education early and tailor it to multiple stakeholder groups before anything is in place. Provide stakeholders with baseline knowledge of the value that AI technology will offer to ensure that they have clear expectations when it comes to working with the technology. It’s important that people throughout the organization understand the benefits of adopting AI. That includes everyone from administrative personnel to key business stakeholders, not just by those who will work closely with the new systems. Maximizing the value of AI means using the technology to augment what the human workforce does. Therefore, education should center around the fact that the technology will enhance employees’ daily lives, making it easier to handle routine tasks so they can focus on more valuable pursuits. When they communicate with employees, leaders must focus on explaining why the change is being made instead of emphasizing the features of the technology.

Create a Road Map for Deployments - Creating a clear AI adoption plan can help avoid bumps along the way. With a road map for the project, employees can get accustomed to each change as it happens, allowing for a step-by-step adoption rather than an all-at-once approach. It’s a good idea to plan a slower rollout in which the more tech-savvy stakeholders try the technology first and become its strongest champions. Those early adopters could encourage people in other departments to accept the technology with open minds and a positive outlook. For example, if the IT team is enthusiastic about an AI-powered virtual assistant, executives in the C suite might be more receptive to the idea of allowing the organization to pilot the product.

Training and Education Are Ongoing Endeavors - Change management doesn’t stop once a new technology is deployed. Confidence in a new technology will grow when the stakeholders involved not only know how to use it, but also find it valuable in completing their daily tasks. An approach in which education and planning are paired with training is the best way to support an enterprise wide change.

If a company pours a lot of time, money and other resources into an AI deployment but then doesn’t offer proper training on the new system, it won’t see much of a return on its investments and the promises of improvements in efficiency and productivity will be unfulfilled. For example, if a company offers members of its finance team training on a new AI-powered payroll system but neglects to teach rank-and-file employees how to use it, employees might become confused as payroll concerns arise. Where are the W-2 forms? When do we receive our paychecks? When this happens, employees may turn to the IT department and overload the help desk with questions.

It is critical then, for leaders to spend time creating an ongoing training plan as part of the post-rollout road map. Concrete training goals will help stakeholders grow comfortable with the technology on a step-by-step basis and minimize any productivity losses.