5 ways you can get started using AI(Machine Learning) for innovation.

“Computers will overtake humans with AI within the next 100 years. When that happens we need to make sure the computers have goals aligned with ours” — Stephen Hawking.

A recent report by World Economic Forum predicts around 7.1 million jobs would be replaced by artificial intelligence (AI) by 2020.

Another prediction by Forrester contends that the loss would be over 9.1 million jobs by 2025.

According to CBInsights, there is a sharp increase in global investments in AI from $282 million to over $3 Billion from 2011 to 2016.

In the above graph by Deloitte, we see that all major organizations have immense investments in AI, with Google/Alphabet leading the pack.

Although there are a number of enterprises utilizing AI, the good news is that AI is still in infancy and it’s entire capability is not tapped into.

A detailed report by McKinsey states the following:

“ AI adoption outside of the tech sector is at an early, often experimental stage. Few firms have deployed it at scale. In our survey of 3,000 AI-aware C-level executives, across 10 countries and 14 sectors, only 20 percent said they currently use any AI- related technology at scale or in a core part of their businesses. Many firms say they are uncertain of the business case or return on investment. A review of more than 160 use cases shows that AI was deployed commercially in only 12 percent of cases. ”

In this article I will present the fundamentals of AI and what you need to do in order to adopt AI in your organization.

1. Understanding AI

The most important technology with regards to AI is machine learning (ML), a machine’s ability to perform a task without human intervention and with incremental performance improvement. Examples range from Amazon’s recommendations for products that you might like to Facebook recognizing your friend in an image.

Machine Learning is fundamentally different from how typical software code is written. ML uses learning by examples. A machine is given millions of training inputs, for example, the inputs can be various images of animals: dog, cat, mouse and the outputs would be the correct labels of these images. Once the system learns these, it utilizes this information to correctly predict a new image it comes across.

Any business problem where you have a large amount of data and are trying to get an outcome is a good candidate for the application of machine learning.

Although AI is often envisioned as a humanoid-robot, this type of algorithmic AI is the most common use case in today’s world.

For example, JPMorgan introduced a new AI program that reviews commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of work each year by lawyers and loan officers. This is now reduced to a few seconds.

“We’re willing to invest to stay ahead of the curve, even if in the final analysis some of that money will go to product or a service that wasn’t needed,” Marianne Lake, the lender’s finance chief, told a conference audience in June. That’s “because we can’t wait to know what the outcome, the endgame, really looks like, because the environment is moving so fast.”

2. AI implementation strategy:

Everyone understands Apple’s Siri, Microsoft’s Cortana, Google’s OK Google, and Amazon’s Echo.

What we don’t understand is how we can utilize AI in our enterprises.

We grapple with questions like:

1. What are my options to pick the right machine learning technology?

2. How can I make AI technology investment that produces good returns?

3. When do I act to get ahead of competition?

While these questions are valid, they form the proverbial “putting the cart in front of the horse”.

Instead think about:

1. A business problem that needs to be solved that impacts top or bottom line.

2. What would the business process transformation look like?

3. Can emerging technologies like Machine Learning and AI solve my problem quickly and reliably?

In the same report by McKinsey, we notice 6 important characteristics of AI adoption.

3. Successful implementation factors:

Studies/reports from various sources suggest the top three success factors of AI implementations:

1. Open organization culture and change management

2. Business use cases

3. Data ecosystems and digital readiness

1. Organization Culture: Research reveals that leaders who use emerging technologies to create transformational business models drive successful AI adoption. They have a high sense of urgency and break the silos in their organization by an organization-wide adoption strategy. They empower the employees to learn and deliver these innovative solutions.

It is also important to understand that bolting AI solutions on top of legacy applications is detrimental to overall business goals. Having your digital platforms ready, adopting a digital transformation mindset and setting up easy integration points to various systems and applications would ensure your solution is robust, scalable and worth the investment.

2. Business Use Cases:

Current use cases are predominantly around data — customer insights and marketing leading the way.

An important point is that most of these applications are core to the business functions for example, customer service, operations and product development. Hence the early adopters of AI are seeing an increase in the revenue, grow their market-size and displace competitors.

AI adoption is led by High-tech, telecom and financial services where the complexity is high both geographically and operationally. They also have a high volume of data that can be utilized as a foundation.

3.Data systems and digital readiness

While AI is a broad term used for a variety of self-governing applications, I will focus on the most commonly used algorithm today — machine learning. The other key areas like robotics, autonomous vehicles, language and virtual agents are not considered in this article.

Machine learning can be used for recognizing complex patterns, synthesizing information and forecasting. Hence machine learning requires large data sets.

With the growth of data volume from various sources, most enterprises are challenged with clean and reliable data.

Imagine a scenario where the data comes from your Sales Orders, CRM systems, Social media profiles and customer service.

· This data from disparate systems should be centralized with the right technology architecture in place.

· You should have easy integration points in place so that these algorithms can seamlessly integrate with any application.

· Ensure that even if the new applications are outdated, they can be easily reconfigured or removed if necessary.

This is impossible and challenging if you are not part of the digital transformation journey.

4. Challenges in adopting AI:

Even though we have enough examples of enterprises adopting AI/ML, there are a lot of challenges for others who are either thinking of adopting or apprehensive about what it means to them.

Some of the challenges for adoption are:

1. Apprehensive of new technology: Business leaders need to understand that AI is here to stay and if you do not have a strategy in place, your competition is ahead of you.

2. Organizational change management: In most cases the challenge of transforming what people do is greater than the technical challenge of implementation of AI. Having an open and collaborative culture is the first step.

3. Talent availability in AI: Although you would need to invest in retrain/reskill your workforce, working with partners is beneficial in the short run.

5. The AI payoff:

Artificial intelligence adoption is a priority when used to solve a business problem that impacts revenue or efficiency. AI will continue to be used for disruptive innovation, competitive advantage and key differentiator of your products for organizations large and small. Hence, CEO and business leaders today have to consider the following when adopting AI in their organizations:

· Understand how AI can boost both your top and bottom lines

· Develop a clear AI adoption strategy built on solving business problems

· Align functional leaders in IT, Lines of Business (LOB) and digital to work together on AI initiatives

· Partner with vendors with appropriate capabilities and skills required to launch your AI solution.

· Empower employees to innovate, get trained and deliver around these latest technologies.

AI will definitely move in directions that are impossible to predict. Nonetheless those who jump on this train are the ones to get ahead of competition as well as have the first-mover advantage. Organizations that seize the opportunities that AI has to offer will thrive and displace those with a lower emphasis on adopting these latest technology.