AI: Automating, Accelerating and Improving Decision-Making

Amr Awadallah, Cloudera

Amr Awadallah is the Chief Technology Officer of Cloudera. Before co-founding Cloudera in 2008, Amr (@awadallah) was an Entrepreneur-in-Residence at Accel Partners. Prior to joining Accel he served as Vice President of Product Intelligence Engineering at Yahoo!, and ran one of the very first organizations to use Hadoop for data analysis and business intelligence.

Amr joined Yahoo after they acquired his first startup, VivaSmart, in July of 2000.

Amr holds a Bachelor’s and Master’s degrees in Electrical Engineering from Cairo University, Egypt, and a Doctorate in Electrical Engineering from Stanford University. Capgemini’s Digital Transformation Institute spoke to Amr to understand how organizations can benefit in their decision-making using AI.

How would you define AI and what level of take-up are you seeing?

I prefer to see AI as the automation of decision-making as opposed to ‘intelligence’. It’s about learning about multiple areas and then making decisions like a human does, but in a much quicker, faster, more reliable and more accurate manner. So, it is really about the automation of decisions.

We have more than 1,000 customers worldwide. But I would say only a handful are truly undertaking compelling AI implementations and automating decisions in a unique way. Most companies are still just figuring out how to collect, sort and catalogue their data.

Make sure there’s a problem that AI can solve

Do you think organizations understand AI’s potential?

I don’t. Many organizations are very excited by the buzz but don’t really understand what it means. It is important to understand the limitations of what can and cannot be done with AI. A significant reason for the confusion is the name ‘intelligence’, because it is not really about intelligence. It is actually about process and decisions. It is about learning how certain processes and decisions work within the organization and then automating them.

What does it take for organizations to deliver benefits from AI and make a success of their programs?

The first imperative is making sure you have the proper systems and the right skills. The second is ensuring that you identify the initial use cases that can benefit from AI, as opposed to just adopting AI for the fun of adopting AI. You need to have a very well-defined use case or a very well-defined problem that AI can help solve.

The benefits are there, but realizing them requires hard work

What differentiates the companies that benefit from AI?

What differentiates them is the first-mover advantage. Those organizations that are reaping the benefits today started thinking about AI up to eight years ago. They began building the proper foundations from a data platform perspective. They started training their people in the skills required. Now, eight years later, they are at the point where they are truly able to leverage AI.

There is a kind of basic hierarchy of needs or processes that organizations need to go through. For example, they need to be able to collect data and different types of data across their organization, including real-world data. So, having more sensors and technologies that bring in that data from the real world is the top priority. Number two is processing that data. Being able to manage, process and clean it up at scale is still something that most organizations struggle to do. Then you start with basic analytics, gradually moving to advanced analytics and then AI. The last step is the most advanced, which is to automate. Depending on the organization, this stage can take anywhere from two years if they are very fast or as much as eight years.

What is holding organizations back from embracing AI?

It is a combination of two things – a proper data infrastructure and skills. The automation of decisions is dependent on having massive amounts of data that you use to train the algorithms and undertake the automation. But most organizations don’t have very good hygiene levels when it comes to data management. In terms of skills, the skills that most organizations have are very focused on business intelligence, but it’s a discipline that’s very reactive in nature. Here, you are automating reports that a human needs to read to make a decision. That is the very different skill set of automating decisions, which requires training to bring people up to speed.

What should large organizations do about skills?

One thing is clear – large organizations are not going to be able to just hire people who are good at AI, because they are very hard to find. The best thing to do is to train your existing people. Another way of working around the skills challenge is to develop an AI ecosystem, where a wider spectrum of companies can tap into the power of AI.

Every decision is ripe for AI

Are there specific sectors where you believe AI will see greater adoption?

We are seeing across-the-board adoption. Typically, you have more agile, innovation-ready like finance and telecoms, but we are seeing appetite in many sectors. We are seeing it in power, smart grids and utilities. We are seeing it in manufacturing. We are seeing it in shipping, for companies that manage big fleets of ships across the world. Multiple sectors are very receptive and understand the implications of AI.

Are there particular business functions that are more suited to AI?

Any function where humans make repeatable decisions can be impacted by AI. It is applicable to a lawyer reviewing a contract to a doctor making a diagnosis. Another area is prediction. For example, predicting ahead of time the failure of a piece of equipment, such as an elevator or power generator, by analyzing the patterns of data emerging. Predictive analytics and maintenance is a very common underlying theme and gets classified under the AI umbrella. Another function is anomaly detection – trying to detect weird behavior or patterns within an organization. For example, a bank trying to detect patterns to counter money laundering.

The future for AI

How long do you think it will take for AI to become mainstream?

In the enterprise software arena, as opposed to consumer electronics, everything takes time to mature. It is not going to be like smartphone adoption. Based on historical experience, I would say five more years.

What is your view on the impact of AI on jobs?

In my view, AI is not replacing jobs. It is replacing decisions that certain jobs make. Take the example of lawyers I provided earlier. You will require less lawyers, but you will still require lawyers. We will need lawyers to review the exceptions – the challenging cases that the AI system cannot automate. Any new technology introduces new levels of efficiency and we as humans have to adapt. This was true with the industrial revolution. When we created the steam engine and the electric engine, a lot of people had to re-train. There will be a lot of efficiencies that will have a significant impact on many jobs, but jobs are not being completely eliminated.

What are some of the skills that people will need to survive in the AI age?

Firstly, skills around maintaining, operating, training and overseeing AI systems to ensure they are working correctly.  The other important skill that will be required across all sorts of jobs would be the willingness to learn new skills. That is because expectations will change significantly as machines start to take on more and more decisions from humans.

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