Preparing for Artificial Intelligence and Machine Learning: What Every CDO and CAO Needs to Know

Artificial intelligence and machine learning is fast becoming part of many organizations and companies, often before they are ready for it or not. From automated manufacturing plants staffed entirely by robots to language processing systems that manage customer services, AI technology, which is driven by machine learning is already having a massive impact. This, together with the rise in innovation brought on by increasing computer processing power and the sheer volume of data created means that AI has an enormous potential to transform almost every industry – from banking to retail.

So what does this change mean for CDOs and CAOs? With AI being labelled “the most important general purpose technology of our era”, the effects of it will be huge in the next decade. Organizations are transforming many of their core processes and business models to bring artificial intelligence and machine learning into them.

While many in the C-suite are on board with the potential benefits of bringing artificial intelligence and machine learning into their organizations, they are still finding their feet when it comes to the practical applications of it. Many C-suite executives believe that artificial intelligence will enable their organizations to move into a new era of doing business and engaging with their customers, but several are still to incorporate it in the products, services and processes.

The Slow Road to Adopting Artificial Intelligence and Machine Learning

With their being so much hype and confusion around artificial intelligence and machine learning, it is not surprising that it has been slow to take off within many organizations. Most current investment in artificial intelligence and machine learning tends to come from research and development divisions from within large global corporate companies such as Google and Amazon, with the take up of artificial intelligence and machine learning still being in the pilot stages or discussion phase in many other organizations. For CDOs and CAOs who are already getting to grips with huge changes brought on by digital transformation and data, artificial intelligence and machine learning adds another complex layer that they need to integrate into their organizations and data dashboards.

Artificial Intelligence, Machine Learning & Data: A Marriage Made in Heaven

With the success of artificial intelligence and machine learning resting largely on data, there are often large gaps between CIO’s natural inclination to advocate the adoption of this new technology in their organizations and them delivering key messages about the benefits of it to CDOs and CAOs. While many in the C-suite are investing in artificial intelligence talent and have built strong information infrastructures, others lack specific analytics experience and easy access to their own company data.

When it comes to adopting artificial intelligence and machine learning, CDOs and CAOs might be wondering where to start. There are several key considerations that must be taken into account when assessing whether adopting artificial intelligence and machine learning is right for your organization:

Is it absolutely necessary?

With any new and innovative technology there is always “adoption for adoption’s sake”, and if your competitors are using it, keeping up with them is often one of the main reasons for adopting artificial intelligence and machine learning. This could then lead to key resources being diverted away from areas and projects that could potentially drive more growth. While machine learning and artificial intelligence is often extremely valuable to advancing the customer journey for your organization and delivering real insights into its performance, CDOs and CAOs should consider whether as a technology it will be valuable to them in the long run.

Who will take ownership?

Artificial intelligence and machine learning requires someone on the C-suite who is responsible for seeing its potential and believing in the projects in which it is implemented. This person must have a very strong understanding of the technology to utilize it to its best advantage. Ideally this person will have the openness, ability to change, vision and a strong understanding of the organization and how artificial intelligence and machine learning will fit in to it.

The natural choice for this role is the Chief Data Officer, Chief Analytics Officer and in some cases the Chief Information Officer. All these roles have a viable interest, but sometimes organizations create an entirely new position on the C-suite such as the Chief Artificial Intelligence Officer, especially if a large focus on artificial technologies is needed. This is often the case at larger organizations. A Chief Artificial Intelligence Officer can often fill several key functions to ensure that an organization is well placed to adopt these innovative technologies. Chief Data Officers and Chief Analytics Officers are also well placed to take a view across an organization and best understand how artificial intelligence and machine learning can shape each department. Chief Data Officers and Chief Analytics Officers can set out a roadmap that shows how artificial intelligence and machine learning will be implemented in line with the organizations overall strategy and develop more knowledge about how it works. This in turn will help with the selection of the correct technology.

Are your employees ready for artificial intelligence and machine learning?

All employees need to be empowered to how to develop and use artificial intelligence and machine learning, even if it is with a basic understanding that it will produce more accurate and better results and decisions than relying on one’s gut instinct alone. For the Chief Data Officer and Chief Analytics Officer this empowerment is not easy, with many employees citing “fear of change” as one of the key reasons why they are not keen to adopt this new technology. Often, this is due to a fear that it will make certain roles and jobs redundant, but if employees are taught to engage with machines early in the adoption process this fear will become obsolete as time passes. In time your employees will become used to working alongside machines, but one of the best things that an organization can do is to be completely open about how artificial intelligence and machine learning will be integrated, what that means for their jobs and provide training so that your employees can engage and interact fully with their new robot colleagues.

Do you have access to the right talent?

While artificial intelligence and machine learning implementation involves training your staff to a degree, it still requires that any employee working with it needs a background in data science or some experience with it as a technology. Unfortunately, these skills are quite scarce and a recent survey by job site Indeed found that from the period of June 2015 to June 2017 there was a 500% rise in the number of vacancy listings in the fields of artificial intelligence and machine learning. And of these listings, 61% were for machine learning engineers, 10% were for data scientists with specific artificial intelligence and machine learning skills and experience and just 3% were for software developers. In addition, more than 40% of respondents said that their enterprise IT staff don’t have the skills required to implement and support the introduction of artificial intelligence and machine learning.

All too often large tech organizations such as Google are sweeping up much of the available talent in this area and many are being poached directly from academia, which is further widening the skills gap. Organizations need to be aware of this and ensure that more talent flows into the pipeline, and that they make themselves an attractive company to work for with competitive salaries and interesting challenges and projects.

Are Your Data Efforts Strong Enough?

To train your algorithms, you need lots and lots of data. Not only do you need lots of data, but it also needs to be of excellent quality. Those organizations whose data is lacking in both areas will not be successful in their artificial intelligence and machine learning efforts. Everything must be collected, and this data must be thoroughly cleansed.  Any mistakes or problems in this process will dilute any results and render any insights found untrustworthy. Ultimately it can lead to wrong solutions, potentially unethical artificial intelligence and machine learning results and bad decision making. This is a process that few organizations are equipped for.

Security is also a key consideration, with organizations needing to be aware of the many regulations that can hold them back from using sensitive or personal data to train their algorithms, particularly in industries such as healthcare and finance.

Conclusion

If implemented correctly artificial learning and machine learning offers a huge range of benefits to organizations which can result in intelligent recommendations or the complete automation of time-consuming repetitive tasks. All of this provides a wealth of information within organizations that Chief Data Officers and Chief Analytics offers can take advantage of, enabling them to make suggestions and recommendations that will boost the productivity of their customer and user base.
To Book to Attend our CDAO Canada Event and to Find out More

During our CDAO Canada event from June 5th to June 6th 2018 you will be able to learn more about utilizing artificial intelligence and machine learning for best results within your organization and preparing your organization for artificial intelligence and machine learning.

To find out more and to book please visit https://coriniumintelligence.com/chiefdataanalyticsofficercanada/.

* MIT Researchers Erik Brynjolfosson and Andrew McAfee

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