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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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Ten lessons from Deloitte for successfully embracing artificial intelligence

Feb 6, 2018 | 6603 Views

Despite its early forms proving increasingly popular among businesses looking to digitise their operations and cut costs, Artificial Intelligence is still in its infancy, and while the majority of organisations are exploring the benefits of the technology, those that are actually implementing it commonly struggle with its successful execution. Building on the firm's front running position in the global AI market, Deloitte's Artificial Intelligence Center of Expertise (AICE) has crafted the top ten lessons for successfully embracing AI within a company.

1. Ensure the support of the leadership and that AI is embedded in the strategy
The establishment of an AI organisation can be seen as an organisational change, and as with more or less all changes, it is important that the change is supported by the leadership and that a clear vision is formulated. The leadership of an organisation must back the establishment of an AI organisation and be prepared to fund it and give it sufficient time. For instance, to manage the AI community and to experiment with this new technology, it is important that the leadership understands the usefulness and necessity of this investment. Leaders should always be aware of what business needs AI can fulfil, and how the technology will influence the organisation as a whole.

Speaking at a recent round-table discussion on how AI might change the partnership model, Deloitte UK's Director of AI & Cognitive Computing, Matthew Howard, argued consultants are essential for keeping AI implementation grounded in such a way. Howard said, "Consultants have a key role to play in fulfilling the demands of the market as part of the AI ecosystem - marrying business needs with technology," before suggesting that consultants could soon have such an important role to play in assisting business leaders in imbedding AI in their organisations, that, "perhaps we will see the first data scientist partnerships soon."

2. Create general awareness of AI among the employees
Even for technology-driven organisational changes, it is just as important to focus on the engagement of human labour. Getting employees to accept and participate in AI implementation is key for successful adoption, and to do this, it is important that executives are aware of AI and also understand how it will affect their organisation. With AI, this is difficult, because, as its potential is still to be realised, it is intangible.

One way around this, as seen within Deloitte, is for leaders to develop an awareness campaign to help employees become aware of AI. Deloitte did this through the firm's AI ??AIME' co-worker. The self-learning machine known as AIME gently introduced the firm's employees to the idea of AI by interacting with them in the workplace. This aroused their curiosity, and encouraged staff to voluntarily sign up for ??AI for dummies sessions' to learn more about the subject.

3. Get AI experts on board, starting with people with a technical background
Before AICE, numerous people within Deloitte were already working with AI across many different parts of the organisation. One problem the company did encounter as a result of this was that the people in one business unit were sometimes unaware of the activities that were taking place in another business unit. Therefore, the first step was to identify what AI developments had already taken place in the organisation and to bring together all of the people who had a technical AI focus.

Starting this by involving people with a technical background has various advantages. They are often ??early adopters' and therefore know more about the subject, and so they already know what is (and importantly what is not) possible and they are often more driven by content and less by commerce. This means that they can quickly find common ground with respect to content, meaning that departmental boundaries vanish. This is important when building an organisation-wide community.

Deloitte therefore appointed a technical AI leader within every business unit. When selecting the correct people, intrinsic motivation is more important than job level. However, we asked people in the more senior positions to think about who would be the right person in their unit. It is important, in addition to intrinsic motivation, for the chosen people to have an understanding of AI, and that, for instance, they have included it in their annual plan so that they want to and are able to devote time to the subject.

4. Make choices and set clear objectives
Together with the chosen AI Tech leaders, in a number of sessions, Deloitte then drew up a joint annual plan. This is something which is important to bring all of the different ambitions into line and to make choices. When making choices it is good to take (technical) feasibility into account.

When drawing up the annual plan, it is wise to include the ??Low-hanging fruit' at the start of the process to deliver easy wins and help build momentum. For instance, by communicating what is already happening in the area of AI or by achieving quick successes, confidence in such a programme can be increased, in the team and among the organisation's leadership.

5. Build the AI community but be selective
Soon after gaining a picture of the technical community, Deloitte received requests from people with more of a ??business focus' to become part of the AI community. In every business unit, the firm appointed an AI business leader, and the organisation found that involving people with a business focus, in addition to - and after appointing - the technical people, also has various advantages.

Technical employees may often focus on their technical solution, but there is a possibility that if it goes too far there is a risk that something is developed that is not based on an actual business problem (??develop for the sake of developing'). Staff with a business focus are in close contact with the customer and therefore they can identify the problems actually faced by the organisations. By involving both groups, organisations can create a situation that allows the optimum solution to be offered to the customer - although it is important to involve both groups differently in the AI organisation. What interests the Tech community is often uninteresting for the business community and vice versa. Deloitte therefore made the conscious decision to cooperate on some activities (for instance some community events) and to keep other activities separate (for instance training sessions).

Notably, Deloitte deployed this balancing act via the development of a new Artificial Intelligence (AI) based method, which can produce strategic market analysis and benchmark reports in the blink of an eye. DeloitteSmartReports.com can browse hundreds of thousands of datasets rapidly, to tailor-make quality reports at a fraction of the cost of a human consultant.

According to Ewout Bolhuis, a Director at Deloitte in the Netherlands, the development of this service was based around customer need, avoiding development for development's sake, while understanding the limitations of AI. "We have automated the entire process. The customer can order, pay and receive the report without the need for a human being," he explained, adding, "These reports are the perfect addition to our current range, because they allow companies to see in which areas their rivals are innovating in."

In order to achieve this balanced workforce, for people with a Business focus, Deloitte made it a condition that they should focus on AI in their daily work, while Deloitte did not make people who only had an interest in the theme members of the community. Instead, this group of employees were encouraged to attend the ??AI for dummies sessions' that would give them a basic knowledge of AI. Deloitte also included their IT Department and Risk & Reputation Office in the organisation, allowing them to optimally facilitate the community (for instance when buying software) and also identify and rapidly address data and privacy issues.

This method of classification resulted in the following groups:

  • Employees with AI tech knowledge
  • Employees with AI business knowledge
  • Other employees

6. Outline clear expectations in the organisation and be realistic
AI is not a panacea. In particular in the first stage of any AI program, it is crucially important to manage the expectations concerning AI. In general, within organisations there are two misconceptions about AI:

1. that AI is the solution to all problems

2. that the organisation is already able to do everything with AI

It is important that both the leadership and the sales people understand the current possibilities of AI. This prevents awkward situations arising, for instance that things are promised that cannot yet be realised. To be able to finally succeed as an organisation, in this initial phase the emphasis must be on building a community, gaining experience and experimenting. Therefore, it is possible that in this first phase sales are limited or absent, that less is possible than envisaged and that mistakes will be made when experimenting. The organisation's leadership must have the courage to invest, in spite of possible setbacks, and to maintain a long-term vision.

7. Ensure technological support and appoint a programme manager from a ??neutral' department
It is important that the time and money that is invested in the programme is also used to facilitate the community in the area of ICT. Organisations should for instance consider establishing a data platform, a code repository (GIT) for sharing code or configuring collaboration tools, as well as appointing a programme manager.

As indicated previously, Deloitte is made up of various business units. In spite of the fact that all of the units belong to the same organisation, business units can have conscious and unconscious preferences that hamper knowledge sharing. Because the Innovation department serves a general, company-wide interest, it is easier for that arm to take on a connecting role in the process.

8. List the AI competencies
When leaders have identified the community, it is important to investigate which competencies organisations have available and at which level. This can give a clear picture of which business units are progressing well or lagging behind, which AI skills are missing and where the experts can be found.

The result of this analysis forms the basis for, among other things, the AI training plan (what training, to whom, when), and the classification of community members in workflows (business, technology) and level (potentials, experts).

9. Offer a safe learning environment
Many people are unfamiliar with AI. It is also a very wide domain. It is very possible that people have, for instance, experience with classification problems, but have never done anything with Natural Language Processing. As a result, there is a high chance that community members do not yet know or understand some aspects. It is essential to ask questions, and companies should record what has been learned and share it, so that others do not make the same mistakes.

It is realistic to assume that mistakes will be made in the initial phase, as staff will still be growing familiar with many concepts they are expected to engage with. This makes it important to think about risk avoidance, but it is also important to think about how an organisation might identify any compliance or security breaches as fast and effectively as possible and correct them. Involving the Risk & Reputation office (as illustrated by 5.) can help here.

10. Communication
Deloitte's final point is for organisations to communicate a lot and frequently. Groups should make clear to the organisation what has been done in the area of AI, within the programme, and repeat the message. They should not assume that the knowledge will be known immediately, and should always work to specify short-term successes and long-term progress.

According to Marjolein Vlaming, Program Manager of Deloitte's Artificial Intelligence Center of Expertise, "Communication at both the organisational and personal level. Enter into a dialogue with colleagues. It concerns a complex subject. Time must be allocated for explanations and, where required, clarification. Discuss things not only with the ??AI enthusiasts' but also with the sceptics. This will help organisaions to learn about and respond to what is going on."

Encouraging organisations to begin work as soon as possible, she concluded, "AI is currently a hot theme in many organisations, in both the board room and among the employees. This is the moment to start to introduce AI into an organisation!"

Deloitte's Artificial Intelligence Center of Expertise from which the lessons have been sourced is based in Amsterdam, the Netherlands.

Source: Consultancy
Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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