The Big Leap Toward AI @ Scale

3.Preparing for AI @ Scale

Against that backdrop of strategy, priority setting, and piloting, let’s explore how companies can plan for a successful AI@scale transformation.

• Creating a Robust AI Architecture
• Structuring an Ecosystem
• Developing People, Skills, and Processes
• Designing Governance and Organizational Structures

Creating a Robust AI Architecture

IT is often treated as a standalone function that is brought in to implement decisions already made by business leaders.
This approach does not work with AI.If executives want to deploy machine intelligence to solve essential business problems, they must

• prioritize machine architecture equally to
• “people architecture” or
• traditional organizational issues.

Otherwise, the entanglement concerns described earlier will disrupt their plans.As companies move beyond the pilot phase, AI algorithms systemically interact with one another by learning from data, as explained earlier.Researchers at Google raised the entanglement issue several years ago, leading many prominent AI users to develop comprehensive machine-learning architectures.For example, Uber’s Michelangelo, in the words of the company, is “an end-to-end system that enables users across the company to easily build and operate machine learning systems at scale.” (See Exhibit 2.)

Likewise, a leading European online fashion retailer has built an AI platform that allows individual AI systems to work together and reuse components.

Vendors such as Amazon, Google, IBM, and Microsoft have all now launched or announced platform offerings to AI users.

Whether organizations buy one of these emerging platforms from a vendor or build their own, companies that want to deploy AI at scale need a rigorous and consistent framework and system to manage, document, and monitor workflow from data input to final action.

In addition to addressing issues such as entanglement and reusability, for example, companies need to monitor the security of these systems as well as the integrity and compliance of automated actions generated by AI.

Moreover, companies need to ensure that they have the storage, computing, and bandwidth to handle multiple AI engines and their timely actions.

Owing to its flexibility, the cloud is often a preferred option to address these needs. In industrial automation and Internet of Things networks, however, latency and bandwidth constraints may prevent the cloud from serving as a complete solution.

These settings require novel structures, such as edge computing, in which part of the processing power is kept closer to the action in the periphery.

Ultimately, the intelligent algorithms are a relatively small part at the core of the overall system.

Consequently, many companies expanding their AI operations need only a small number of data scientists and AI experts at the core but require a large number of data and systems engineers with AI experience to ensure the performance and resilience of the pipeline and peripheral systems.

For instance, with experienced data engineers and architects, a leading oil and gas company has built its own integrated-operations center, leveraging AI to enable predictive maintenance and optimize production. The projected efficiency gains amount to several billion dollars over the next few years.

Structuring an Ecosystem

The question is not if companies will work with vendors and partners but how.

• At least for the short term, it is their only way to access necessary AI talent and capabilities.
• Even companies with strong AI backgrounds rely to some extent on vendors.

With AI, however, these relationships could jeopardize core sources of competitive advantage for the companies.
Vendors need to train their AI tools, often using sensitive client data.

Companies can work with AI vendors in many ways,

• from outsourcing an entire process to buying selected services,
• seeking help in building in-house solutions, or training internal staff.

Executives should view these options in light of two questions:

• How valuable is the process or offering to our future success?
• How strong is our ownership, control, or access to high-quality, unique data, relative to the AI vendor’s?

By analyzing the AI landscape this way, companies will discover that their AI efforts land in one of four quadrants that each requires a different strategy:

• Commodities (low-value potential, low differentiated data access). Manage vendor relationships to reduce costs and improve performance in support functions and elsewhere.
• Danger Zones (high-value potential, low differentiated data access). Develop or acquire competitive data sources to turn danger zones into gold mines.
• Hidden Opportunities (low-value potential, high differentiated data access). Leverage technical expertise of vendors to generate quick wins and insights.
• Gold Mines (high-value potential, high differentiated data access). Capitalize on superior data access in strategically relevant areas and develop AI independently.

Beyond the critical issues of trust and confidentiality, the overall commercial offerings of vendors-including their newly launched platforms-differ widely in capabilities, integration abilities, and ease of use.

So companies need sufficiently solid AI capabilities to select and manage these relationships.
Many business leaders ultimately choose to hire an independent advisor to assess the tradeoffs while they build up the internal capability to bring work in-house.

Developing People, Skills, and Processes

Companies moving toward AI@scale should carefully consider how the transformation will affect their workforce through the creation of skills shortages, elimination of jobs, or both.

This complex topic requires intensive strategic workforce planning, retraining, and process re-engineering to accommodate a human-machine world.

The most immediate need is critical technical skills.

While many companies focus on hiring data scientists, they are encountering a greater shortage of people who have both business skills and an understanding of AI, as well as systems and data engineers, as mentioned earlier.

Longer term, companies need to understand the fundamental and nuanced shifts in the workplace that occur as humans and machines work side by side. (See Exhibit 3.)

When AI is focused on the augmentation of employees in their current roles, changes to the workforce and processes are relatively modest.

Indeed, the introduction of augmentation in many internal processes and service functions often improves employees’ experience.
In financial services, for example, the use of AI is reducing the burden, while enhancing the quality of many processes.

These changes become more extreme as processes start being reimagined for true human-machine collaboration.

In radiology,doctors and hospitals are already starting to prepare their processes and business models for a time when regulators accept machines as a second opinion-and ultimately a first opinion-for diagnostics, with a human in the loop to correct potential errors.

Finally, in what we call “hyperlearning,” machines are capable of teaching themselves in a virtual environment with little or no human intervention and without relying on massive data input from the real world.

As discussed earlier, GANs and digital twins are such environments.
It is conceivable that algorithms will not need human training or even real-world data in these environments in the near future.
This evolution has already occurred in the relatively restricted environment of chess but also, for instance, in flight simulators for drones and end-to-end supply chain optimization.

As companies move along this continuum, they will need to address new and novel change management and reskilling demands.

Renault has already embarked on this path with its digital transformation and with the creation of its Digital Hub, a large center dedicated to the development of digital products, training and innovation.

Designing Governance and Organizational Structures

Governance and organization of any major transformation are heavily dependent on context and goals.

But all AI@scale transformations require the general three-dimensional structure mentioned in the introduction. (See Exhibit 4.)

Corporate Center. Certain types of expertise and governance should be centralized. These include excellence hubs that house experts in many of the (horizontal) AI The Building Blocks of Artificial Intelligence -such as machine vision, natural-language processing, and some general frontier topics in machine learning-and support AI initiatives and vendor assessment throughout the company.

Data is the raw material of AI, but it also contains some of the company’s most sensitive information. A world-class data governance function, which sets data permissions across the organization, is critical in an AI world, to ensure both competitive advantage and regulatory compliance.

Finally, data architects, who manage global topics such as industrialization frameworks and platforms as well as vendors, should be centralized along with cybersecurity experts.

Business Units and Functions. As a general rule, cross-functional agile teams situated within business units or functions should be responsible for the development of AI-enabled processes, products, and services. These teams should include both AI experts and topic specialists. Roving “SWAT teams,” including HR experts and change specialists, should also be available to help decentralized units affected by AI use cases with implementation, training, and other issues.

Field Level. The employees responsible for managing the processes and actions modified by AI are, of course, decentralized, located close to the market, shop floor, or field. These units will need to understand the new tools and how they affect processes and skill requirements and will carry the principal burden of change. The SWAT teams will be available to bring these people up to speed on how to leverage the new technology and assist with the transformation.

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