Artificial Intelligence and Machine Learning Part 15

© DataMites™ | Feb 2019 | CII – Confederation of Indian Industry | AI Transforming Business
Artificial Intelligence and
Machine Learning
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Organizational challenges around technology, processes, and
people can slow or impede AI adoption
• Organizations planning to adopt significant deep learning efforts will need to consider a spectrum of options about how to do
so. The range of options includes building a complete in-house AI capability either gradually in an organic way or more rapidly
through acquisitions, outsourcing these capabilities, or leveraging AI-as-a-service offerings.
• Given the importance of data, it is vital for organizations to develop strategies for the creation and/or acquisition of training
data. But the effective application of AI also requires organizations to address other key data challenges, including establishing
effective data governance, defining ontologies, data engineering around the “pipes” from data sources, managing models over
time, building the data pipes from AI insights to either human or machine actions, and managing regulatory constraints.
• Given the significant computational requirements of deep learning, some organizations will maintain their own data centers,
because of regulations or security concerns, but the capital expenditures could be considerable, particularly when using
specialized hardware. Cloud vendors offer another option.
• Process can also become an impediment to successful adoption unless organizations are digitally mature. On the technical side,
organizations will have to develop robust data maintenance and governance processes, and implement modern software
disciplines such as Agile and DevOps. Even more challenging, in terms of scale, is overcoming the “last mile” problem of making
sure the superior insights provided by AI are instantiated in the behavior of the people and processes of an enterprise.
• On the people front, much of the construction and optimization of deep neural networks remains something of an art requiring
real experts to deliver step-change performance increases. Demand for these skills far outstrips supply at present; according to
some estimates fewer than 10,000 people have the skills necessary to tackle serious AI problems and competition for them is
fierce amongst the tech giants.19 Companies wanting to build their own AI solutions will need to consider whether they have
the capacity to attract and retain these specialized skills.

• Ten things to solve for
• In the search for appropriate measures and policies to address these challenges, we should not seek to roll back or slow diffusion of the technologies. Companies and governments should harness
automation and AI to benefit from the enhanced performance and productivity contributions as well as the societal benefits. These technologies will create the economic surpluses that will help
societies manage workforce transitions. Rather, the focus should be on ways to ensure that the workforce transitions are as smooth as possible. This is likely to require actionable and scalable
solutions in several key areas:
• Ensuring robust economic and productivity growth. Strong growth is not the magic answer for all the challenges posed by automation, but it is a prerequisite for job growth and increasing prosperity.
Productivity growth is a key contributor to economic growth. Therefore, unlocking investment and demand, as well as embracing automation for its productivity contributions, is critical.
• Fostering business dynamism. Entrepreneurship and more rapid new business formation will not only boost productivity, but also drive job creation. A vibrant environment for small businesses as well
as a competitive environment for large business fosters business dynamism and, with it, job growth. Accelerating the rate of new business formation and the growth and competitiveness of
businesses, large and small, will require simpler and evolved regulations, tax and other incentives.
• Evolving education systems and learning for a changed workplace. Policy makers working with education providers (traditional and nontraditional) and employers themselves could do more to
improve basic STEM skills through the school systems and improved on-the-job training. A new emphasis is needed on creativity, critical and systems thinking, and adaptive and life-long learning.
There will need to be solutions at scale.
• Investing in human capital. Reversing the trend of low, and in some countries, declining public investment in worker training is critical. Through tax benefits and other incentives, policy makers can
encourage companies to invest in human capital, including job creation, learning and capability building, and wage growth, similar to incentives for private sector to invest in other types of capital
including R&D.
• Improving labor-market dynamism. Information signals that enable matching of workers to work, credentialing, could all work better in most economies. Digital platforms can also help match people
with jobs and restore vibrancy to the labor market. When more people change jobs, even within a company, evidence suggests that wages rise. As more varieties of work and income-earning
opportunities emerge including the gig economy, we will need to solve for issues such as portability of benefits, worker classification, and wage variability.
• Redesigning work. Workflow design and workspace design will need to adapt to a new era in which people work more closely with machines. This is both an opportunity and a challenge, in terms of
creating a safe and productive environment. Organizations are changing too, as work becomes more collaborative and companies seek to become increasingly agile and nonhierarchical.
• Rethinking incomes. If automation (full or partial) does result in a significant reduction in employment and/or greater pressure on wages, some ideas such as conditional transfers, support for mobility,
universal basic income, and adapted social safety nets could be considered and tested. The key will be to find solutions that are economically viable and incorporate the multiple roles that work plays
for workers, including providing not only income, but also meaning, purpose, and dignity.
• Rethinking transition support and safety nets for workers affected. As work evolves at higher rates of change between sectors, locations, activities, and skill requirements, many workers will need
assistance adjusting. Many best practice approaches to transition safety nets are available, and should be adopted and adapted, while new approaches should be considered and tested.
• Investing in drivers of demand for work. Governments will need to consider stepping up investments that are beneficial in their own right and will also contribute to demand for work (for example,
infrastructure, climate-change adaptation). These types of jobs, from construction to rewiring buildings and installing solar panels, are often middle-wage jobs, those most affected by automation.
• Embracing AI and automation safely. Even as we capture the productivity benefits of these rapidly evolving technologies, we need to actively guard against the risks and mitigate any dangers. The use
of data must always take into account concerns including data security, privacy, malicious use, and potential issues of bias, issues that policy makers, tech and other firms, and individuals will need to
find effective ways to address.

Artificial Intelligence in Business Gets Real
• BCG and MIT Sloan Management Review launched their second annual report on
AI in business, based on a survey of more than 3000 enterprises in 29 industries
and 126 countries. Some of the key findings are that:
• The 18% companies which are classified as pioneers based on their knowledge
and activity level, are pulling ahead of others on many dimensions
• Their overwhelming emphasis is on revenue enhancing rather than cost reducing
applications
• Pioneers are most aggressive in their efforts to scale AI beyond spot applications
to enterprise level impact
• Conversely, some of the barriers which hold other companies include leadership
support, technological capabilities and lack of an articulated business case.
• Interestingly the majority of all companies see AI triggering modifications to their
business models in the next 5 years and more than 90% predict that AI will drive
new business value over the same period, spread across most functional areas of
business.

• Pulling Together the Transformation Program
• An AI@scale transformation should occur through a series of top-down and bottom-up actions to create alignment,
buy-in, and follow-through. This ensures the successful industrialization of AI across companies and their value
chains:
• AI Ambition and Maturity Assessment. This top-down step establishes the overall context of the transformation and
helps prevent the company from pursuing disconnected AI pilots. The maturity assessment is typically based on a
combination of surveys and interviews.
• Evaluation of AI Initiatives and the Operating Model. This bottom-up step provides a baseline of current AI
initiatives. It should include goals, business cases, accountabilities, work streams, and milestones in addition to an
analysis of data management, algorithms, performance metrics, and cybersecurity. A review of the current AI
operating model should also be conducted at this stage.
• Priority Setting and Gap Analysis. The next top-down step prioritizes AI initiatives, focusing on easy wins and
unicorns. This step also identifies the required changes to the operating model.
• Outline of AI@scale Transformation Program. This top-down step consists of both the transformation roadmap,
including the order of initiatives to be rolled out, and the creation of a program management office to oversee the
transformation.
• Detailed Implementation Planning of AI@scale Program. The last step covers implementation, detailing the work
streams, responsibilities, targets, milestones, and resources.

Content will be created using AI
Zone Out | June 2018
A sci-fi short film written and directed by Benjamin, an AI.

It is beyond any doubt that
AI are likely to have a major impact on
work and productivity over the next decade.

We live in Exciting Times
Era of Augmenting Human Intelligence

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Artificial Intelligence and Machine Learning Part 15

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