AI And Automation In The Cloud -- Seizing The Moment For Digital Transformation

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Offline Abdus Sattar

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AI And Automation In The Cloud -- Seizing The Moment For Digital Transformation
Kristof Kloeckner
Aug 23, 2018,9:00 am

Kristof Kloeckner

CTO and General Manager, Technology, Innovation and Automation for IBM Global Technology Services (retired).


There can be little doubt that digital disruption is upon us on a large scale. Entire industries and their business models, as well as society at large are being transformed by the impact of digital technologies. To understand risks and opportunities, we need to look at how the following three forces are coming together to turn the winds of change into a perfect storm.

• An abundance of data providing raw material for insights

• Artificial intelligence (AI) and analytics turning data into actionable insights, enabling automation and augmenting human intelligence

• Cloud as a fundamental change to service delivery and service consumption, as well as an enabler of communities

Let’s look at data and AI first. Data has often been called the new oil, and more recently, AI is being compared to the new electricity. These are apt comparisons that describe how data is turned into insights that power business processes. We can distinguish three broad areas of application:

• Gaining insights from data to optimize the execution of business processes, be it through automation or through improved human decision making

• Improving the knowledge life cycle by curating knowledge and providing advisory tools, enabling subject matter experts to do their jobs better, faster and with greater consistency

• Enabling better, more human-like user interfaces to services, especially through natural language conversations. A major part of this is divining human intent and directing appropriate responses, including enabling self-service

These three areas are connected and can support each other. If handled badly, they compound problems. Currently, automation is most successful when applied to low-level tasks that need to be executed at speed and with consistency, like incident response and problem resolution in IT services. At this level, automation improves overall service levels while also allowing humans to focus on more complex situations. Advisory tools, on the other hand, clearly augment human intelligence, but their underlying algorithms warrant scrutiny to avoid bias. There is an ongoing important debate over how to reconcile the need for transparency and accountability with the desire to achieve a competitive advantage, and an increasing research focus on AI that explains itself. Finally, user interface technology like chatbots is making great strides, but in many situations, people still prefer to talk to humans, and chatbots need to know when to hand over to a person.

AI is clearly playing a strong role in the evolution of the nature of work, and in the relationships between enterprises, their employees and their customers and clients. Success will ultimately depend on making all affected groups stakeholders in the transformation. For instance, expert communities need to see benefits for themselves in contributing their knowledge and experience opportunities for professional growth. Clients need to be sure of their ownership of their data. Enterprises need to acquire or build the skills (like data science) to successfully implement AI and to carefully manage expectations. Society needs to see ethical questions of applying AI addressed, which is increasingly reflected in research agendas.

It is very encouraging to see communities and organizations springing up that address aspects of these challenges like OpenAI, foster shared progress, for instance through Kaggle’s competitions or provide practical advice like Andrew Ng’s Machine Learning Yearning. In fact, much of the acceleration of technological advances is due to the wide sharing of code and best practices on the cloud and through digital communities. Availability of AI services and developer toolkits on commercial clouds like Google, Microsoft, IBM or Amazon will also speed up adoption. However, the ease of acquiring core technology puts an added responsibility on the implementers of AI solutions to follow best practices and be mindful of potential pitfalls. Otherwise, naïve and faulty implementations will lead to a backlash against the use of AI and imperil its benefits.

Let’s now have a look at the role of the cloud in digital transformation. We have already seen an indication of the role of clouds as platforms for the sharing of technology and best practices in the example of AI communities and AI cloud services. In fact, even though platform as a service is still relatively small compared to infrastructure as a service and software as a service, it increasingly drives the competitive landscape among the major cloud providers, as the speed of innovation becomes as important as savings, if not more so. Interestingly, data services are at the forefront of this dynamic.

The ability to quickly compose services from existing underlying core services is a major attractor and differentiator for clouds. New capabilities (like data or AI services) are available first on clouds, and clouds drive standardization of what services are being adopted and how. This standardization enables automation, and automation is again a prerequisite for manageability at scale and industrial strength service delivery. It is therefore not surprising that major cloud providers are emphasizing their management and automation capabilities, including managed cloud services.

Enterprise use of clouds is now mainstream, which includes a variety of deployment models and multicloud deployments in hybrid clouds. Needs for cloud integration and transportability of cloud solutions give rise to rapid adoption of container-based approaches. IBM’s Kubernetes-based Cloud Private and Google’s announcement of an on-premise version of the Google Kubernetes Engine are examples of this.

Standardization and automation in the cloud are also a great enabler for an accelerated service delivery life cycle through DevOps., including DevOps for AI. This will, in turn, drive faster business cycles.

We have seen that cloud is the underpinning for delivering the data and AI services that fuel digital transformation, and increasingly, cloud growth itself is fueled by data, AI and the ability to optimize business processes. This is a self-reinforcing process whose speed will be determined by the availability of skills in data science and DevOps, but also the ability of all stakeholders to come to an agreement about the intended benefits and the management of risks of the digital transformation.

Abdus Sattar
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Offline enamul17

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Thanks for sharing!

Offline 710001888

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Good write-up.

Offline Farzana Akter

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--Sincerely yours

Farzana Akter
Lecturer, Department of Computer Science and engineering
Daffodil International University