Blog posts by Anne Bailey
We already hear a lot about artificial intelligence (AI) systems being able to automate repetitive tasks. But AI is such a large term that encompasses many types of very different technologies. What type of solutions are really able to do this?
Robotic Process Automation (RPA) configures software to mimic human actions on a graphic user interface (GUI) to carry out a business process. For example, an RPA system could open a relevant email, extract information from an attached invoice, and input it in an internal billing system. Although modern RPA solutions are already relying on various AI-powered technologies like image recognition to perform their functions, positioning RPA within the spectrum of AI-powered tools is still somewhat premature: on its own, RPA is basically just an alternative to scripting for non-technical users.
Enterprises that are currently beginning with automating prescribed tasks hope to adopt more advanced capabilities like data-based analytics, machine learning, and ending with cognitive decision making; they should however realize that existing RPA solutions might not yet be intelligent enough for such aspirations.
Filling in the Gaps
If RPA sounds limited, then you are correct; it is not a one-stop-shop for intelligent automation. RPA only automates the button clicks of a multi-step process across multiple programs. If you’re under the impression that RPA can deliver end-to-end process automation, pause and reassess. RPA can do a limited and explicitly defined set of tasks well, but faces serious limitation when flexibility is required.
As soon as any deviation from the defined process is needed, RPA cannot and does not function. However, it can be part of a larger business process orchestration that operates from an understanding of what must be done instead of how. RPA delivers some value in isolation, but much more is possible when coordinated with other AI systems.
The weaknesses of RPA systems overlap nicely with the potential that machine learning (ML)-based AI can offer. ML happens to be capable of adding flexibility to a process based on data inputs. Solutions are coming available that learn from each situation – unlike RPA – and produce interchangeable steps so that the system can assess the type of issue to be solved, and build the correct process to handle it from the repository of already learned steps. It widens the spectrum of actions that an RPA system can make.
Synchronization Adds Value
AI does have strengths that overlap with RPA weaknesses like handling unstructured data. An AI-enabled RPA system can process unstructured data from multiple channels (email, document, web) in order to input information later in the RPA process. The analytics functionality of ML can add value to an RPA process, such as identifying images of a defective product in a customer complaint email and downloading them to the appropriate file. There are aspects that the pairing of RPA and AI do not solve, such as end-to-end process automation, or understanding context (at least not yet).
Overall, RPA’s value to a process increases when used in combination with other relevant AI tools.
Identity and Access Management (IAM) is on the cusp of a new era: that of the Identity Fabric. An Identity Fabric is a new logical infrastructure that acts as a platform to provide and orchestrate separate IAM services in a cohesive way. Identity Fabrics help the enterprise meet the current expanded needs of IAM, like integrating many different identities quickly and securely, allow BYOID, enable accessibility regardless of geographic location or device, link identity to relationship, and more.
The unique aspect of Identity Fabrics is the many interlinking connections between IAM services and front- and back-end systems. Application Programming Interfaces (APIs) are the secure access points to the Identity Fabric, and can make or break it. APIs are defined interfaces that can be used to call a service and get a defined result, and have become a far more critical tool than simply for the benefit of developers.
Because APIs are now the main form of communication and delivery of services in an Identity Fabric, they – by default – become the security gatekeeper. With an API facilitating each interface between aspects of the fabric, it is potentially a weakness.
API security should be comprehensive, serving the key areas of an Identity Fabric. These include:
- Directory Services, one or more authoritative sources managing data on identities of humans, devices, things, etc. at large scale
- Identity Management, i.e. the Identity Lifecycle Management capabilities required for setting up user accounts in target systems, including SaaS applications; this also covers Identity Relationship Management, which is essential for digital services where the relationship of humans, devices, and things must be managed
- Identity Governance, supporting access requests, approvals, and reviews
- Access Management, covering the key element of an Identity Fabric, which is authenticating the users and providing them access to target applications; this includes authentication and authorization, and builds specifically on support for standards around authentication and Identity Federation
- Analytics, i.e. understanding the user behavior and inputs from a variety of sources to control access and mitigate risks
- IoT Support, with the ability of managing and accessing IoT devices, specifically for Consumer IoT – from health trackers in health insurance business cases to connected vehicles or traffic control systems for smart traffic and smart cities
API security is developing as a market space in its own right, and it is recommended that enterprises that are moving towards the Identity Fabric model of IAM be up to date on API security management. The recent Leadership Compass on API Management and Security has the most up-to-date information on the API market, critical to addressing the new era of identity.
Regulation has the uncomfortable task of limiting untapped potential. I was surprised when I recently received the advice to think of life like a box. “The walls of this box are all the rules you should follow. But inside the box, you have perfect freedom.” Stunned as I was at the irony of having complete freedom to think inside the box, those at the forefront of AI development and implementation are faced with the irony of limiting projects with undefined potential.
Although Artificial General Intelligence – the ability of a machine to intuitively react to situations that it has not been trained to handle in an intelligent, human way – is still unrealized, narrow AI that enables applications to independently complete a specified task is becoming a more accepted addition to a business’ digital toolkit. Regulations that address AI are built on preexisting principles, primarily data privacy and protection against discrimination. They deal with the known risks that come with AI development. In 2018, biometric data was added to the European GDPR framework to require extra protection. In both the US and Europe, proposals are currently being discussed to monitor AI systems for algorithmic bias and govern facial recognition use by public and private actors. Before implementing any AI tool, companies should be familiar with the national laws for the region in which they operate.
These regulations have a limited scope, and in order to address the future unknown risks that AI development will pose, a handful of policy groups have published guidelines that attempt to set a model for responsible AI development.
The major bodies of work include:
- The Montreal Declaration for Responsible Development of AI from the University of Montreal and Fonds de Recherche du Quebec (published December 2018)
- Guidelines on Artificial Intelligence and Data Protection from the Council of Europe (published January 2019)
- Ethics Guidelines on Trustworthy AI from The EU Commission (published April 2019)
- The OECD Principles on AI from the OECD (published May 2019)
The principles developed by each body are largely similar. The main principles that all guidelines discussed address the need for developers and AI implementers to protect human autonomy, obey the rule of law, prevent harm and promote inclusive growth, maintain fairness, develop robust, prudent, and secure technology, and ensure transparency.
The single outstanding feature is that only one document provides measurable and immediately implementable action. The EU Commission included an assessment for developers and corporate AI implementors to conduct to ensure that AI applications become and remain trustworthy. The assessment is currently in a pilot phase and will be updated in January 2020 to reflect the comments from businesses and developers. The other guidelines offer compatible principles but are general enough to allow any of the public, private, or individual stakeholders interacting with AI to deflect responsibility.
This collection of guidelines from the international community are not legally binding restrictions, but are porous barriers that allow sufficiently cautious and responsible innovations to grow and expand as the trustworthiness of AI increases. The challenge in creating regulations for an intensely innovative industry is to build in flexibility and the ability to mitigate unknown risks without compromising the artistic license. These guidelines attempt to set an ethical example to follow, but it is essential to use tools like the EU Commission’s assessment tool which establish an appropriate responsibility, no matter the status as developer, implementor, or user.
Alongside the caution from governing bodies comes a clear that AI development can bring significant economic, social, and environmental growth. The US issued an executive order in February 2019 to prioritize AI R&D projects, while the EU takes a more cautiously optimistic approach by building of recognizing the opportunities but prioritizing building and maintaining a uniform EU strategy for AI adoption.
If you liked this text, feel free to browse our Artificial Intelligence focus area for more related content.
Thanks to an incessant desire to remove repetitive tasks from our to-do lists, researchers and companies are developing AI solutions to HR – namely to streamline recruiting, improve the employee experience, and to assess performance.
AI driven HR management will look different in small businesses than in large companies and multinationals. There are different barriers that will have to be navigated, but also different priorities and opportunities that small businesses will have with AI.
Smaller budgets create price barriers to implementing an AI system, and likely psychological barriers as the self-built CEO resists delegating tasks that would otherwise rely on his or her gut instinct. Access to a sufficient quantity of data to optimize algorithms is perhaps the largest challenge that small businesses will face when integrating AI into their HR practices. Companies typically gather data from their own databases, assembling a wide range of hiring documents, employee evaluations, etcetera. Large companies have decades of stored HR data from thousands of employees, and clearly have an advantage when it comes to gathering a large volume of usable data.
In terms of priorities, there is a huge divide between the value proposition that AI offers to large and small businesses. Big companies need to leverage time-saving aspects, especially to create a customized connection for thousands of employees. Routine communication, building employee engagement, and monitoring employee attrition are all aspects that minimize repetitive work and save time. In a sense, the goal is to give institutional bureaucracy a personal touch – like a small business has. A small company’s strengths come from its unique organizational culture, which is heavily dependent on natural, human interaction and well-designed teams. It is this “small company” feel that large companies try to imitate with AI customization features.
Of course, small companies also need to save time, especially because many do not have a dedicated HR department – in some cases, the department consists of one person dividing time between their main role and HR tasks. Their time is limited, so instead of implementing FAQ chatbots that make the organization feel small and accessible, small businesses should focus on another area which consumes too much time: recruiting and promoting visibility.
Finding qualified and competitive candidates is challenging when a firm’s circle of influence is geographically limited. A factor often contributing to success in small firms is the ability to hire for organizational fit, thus building tightly knit teams to deliver agile service. To increase the chances of attracting highly qualified candidates, small businesses should focus on using AI systems to support recruiting and hiring for organizational fit.
Small businesses are always under pressure to do more with less. When implementation costs are high and internal resources limited, small businesses can consider plug and play tools which rely on external datasets. For those who are open to experiment, they can look for AI projects that have overlap with their goals. For example, socially minded companies looking to attract more diverse applicants can participate in studies like AI-enabled refugee resettlement, placing people in areas where they will be most likely to find employment. A project like this could shift setup costs for implementing new technology and achieve wider HR goals that the company may have, like gaining employees with specific skills that are not common in the area, opening up more opportunities for innovation through diversity, gaining different language capabilities, and so on.
The risk of using AI technologies to support hiring has already played out in the case of Amazon. With the best intentions, the research team designing a hiring tool to select the highest qualified candidates based on their resumes noticed that their algorithm had learned to value traits that indicated the candidate was male, and penalize indicators that the candidate was female. The cause was imbedded in their input data: the CVs and associated data given to the system to learn from was influenced by years of gendered hiring practices. The project was quietly put to rest. This example was luckily only a pilot version and wasn’t the deciding factor in any applications, but provides a valuable lesson to developers and adopters of recruitment AI: maintaining transparency throughout development and beyond will illuminate weaknesses with time. Robust checks by outside parties will be necessary, because one’s own biases are most difficult to see.
AI can have a role to play in small business HR strategies just as much as the large corporations. But as with any strategy, the decision should be aimed at delivering clear advantages with a plan to mitigate any risks.
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AI for the Future of your Business: Effective, Safe, Secure & Ethical Everything we admire, love, need to survive, and that brings us further in creating a better future with a human face is and will be a result of intelligence. Synthesizing and amplifying our human intelligence have therefore the potential of leading us into a new era of prosperity like we have not seen before, if we succeed keeping AI Safe, Secure and Ethical. Since the very beginning of industrialization, and even before, we have been striving at structuring our work in a way that it becomes accessible for [...]