An extensive summary
Title Article: Artificial Intelligence for the Real World
Authors: Thomas H. Davenport, Rajeev Ronanki
Harvard Business Review, January - february 2018., pages 108-116
Cognitive Technologies are increasingly being used to solve organization problems, but many of the most ambitious AI projects encounter setbacks or fail.
Organizations should take an incremental rather than a transformative approach and focus on augmenting rather than replacing human capabilities.
To get the most out of AI, firms must understand which Technologies perform what type of tasks, create and prioritized portfolio of projects based on organization’s needs, and develop a plan to scale up across the organization.
There are various categories of AI being employed and a framework is provided for organizations to build up their AI capabilities. AI can support three important organization needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees:
This means: using robotic process automation (RPA) technologies. RPA is the least expensive and easiest to implement of the cognitive Technologies. Think on robots (that is code on a server) that act like a human inputting and consuming information. Tasks include:
transferring data from e-mail and call center systems into systems of record – for example, updating customer files or service additions;
reconciling failures to charge to charge for services across billing systems by extracting information from multiple document types; and
“reading legal and contractual documents to extract provisions using natural language processing
This is using algorithms to detect patterns in vast volumes of data. Think of it as “analytics on steroids.” These machine-learning applications are being used to:
predict what a particular customer is likely to buy;
- identify credit fraud in real time and detect Insurance claims fraud;
analyze warranty data to identify safety or quality problems in automobiles and other manufactured products;
automate personal targeting of digital ads; and
provide insurers with more accurate and detailed actuarial modeling
Cognitive insights provided by machine learning differ from those available from traditional analytics in three ways:
- They are usually much more data-intensive and detailed;
- The models typically are trained o some part of the data set;
And the models get better – that is their ability to use new data to make predictions or put things into categories improves over time.
Versions of machine learning (deep learning in particular, which attempts to mimic the activity in the Human brain in order to recognize patterns) can perform feats such as recognizing images and speech. Machines learning can also available new data for better analytics and identify probablistic matches. Cognitive insight applications are typically used to improve performance on jobs only machines can do.
Projects that engage employees and customers using natural language processing chatbots, intelligent agents, and machine learning. This category includes:
intelligent agents that offer 24/7 customer service adressing a broad and growing array of issues from password request to technical support questions – all in the customer’s natural language;
internal sites for answering employee questions on topics including IT, employee benefits, and HR policy;
product and service recommendation systems for retailers that increase personalization, engagement, and sales – typically including rich language or images; and
health treatment recommendation systems the help providers create customized care plans that take into account individual patients’ health status and previous treatments.
As organizations become more familiar with cognitive tools, they are experimenting with projects that combine elements from all three categories to reap the benefits. An Italian insurer, for example, developed a “cognitive help desk” within its IT organization. The system, engages with employees using deep-learning technology (part of the cognitive insights category) to search frequently asked questions and answers, previous resolved cases, and documentation to come up with solutions to employees’ problems. It uses a smart-routing capability (business process automation) to forward the most complex problems to human representatives, and it uses natural language processing to support user requests in Italian.
A four step framework for integrating AI Technologies that can help organizations achieving their objectives:
Step 1: Understanding the Technologies
Before embarking on an AI initiative, organizations must understand which Technologies perform what type of tasks, and the strengths and limitations of each. Armed with a good understanding of the different Technologies, organizations are better positioned to determine which might best adress specific needs, and how system can be implemented. Acquiring this understanding requires ongoing research and education, usually within IT or a research group. In particular organizations will need to leverage capabilities of key employees, such as data scientists, who have the statistical and big-data skills necessary to learn the nuts and the bolts of these Technologies. Given the scarcity of cognitive technology talent, most organizations should establish a pool of resources and make experts available to high priority projects.
Step 2: Creating a portfolio of projects
The next step is to systematically evaluate needs and capabilities and then develop a prioritized portfolio of projects. This can be done in workshops or through small consulting engagements focussing on three broad areas:
Identifying the opportunities Think on:
In some cases the lack of cognitive insights is caused by a bottleneck in the flow of information; knowledge exists in the organization, but is not optimally distributed.
- Scaling challenges
In other cases knowledge exists, but the process for using it takes too long or is expensive. An example:, Watson is helping researchers to surface relationships and find hidden patterns that should speed the identification of new drug targets, combination therapies for study, and patient selection strategies for new classes of drugs.
- Inadequate firepower
When an organization has massive amounts of data but lack insight how ity can be strategically applied using machine learning.
Determining the use cases
The second area to assess: evaluate use cases in which cognitive applications would generate substantial value an contribute to the organizations successes.
Selecting the technology
The third area to assess examines whether the AI tools being considered for each use case are truly up to the task. F.i chatbots and intelligent agents may frustrate some organizations because most of them can’t yet match Human problem solving beyond simple scripted cases (though they are improving rapidly).
Step 3: Launching pilots
Proof-of-concept pilots are particularly suited to initiatives that have high potential business value or allow the organization to test different Technologies at the same time.
Cognitive work redesign efforts often benefit from applying design-thinking principles, understanding customers or end-user needs, involving employees whose work will be restructured, treating designs as experimental “First drafts”, consider multiple alternatives, and explicitly considering cognitive technology capabilities in the design process. Most cognitive projects are also suited to iterative, agile approaches to development.
Step 4: Scaling up
Many organizations have successfully launched cognitive pilots, but they haven’t had as much success rolling them out organization-wide. Because cognitive Technologies typically support individual tasks rather than entire processes, scale-up almost always requires integration with existing systems and processes. Organizations should consider whether this is possible.
In scaling-up, organizations may face substantial change-management challenges and if scale-up is to achieve the desired results, firms must also focus on improving productivity.
The Future Cognitive Organization
Through the application of AI, information-intensive domains could become simultaniously more valuable and less expensive not only for an organization but also for the society as a whole. The great fear about cognitive technologies is that they will put masses of people out of work. However cognitive systems perform tasks, not entire jobs, and they do work that wasn’t done before in the first place such as big-data analytics. With the right planning and development, cognitive technology could usher in a golden age of productivity, work, satisfaction, and prosperity.