This article is part of a media partnership with Data Analytics and Behavioural Science Applied to Retail and Consumer Markets conference, an event featuring high quality case studies, networking sessions and discussions full of insights on the Retail and Consumer Markets. It takes place June 28 at Millennium Hotel Mayfair, London. To learn more about how to gain business insights with Artificial Intelligence, you can’t miss Mandie Quartly’s session. Come and learn how to take advantage of rapidly evolving and innovating technologies.
The potential of Artificial Intelligence (AI) has filtered all the way up to top management in businesses around the world. Artificial Intelligence has many upsides for organizations, such as lower costs, better service quality, and the promise of fast, actionable business insights. The question is how can decision-makers lead their organizations towards harnessing the true power of AI-powered technology.
The market for AI technologies is booming, fueled in part by rising adoption by the enterprise. Results from a study by Narrative Science found that 38% of enterprises are already using AI, and that number is set to grow to 62% by 2018. In this Forbes article from earlier this year, Gil Press found that this is but one of many encouraging surveys:
- Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016.
- IDC estimates that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.
AI is a complex field. Executives cannot just think of it as a process of applying isolated technical knowledge to a specific problem. Real value comes from understanding the vast array of technologies that underpin Artificial Intelligence, and how they can be used to develop full-service products.
How to think about AI-aided technologies for your business
Accenture’s comprehensive report on the potential of AI technologies for businesses, Turning Artificial Intelligence into Business Value. Today, sheds light on how to better understand the AI-aided solutions to your business goals. The report separates them in terms of automating and augmenting. For either category, companies need to asses solutions based on two criteria: work complexity, and data complexity.
Work complexity can span from routine, rule-based work (i.e. the work of a clerk officer), to ad-hoc tasks that require human judgement (i.e. the work of a research scientist). Similarly, data complexity can range from low-volume, structured data sets, such as sales reports, to high-volume, volatile data coming from social media, for example.
The four activity models for business AI solutions
Analyzing the above mentioned criteria results in a framework, from which four primary types of activity models arise:
- Efficiency
This model aims to provide consistent, cost-effective performance solutions with technology that senses and acts. Humans monitor accuracy and adjust rules based on business conditions. The systems translate these decisions into action quickly, accurately and efficiently.
- Expert
This model includes so-called “expert systems”. These are able to survey massive data sets to make recommendations based on that knowledge. The system functions either autonomously, or provides input for human judgement.
- Effectiveness
The effectiveness model aims to enhance workers’ overall ability to produce a particular desired result. Technology then acts as personal assistant; humans use the cognitive tools to assist in scheduling, communicating, monitoring and executing activities. Consumer-facing agents such as Siri, Cortana and Google Now are perfect examples.
- Innovation
In this model, AI enhances human creativity and ideation—for example, for artists, entrepreneurs, researchers, and designers. Technology acts as a support system that offers recommendations and identifies alternatives, while humans make decisions and act.
These are just a few examples of how Artificial Intelligence technologies can be integrated into compelling business solutions
- Computer Vision – Insights into shopper behavior; Business operations analytics
- Audio Processing – Call center automation
- Speech recognition – Interactive voice response systems
- Sensor Processing – Precision agriculture
- NLP – Personal assistants
- Knowledge Representation – Web search and linking
- Inference Engines – Loan & credit approval
- Expert Systems – Medical diagnosis
- Machine Learning – Software tools
Any business looking towards AI to optimize its processes has to keep many options open. Executives cannot completely focus on any particular technology. The truth is, technology by itself is not the answer. The first step is thinking about the type of work that needs to be done, and then, about how integrating AI technology can become a solution to that problem.
The AI-development mindset
Developing Artificial Intelligence systems requires different methodologies than are needed for developing traditional IT-systems. For example – training a cognitive system resembles training a new employee, as both have to be trained in a particular domain of expertise before they can add value. On the other hand, developing expert systems primarily focuses on identifying relevant data sources and then gathering and curating content. This new development paradigm comprises training and supervising an AI solution.
Getting to business insights when you need them – how to make it happen?
There’s a lot of talk about using Artificial Intelligence (AI) to gain business insights, but there are a number of key ingredients required to actually make it happen in a timely fashion. A vital element is the technology which underpins AI and machine learning applications. Come and hear more about “making it happen” for your organisation; learn how to take advantage of rapidly evolving and innovating technologies. The Data Analytics and Behavioural Science Applied to Retail and Consumer Markets conference, takes place on 28 June at Millennium Hotel Mayfair, London. Mandie Quartly will be the plenary speaker.
Mandie Quartly is the worldwide lead for Machine Learning and High Performance Analytics software in the IBM OpenPOWER Ecosystem team. In essence, her focus is the creation and growth of strategic relationships with key software organisations looking to enable end users to gain timely insights from their big data using IBM’s POWER hardware platform. Her background is Linux, Power Systems and High Performance Computing focused, specialising in the design and implementation of high performance Linux-based systems. Mandie has an MBA and a Ph.D. in Astrophysics.
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Image: Engineering at Cambridge, Jean de la Verpilliere, CC BY NC-ND 2.0
