What You Will Learn
- 1 What is Artificial Intelligence?
- 2 The Real Difference between Artificial and Augmented Intelligence
- 3 Adding Context
- 4 Augmented Intelligence as a Service
- 5 The Four Laws of Augmented Intelligence
- 6 Using Augmented Intelligence in Your Sales Process
- 7 Augmented Intelligence in Practice
- 8 Why Augmented Intelligence Matters
- 9 Augmented Intelligence Technology
- 10 Artificial versus Augmented Intelligence
- 11 Architecting an augmented intelligence solution
- 12 Augmented intelligence in practice: Auto Manufacturing
- 13 Key Takeaway
Artificial intelligence is a buzzword that’s found its way into conversations in companies across the globe. You may be currently using it to run your company better or improve the customer experience, but is artificial intelligence all that it promises? What’s the real story of artificial intelligence? And how can you apply its practical use to the world of sales performance?
What is Artificial Intelligence?
Artificial intelligence in its simplest terms is intelligence exhibited by computers rather than a living being, hence the artificial descriptor. Artificial intelligence occurs when machines “learn” with sophisticated models based on past behaviors and preferences or perform complex tasks that require human intellect. This, in turn, helps deliver the best next action and improve outcomes over time.
This form of intelligence is used most often to automate business and customer-facing processes. It requires no human intervention or thinking. Most people interact with artificial intelligence every day from detecting fraud or diagnosing malware to conversations with chatbots like Apple’s Siri.
The Real Difference between Artificial and Augmented Intelligence
Artificial intelligence has seen a huge rise. In 2014, $300 million was invested in artificial intelligence startups. It’s a very attractive approach to put big data into practice. However, when looking at how companies are applying AI, it’s obvious they’re really seeking an augmented intelligence solution.
According to a study by Narrative Science and National Business Research Institute, 44 percent of executives believe artificial intelligence’s most important benefit is “automated communications that provide data that can be used to make decisions.” This very description is that of augmented not artificial intelligence. This same study also found that 80 percent of executives believe artificial intelligence improves worker performance and creates jobs. Augmented intelligence automates processes that surface recommendations and insights that make decision-making more effective.
Big Data Feeds Augmented Intelligence
Big data has been heralded as the biggest game-changer for business since the Internet. First, organizations had to collect and aggregate the data, building an infrastructure to support it. Then, they had to actually do something with it. Big data means nothing unless you can discover insights that drive action and improve outcomes.
Companies have been in the midst of moving their analysis of data in spreadsheets to business and sales intelligence solutions. Simple drag and drop, powerful filters, and interactive visualizations were meant to be the cure to manual analysis. That is, if you know where to find insights hidden in your data and how to interpret the results.
Big data and augmented intelligence are propelling businesses forward by automating this search for answers. It enables finance, marketing, and sales to maximize their time spent hitting their numbers.
With context, the “A” in AI becomes augmented. This shift occurs when insights are aggregated for human engagement. It’s not an automated action. For example, Bank of America found, through analysis of its big data, that its top performing employees at call centers were those who took breaks together. This valuable nugget of information didn’t become clear because a machine-learned it. There were trends and patterns found that were then further reviewed by humans. The bank used this information to establish a policy around group breaks, improving performance by 23 percent.
This example shows how big data was taken a step further with human cognition. Machines can learn and discover insights in real time, but they can’t dream. Humans are a lynchpin to transforming data-driven insights into quick, confident, and impactful actions that drive business results.
Augmented Intelligence as a Service
Augmented intelligence isn’t something that happens in a magic lab. It happens, rather, right on your screen. When considering leveraging it, companies must consider if they want to maintain a toolkit or deploy a service. Toolkits are packages of machine learning algorithms that require an IT infrastructure and training by data scientists for models to be harnessed.
This contrasts with augmented intelligence as a service: a working, scalable, full-service solution. Business users can make better decisions using insights and recommendations embedded in the applications where they live and breathe every day—without a data scientist or time-consuming and costly implementation.
The cornerstone of an augmented intelligence service is continuous learning. As more data is processed by machine learning algorithms every day, the recommendation confidence also improves. Human action and outcomes hone augmented intelligence as a service to drive business results without IT and data scientists.
The Four Laws of Augmented Intelligence
It’s important to provide a framework for augmented intelligence. Otherwise, it may be hard to understand the narrative it’s presenting. This framework consists of four laws:
Using Augmented Intelligence in Your Sales Process
With the right software system, machine learning can be embedded throughout the seller’s journey. This journey is enabled with actionable recommendations inside the applications sales professionals live and breathe in every day. Delivering relevant insights at the right time ensures individuals are able to make quick, confident, and impactful decisions when it matters most to accelerate and close bigger deals faster.
Augmented Intelligence in Practice
For augmented intelligence to have an impact, the insights must be at the individual level. Each professional needs to understand how he or she can improve and drive results, no matter what their role.
Sales operations teams are tasked with increasing revenue and ensuring sales teams hit their targets. They need to be experts in technology platforms, be it CRM systems, sales enablement, or incentive compensation. They also have to know how to measure activities, get results, use numbers in an insightful way, and guide deals through to close, while being a coordinator and project manager.
Augmented intelligence is pivotal as sales ops teams seek opportunities to transform from tactical roles—putting out fires—to strategic roles—streamlining processes and finding opportunities to grow the business. Augmented intelligence eliminates guesswork with granular prescriptive compensation plan recommendations. Seamless territory coverage and personalized quotas motivate every sales rep to achieve. Relying on predictive and prescriptive recommendations means less time working with spreadsheets, emailing forecasts, updating price books, and managing product configurations and more time driving results.
Sales is responsible for one thing—hitting the number. Augmented intelligence gets more people to the President’s Club every year by eliminating guesswork and highlighting the next best action to bring a deal to close. Guiding sales to the leads and opportunities that need their attention the most accelerate pipeline, rescues pushed deals, and prevents sandbagging. Onboard sales reps faster and make the most out of every meeting with prescriptive learning courses, coaching, and sales collateral that earns the next meeting. Put your best foot forward with personalized pricing, products, and services most likely to win the deal.
Why Augmented Intelligence Matters
Simply put, it can bring about the change and solutions that big data has promised. It turns every sales ops professional, sales leader, and sales rep into a force multiplier. They are able to make quicker, confident, and more impactful decisions that accelerate planning and pipeline, and close bigger deals faster.
Artificial intelligence works better in structured environments where there are few unknowns. The most desirable possible outcome is predictable. Chatbots and email assistants can craft messages that generate meetings. But these meetings are attended by sales reps who must bring the deal to close. Artificial falls short in situations where goals and inputs are complex. This is where augmented intelligence stands taller. It guides sales ops to plan an effective sales strategy and sales reps to execute a playbook.
Augmented Intelligence Technology
2018 is set to be the year that artificial intelligence, or A.I., breaks into the technology mainstream.
Or at least it is according to a rising swell of media hype.
Every day, new articles emerge asking if “Robots [Will] Take Our Children’s Jobs,” or suggesting that “Artificial Intelligence is Changing Our Brains.” In circles from marketing to finance, business operations to data analysis, 2018 is being hyped as the year machines take over the role of human analysts, offering the ability to make fast, informed decisions based on volumes of data that no human could ever hope to analyze in a timely fashion.
In this tutorial, we’re going to explore how businesses can be applying augmented intelligence technology, we’ll look at the differences between augmented and artificial intelligence, and we’ll help identify solutions that can truly impact a bottom line.
“AI systems [are often] tested on a specific problem or application, and while machines may exhibit stellar performance on a certain task, performance may degrade dramatically if the task is modified
even slightly.” – A.I. Index Report, 2017
Artificial versus Augmented Intelligence
The term ‘artificial intelligence’ has been around the data analytics field for decades, but only recently can be seen its application in the business environment. In 2011, IBM’s Watson gained notoriety for beating two human contestants on Jeopardy, and after a series of similarly well-publicized events, the technology – and other technical solutions like it – gained enough traction to be applied in business settings. Today, artificial intelligence solutions are being introduced in industries from manufacturing to medical research, in each case promising some form of human-like decision-making and forecasting.
What is remarkable, though, is that despite being advertised as a true replacement for human intelligence – these A.I. systems require an active and engaged team of human analysts, who often come in the form of “add-on services” from the very companies providing the A.I. service.
That is because artificial intelligence is not yet as intelligent as it is hyped to be. While machines can be programmed to analyze specific streams of data, draw some basic insights, and even recommend similar data for exploration, they are not as capable as one would like to think they are at noticing subtlety, drawing bigger picture conclusions beyond the data immediately in front of them, or forecasting, skills at which humans succeed almost uniquely.
“Gains in specific tasks or gameplaying proficiency are still a far cry from general intelligence. A child, for example, knows that a water glass tipping on the edge of the table will most likely fall to the floor. He or she understands the physics of everyday life in a way that A.I. programs do not yet.” – The New York Times, 2017
That is why in this tutorial prefer to distinguish artificial from augmented intelligence, and why smart companies are currently championing the use of augmented intelligence approaches.
Augmented intelligence describes a combination of the human brain and the most reliable facets of artificial intelligence. An approach using augmented intelligence plays to the strengths of both human analysts and machines, assuming that, between the two, humans tend to be the better decision makers while machines tend to be faster analysts. It also assumes that the human analyst should always be in the driver’s seat, and that the machine exists to assist, not replace.
There are companies that take advantage of this technology and employ some of the top data analysts in the world. These analysts, using proven analytic methodologies, can perform pattern and trend analysis across multiple structured and unstructured data sets to detect threats or assess potential business opportunities. On their own, though, they may not have the capacity to analyze all available sets or provide analysis at a speed that benefits key decision makers.
However, that same analyst, armed with a system that enables automated data ingestion, persistent search, and dynamic discovery – what many consider the building blocks of artificial intelligence – can deliver their analyses in real-time while ensuring that they are looking across, absorbing, and analyzing every possible data point. As an example, coupling proven analytic methodologies with augmented intelligence technology enables human analysts to create complex queries, which filter the information they are aggregating to only the most meaningful. This means that analysts are discovering, consuming, and analyzing more specific data that is also more relevant and can be acted on faster.
In the augmented intelligence equation, the human analyst is fundamental to the final output but is now able to deliver at a speed and scope that could only be achieved with the aid of intelligent machine computing.
Architecting an augmented intelligence solution
There are three key features that form the basis of an effective augmented intelligence solution for
For any system to offer actionable intelligence, it needs to be pulling from all of a company’s relevant data sets, whether those are internal or external, structured or unstructured. In the past, were seen instances in which some business intelligence providers claim to deliver powerfully, “machine-learning”-driven business intelligence, but in practice can only ingest a fraction of a company’s datasets into their own systems at a given time. So, their intelligence ends up delivering insights based on an incomplete picture of a company’s data. Searching across all possible datasets – meaning billions of data points versus millions – requires that a system be built horizontally – or flexible – to scale with the breadth of data it is required to handle.
A good augmented intelligence system makes use of the currently available components of artificial intelligence to sort through all of that data, enriching it in a way that makes it more searchable and effective. For instance, natural language processing – a technology that lies at the core of artificial intelligence – can be used to organize and assign meaning to unstructured data such as customer service calls, social media posts, or product information. Sentiment analysis, an additional layer that is particularly useful to those who provide customer service, can be used to distinguish the emotional weighting of certain data points.
Low-Entry Search and Analysis
The final component of most augmented intelligence solutions is a front-end search and analysis interface – the window between the company’s now-enriched data and the analyst charged with deriving insights from it. What differentiates an augmented intelligence solution from its artificial intelligence counterpart is the breadth of its design target. On a day-to-day basis, the intended target of an augmented intelligence solution is a trained analyst, someone who has at least a foundational knowledge of analytic methodologies and advanced search techniques.
However, the solution must also provide a high-level overview accessible to those without advanced analytics training. This view tailors to the decision-makers, business executives, and the C-suite level to whom the analytics teams often report.
As one can see, in an augmented technology solution, each of these three components – from the data enrichment to the front-end interface – is constructed under the premise of teaming humans with machines, and not removing humans from the equation. And as we’ll show, this philosophy not only applies to how augmented technology systems are constructed, but also to how they are put in practice.
Augmented intelligence in practice: Auto Manufacturing
Augmented intelligence solutions can be applied across numerous industries and use cases. In each use case, though, it is important to understand that the business intending to implement the system is using it to enhance the capabilities of the human analysts it already employs – not to replace those analysts.
One of the clearest examples involve a North American Automobile Manufacturer (“NAAM”), who wanted to bolster the effectiveness of its auto safety analysts by expanding the amount and types of data that they analyze. The team’s ultimate mission is to protect their customer’s safety by getting ahead of possible manufacturing defects.
NAAM’s Vehicle Safety Analytics division (“VSA”) consists of data scientists and technical developers who organize and study data by mining internal and external data sources. These sources include social media, vehicle telemetric data, customer complaints, warranty, call center, and legal claims data. VSA faces billions of rows of the company’s structured data fields, as well as complex unstructured text data which need to be classified into specific hazard categories, such as steering loss or brake malfunction, in order for the team to more easily identify emerging issues.
On their own, VSA would not be capable of searching across all of those datasets at once, and no machine has the judgment to identify problem parts on its own. The only way to enable VSA to analyze all of that data was through an augmented intelligence solution that fused the disparate sources into one system applied some of the core building blocks of artificial intelligence and created a smart search capability that VSA could use to identify potential safety outliers.
The system designed did not remove the human analysts on the VSA team from the equation, (though the client was able to reduce the number of data “readers” that it previously used to cull through the incoming data.) Instead, the system enabled the VSA team to be more productive and effective in discovery, comprehension, and analysis – to spend less time searching and more time doing meaningful analysis. The multiple datasets they were tasked with analyzing now filter through a single, easily-searchable portal, and are enriched with AItype technologies – from NLP to advanced text mining – that enable the team to look at specific data with pinpoint precision, at pace.
“60% of companies believe augmented intelligence will help them obtain new customers, and over half of those surveyed believe these technologies will help significantly increase revenue.” – Forbes, 2017
There is little question that artificial intelligence marks an exciting advance in the future of computing and business transformation. It is no doubt impressive, for instance, to know that a computer can defeat a Chessmaster or win at jeopardy. But today artificial intelligence is still only as powerful as the human intelligence that is fueling it.
This is not to say that artificial intelligence will not continue to mature – it will. Advancements in neural network applications, cognitive computing, and predictive analysis will bring us closer to the point of full automation in many data analysis environments.
For now, though, augmented intelligence is an approach that takes many of the underlying features of artificial intelligence and packages them in a way that bolsters human intelligence, without replacing it. Augmented intelligence will give companies a reliable way to maximize their data-mining and analysis capabilities, effectively driving concrete business metrics.