The National Football League in the United States replenishes its talent through the draft, each year choosing a few hundred players from 12,000 college athletes. A franchise’s workflow for the event involves gathering every statistic imaginable for each player, collecting qualitative assessments from scouts, and ranking players overall and by position so the general manager is ready to choose when he is “on the clock” in each draft round.
During each round, a team typically selects the best overall player still remaining, or the best player available for a position it needs. On the surface, the drill does not appear more complicated than that, but it is—starting with the fact that the workflow just described hardly represents the many variables and nonsequential nature of the process.
To better manage the multivariate complexities, the San Francisco 49ers professional football team partnered with SAP to create an application that not only provides speedy access to all player stats, but also supports the processes that occur in the minds of the humans making or influencing draft picks, including the general manager, the player personnel executives, the scouts, and the trainers.
The SAP solution supports cognitive processes, such as comparing players and analyzing performance, while respecting intuition, experience, and debate among deciders and influencers. “We’re getting in the mind of the customer and being empathetic,” suggests Jonathan Becher, chief marketing and communications officer of SAP. “We are getting to their mindset.”
The software helps the team understand the players available at any moment, analyze how they would help relative to the current roster and other possible picks, and how they fit in the context of the team’s offensive and defensive philosophies. Through a guided dialogue, it helps the scouts, player personnel executives, and general manager answer questions as they arise while they watch a player, debate a selection, or make the choice. The software pulls up player details in response to queries, and by analyzing queries over time, it anticipates what the user is after, making correlations on its own—similar to the way some mobile consumer apps operate.
Encouraged by the trend toward contextually aware mobile apps, enterprises are starting to request—and software developers are beginning to create—business applications that include the essential workflow automation but go well beyond it to incorporate support for the human cognitive processes as part of the overall business process.
PwC calls this new type of application a mindful app because it incorporates the mindflows of cognitive processes, in contrast to the workflows of the business processes at the core of standard enterprise applications, and it focuses on the “now” by delivering intelligence in the moment. Mindflows are the patterns of thinking that knowledge workers use while doing their work. By now, enterprise software has so effectively automated business process workflows that it actually raises the level of importance of knowledge work, because that is where the human being can add value.
This first article in this issue of the Technology Forecast examines why and how enterprise applications are on the verge of becoming more mindful. The two companion articles explain the underlying technologies and how CIOs and other business leaders can begin to develop and deploy them across an organization. (See the article, “Technologies that enable mindful apps,” and the article, “The mindful CIO,”.)
Long before the digital age, the idea of automating workflow dominated the thinking about business processes because it was—and still is—the key to efficiency, consistency, and other virtues required for large-scale production, distribution, and support. As much as possible, enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM), and other enterprise applications minimize any wasteful variability humans can introduce to a process and let the application control it. No wonder most companies have widely adopted these applications to automate their workflows.
Automation has done wonders for business, taking the power of the assembly line of Henry Ford’s day to the global scale of today, where the notion of making, selling, and distributing millions or billions of something is normal. But not everything can be standardized and turned into a software-dominated process workflow. Not everything should be. Businesses understand this concept implicitly, which is why they have knowledge workers to solve problems, integrate new information with old and make decisions, and rapidly respond to unanticipated events.
For example, repair technicians need to assess a machine and figure out what to do on the basis of their experience, education, and intuition. Salespeople need to do the same when determining prospects and managing the sales pipeline. The same is true for architects, engineers, policymakers, designers, construction workers and other builders, physicians, programmers, spies, teachers, marketers, and scores of other professionals.
Everyone knows the difference between a customer service representative who diligently follows a workflow-based script versus one who is trying to solve the customer’s problem. The one who uses his or her thinking skills is the one who customers prefer and who wins their loyalty.
Such knowledge workers have long used software to help them do their work, but that software has little connection to how business really gets done, other than conforming inputs and outputs (import and export) to other tools. For example, a salesperson will use sales force automation software to record the details of a potential contract for use in deal pipeline monitoring, but not to analyze a customer and design an offer that has the best chance of closing a deal.
In fact, knowledge activity happens in the person’s mind, in ways that are often idiosyncratic and personal but that tend to follow patterns within each persona or group. To contrast these cognitive activities with workflows, PwC calls them mindflows.
Mindflows are goal-driven thinking patterns—such as comparing, evaluating, and summarizing—used by anyone engaged in complex analysis and decision making. The journey to the goal potentially has many paths and unfolds in an iterative divergence-convergence patterns. (See Figure 1.) Scientists have studied patterns of thinking for decades and have offered frameworks that can inform the formalization and codification of mindflows. (See Figure 2.)
Example of a hiring mindflow. Mindflows are the patterns of thinking skills and processes that a knowledge worker uses while doing his or her work
An example of a framework for thinking processes and skills
Oracle’s marketing automation solution, for example, is “modeled around how the marketer thinks,” says Steven Woods, group vice president of software development at Oracle. “I think that’s how today’s software should be designed. How does a human think given a certain task, and how will software mold that thought process?”
The good news is that patterns of thinking are common across many situations in business, whether in sales, hiring, developing strategy, evaluation, onboarding an employee or partner, and so on. Progress results from the dissemination of increasingly complex bodies of knowledge to advance along the mindflow.
The opportunity is ripe for applications to go beyond automation. Unlike the tools that knowledge workers have and will continue to use to create, manipulate, and retrieve the data they need, the new class of mindful apps is about the thinking itself. Mindful apps model human thinking as part of the business process of interest, use context to augment the capacity of employees to conduct knowledge work, and deliver intelligence in the moment.
To go beyond automation, applications need to bring the human cognitive processes into the overall business process and share control of overall progress. While automation limited or took humans entirely out of the business process, mindful apps bring humans back into the process by blending human and software intelligence. (See Figure 3.) “They’re talking to the system and the system is talking back, and that leads to the next question. It’s more of a dialogue that helps them go to and fro, but the dialogue happens to be based on the context and the circumstance,” says Sami Muneer, vice president of product management for sports at SAP.
Future apps should be designed with the understanding that part of a process is best done by a person, but also with the understanding that software can make people perform better, and vice versa, allowing dialogue between the two in the process
Mindful apps differ from traditional enterprise applications in many areas. Table 1 contrasts traditional applications, mobile apps, and mindful apps. A mindful app is not a knowledge management system or a predictive analytics system, even though some may use knowledge management or predictive analytics in service of their purpose. Instead:
Comparing traditional applications and mindful apps
These characteristics bring the human cognitive processes into the overall business process, creating a symbiotic interaction where the mindflows and workflows provide the structure and the human provides the thinking skills for carrying out the activity. Take for example an online news magazine such as Zite, available as an app on all major devices. The app is much better at aggregating and presenting information to humans. Humans decide what is relevant and indicate what topics they prefer. The app learns from the user and over time gets smart and narrows the information it presents to the user. If the app were to do it alone, it would be less efficient and effective in telling the user what to read. If users worked alone, they would spend a lot of time searching. But the combination has made the reading experience better, richer, and symbiotic.
At an abstracted level, mindful apps allow users to do the following:
“Think about Wayne Gretzky. He was the best hockey player ever, because he didn’t skate to where the puck was; he skated to where the puck was going to be,” says Bill Murphy, CTO of Blackstone, an alternative asset management company. Mindful apps help people skate to where the puck is likely to be. “We aim to take away as much stress as possible from the easy stuff, by automating the routine and mundane actions, and give users more time to focus on the higher-end pieces of what they need to do.”
The goal of mindful apps is to to reduce the cognitive load associated with low-value knowledge processes. Mindful apps could free human cognitive capacity to analyze and make decisions associated with high-value knowledge processes. Low-value knowledge processes include recalling specific details, recalling past events that are similar but not identical, filtering information so only facts pertinent to the context and that help define differences in choices are highlighted, and capturing details automatically for later recall.
Computer algorithms are exceedingly good at these low-value knowledge processes. Humans are adept at problem solving, pattern recognition, identifying outliers, and incorporating new sources of information to a complex decision context. Computers are not so good at these high-value processes.
Without mindful apps, complex decisions are often made with unexplored tacit knowledge, such as subjective probabilities well known for deviating from objective probabilities1. Mindful apps expose these biases and make more explicit the basis for making decisions, even as they don’t predetermine the “best” decisions.
This article uses the words applications and apps somewhat interchangeably. Applications usually refer to software deployed on servers and desktops, whereas apps refer to software functionality accessed on a tablet or smartphone. Mindful apps borrow many characteristics from the mobile apps of today; hence PwC calls them apps and not applications. (See Table 1.)
You might think of mindful apps as something new and separate from traditional applications. That needn’t be the case. Many workflows have breakpoints for human involvement. Applications for such workflows are prime candidates for bringing mindful capabilities to the human part of the workflow. Likewise, in workflow applications that constrain human actions for the sake of efficiency, mindful apps could enhance some processes to allow more human flexibility where the best outcome is the intended goal rather than a predetermined result. Therefore, mindful apps are complementary to traditional applications and offer a way to extend the value of the core systems to new use cases that include knowledge work.
Today, mindful apps are most common on mobile devices, because they typically are used in the moment. “Mobile apps allow me to integrate data and context with the workflow,” explains Isaac Sacolick, CIO of McGraw Hill Construction, which has developed mobile apps to bring real-time insights and interfaces to the work of its sales force. In fact, many mobile apps and other consumerization-era apps have introduced the mindful trait, even if their designers didn’t know they were doing so. The contextual nature of mobile and user-centered apps requires less rigid workflows in many cases, leading to basic mindful features.
Navigation software is an example: If you drive a route different from the one suggested because you know a side street is faster than the main street, navigation software adjusts its route based on your actions. It also adjusts its own suggested route based on traffic reports, historic variances such as commute backups, and weather. It’s mindful of the overall context and of the user.
Mindful apps should also exist when you’re in front of a computer, especially as the notion of “being in front of a computer” disappears in an increasingly always-connected, multi-device world. As apps work across computing platforms, mindful apps should become even more capable. That’s because the shift to a mix of computing platforms favors deployment via the cloud, which means apps can move with the user and read local information. If you’re using a smartphone, the app might take advantage of the device’s array of sensors and overcome any internal processing limitations by running some or all of the app in the cloud. If you’re using a PC, the app should have the same context available and also be able to take advantage of the PC’s more capable user interface and likely connections to other resources.
The Google Now platform, Apple’s Siri service, and many emerging assistant services such as Donna and reQall are examples of mindful apps that transcend individual device capabilities while taking advantage of devices’ unique capabilities. Such apps are likely to form a person’s first impressions of what is possible. “Google Now is a good proxy for the trend in apps that we are all going to be benefiting from in the next 10 years,” says Blackstone’s Murphy. “It’s almost like augmenting users with the best possible personal assistant who is with them all the time. If we can use technology to do that in the enterprise, we will have achieved a lot of productivity gains.”
Google Now is an app for Android or iOS mobile devices, but it’s actually a suite of federated services that Google provides and cross-connects to help people in their daily activities. It tracks where you go, and it monitors your calendar, e-mail, web searches, and browsing history. Using this context, it provides what it considers to be the right information at the time, such as suggesting where to get the best currency exchange rates when you are abroad, alerting you to expected package deliveries, and predicting drive times to your next appointment based on your location, weather, and traffic. Over time, it builds a profile of your preferences—what kinds of restaurants you visit, when you commute or jog, what stores you frequent—and uses these digital footprints to develop a model of your mindflow, reduce the cognitive load needed for mundane details, and free human cognitive capacity for high-value tasks and decisions.
Mindfulness can be applied in a variety of business cases, both as new apps and as enhancements to existing software. For example, think of a time-sheet application in which a sales employee logs arrival and departure times, and travel time to client sites. Today, the employee likely enters this information from a mobile device before starting the car to depart for the next appointment. A modestly mindful time-sheet app would notice that an appointment has run long for the fourth time in five visits and suggest a change to the schedule. A more mindful app would also ask the employee, and perhaps his or her manager, if the sales account is proving more difficult or has a larger scope than expected. That question might prompt an assessment of whether the client’s needs differ from what’s expected. Or, it could trigger a discussion that might lead to the assignment of additional resources, or a change to the sales strategy, or other action not obvious from the traditional use of time sheets.
As another example, consider an app that lets HR know how successful a potential employee could be. “We use data we collect in the HR system around a person, and we try to predict answers to questions such as: What is the likelihood that this individual will be an overachiever?” says Chris Leone, senior vice president of development at Oracle.
Mindful apps can also be much larger in scope, designed specifically to address complex problems that rely on human judgment, expertise, and intuition but that benefit from having detailed information available for context and analysis in the moment.
The SAP sports application is one example. “Traditionally, every team has approached this problem the same way,” says SAP’s Jonathan Becher. “They do linear optimization, in which they say, ‘We’ll see who runs the 40-yard dash in such and such time, who jumps the highest, who catches the most, and so on.’ Imagine if you said instead, ‘We don’t want to find the player with the best stats overall. We want to find the best player for our system in our current environment.’ That may not be the single fastest wide receiver.”
The college football draft is a cognitively intense process. “Teams typically spend half their time collecting the information, 30 percent making sense of it, and 20 percent analyzing it,” observes SAP’s Sami Muneer. The scouts and then the team executives need help in understanding the players available, how they would help the team relative to existing players and other possible picks, and who to choose based on who is available when the team’s next pick comes. There is an art and a science to this process.
SAP used design thinking methodology to engage with the 49ers. Although design thinking has a rich history as a method to understand cognitive activities and apply them in designs,2 its expanded use also includes studying the underlying cognitive processes in how work is done. SAP used it to design how the software itself acts and adapts when in use. “For us, design thinking is observing users in context and focusing on the overall experience,” says Rishi Diwan, vice president and head of product management for sports at SAP.
SAP quickly realized that the equivalent of an HR recruiting system would not help sports teams make the best decisions. “Coaches, scouts, and executives have a certain way of expressing; they have a certain way of understanding. They all take pride in their analysis and experience. You can’t throw charts at them or have the computer drive their behavior. They’ll check out right away. You need to understand their mindset,” Muneer says.
SAP concluded that any solution needed to do the following:
The 49ers’ early experience is that they can deeply assess many more players than was previously possible, and they believe they’re asking better questions and getting better answers. Thanks to interfaces that are mindful of cognitive complexity and the pertinent and necessary information to the task at hand, they can comprehensively understand players in minutes rather than hours. (See Figure 4.) It’s difficult to calculate a traditional ROI because ultimately any team is matching their collective judgment against that of other teams, and a smarter process’s results may not be obvious immediately or even be better in every circumstance. The goal is to increase the odds of success by helping people do what they do better.
Screen shots of a player profile and player details from SAP’s sports application for understanding new players
If you can’t show a 10 percent labor reduction or some other measurable ROI, how can you justify mindful apps? That’s partly a trick question. Mindfulness should not replace efficiency or standardization. If a company can show ROI for achieving further efficiency or standardization, it should do so.
But most large businesses are approaching the limits of operational efficiencies that IT systems can deliver, because they have largely succeeded in their automation and standardization goals. That leaves the nonautomated work—the work done by people—as the opportunity for greater effectiveness. Effectiveness—getting the best results possible—is a crucial goal for human processes.
The metrics are fuzzier—customer satisfaction, quality level, brand reputation, and so on—but over time an effective company should make more money than a merely efficient one. Apple, for example, makes nearly half the world’s PC profits, despite a market share of less than 7 percent.3 Its manufacturing operations are very efficient, but that’s not why it makes so much more profit. The reason is that the company can charge much more because of all the attributes people associate with its devices and services. It’s more effective than its competitors. And that’s better for its bottom line.
In other words, you want to be both efficient and effective.
Marketing automation is one area where mindful apps are appearing in business. It is a discipline that relies on analytics, workflow, and human judgment. Many tools can efficiently distribute marketing messages to customers and would-be customers over e-mail, telephone, websites, and postal mail. They can even segment audiences for some form of targeting. But the decision to buy something is ultimately a human one, based on the idiosyncratic requirements of each person, individuals at a company, the internal procurement process, the vagaries of budgets and time frames, and the judgments buyers make on the path from product identification to purchase decision.
Several vendors, including Oracle’s Eloqua subsidiary, Marketo, and Teradata’s Aprimo subsidiary, provide mindful marketing automation apps. They’re mindful because they use the digital footprints—browsing, downloading, social networking commentary, e-mailing, and tweeting—of potential buyers to understand their mindflows and predict where they are in the buying cycle.
“The paradigm for the interaction architecture in Campaign Canvas [Oracle-Eloqua’s product] was really designed to model, as best as we could, how a marketer worked on the whiteboard to the point that it’s not just the flow but the sequence of thoughts of the marketer,” Oracle’s Woods says. The tool can also be used to model the mindflows in other use cases. An example of its use in an employee onboarding process is shown in Figure 5.
An example of Eloqua’s Campaign Canvas showing the model of the mindflow of a new employee in an onboarding context
These mindful apps seek to understand customer attitudes and interest levels relative to a company or its products. Often, gaining this understanding includes correlating digital footprint data with internal salesperson reports and logs. That information lets marketers target their communications with content at the moment it is impactful. What makes such impact possible is what Woods calls the digital body language.4
Much of what now happens inside and outside an enterprise happens interactively in digital environments. The total of the digital footprints of any customer or employee is their digital body language. Just like a buyer’s physical body language provides cues to a seller about the buyer’s interests and inclinations, digital body language does so in the digital domain where the buyer and seller do not interact in person.
“If you start looking into the psychology of interests, it’s highly dependent on the context of that user’s world at that moment in time,” Woods says. “If you put the same piece of information in front of me at a different moment in time, at a different stage in the projects I’m working on, I’m going to tune it out very, very quickly.” As any marketer knows, you have to figure out a way to get a message in front of someone who is interested in your topic at the moment they’re interested in it. This is being effective.
Throughout the long history of enterprise applications, software has influenced different flows of activity in a business process. As Figure 6 shows, enterprise IT started by supporting commercial transaction flows. Over time, the transactions were stitched together into end-to-end processes, and application support evolved to workflows that optimized group work. ERP systems are the best example.
Enterprise applications have evolved to move the focus from transactional flows to mindflows, and in doing so have reduced the lag between knowing and doing
Information about the transactions was important, so management could monitor the business and make decisions. Thus, information was freed from the transaction systems and flowed in to and out of rationalized, organized data warehouses through data marts to end users, evolving the support to information flows.
As social and collaboration platforms have become popular, and executives recognized the importance of efficiently transferring knowledge through human interaction (even if digital), the support has evolved to knowledge flows within and outside the enterprise.
Each evolutionary step created the potential for IT to participate in a new level of business activities. Also, as technology has extended its reach from transactions to workflows to information to knowledge, it has gradually reduced the time it takes for a significant business event to become broadly known—and responded to.
The next new level of engagement between applications and the enterprise is the flow of thinking at the individual user level—what PwC calls mindflows. And the next frontier in business value creation is targeting effectiveness by guiding employees to do the right thing. Mindful apps, designed to aid human cognitive processes and deliver intelligence in the moment, are beginning to appear in the consumer and business worlds alike.
Mindful apps are in their early days, with plenty of value to be gained and opportunities to be exploited. Smart companies will make sure that making people smarter, better informed, better able to judge, and more effective is a key priority.
1. Amos Tversky and Daniel Kahneman, “Judgment under Uncertainty: Heuristics and Biases,” Science, New Series, 185, no. 4157 (September 27, 1974): 1124–1131, http://psiexp.ss.uci.edu/research/teaching/Tversky_Kahneman_1974.pdf.
2. “Design thinking,” Wikipedia, http://en.wikipedia.org/wiki/Design_thinking.
3. Horace Dediu, “Escaping PCs,” Asymco, April 16, 2013, http://www.asymco.com/2013/04/16/escaping-pcs/.
4. Steven Woods has also written a book on the subject: Digital Body Language (Danville, California: New Year Publishing, 2009).