Adaptive BPMFor Adaptive EnterprisesDr. Rob Walker, Vice President, Decision Management & Analytics Dr. Setrag Khoshafian, Chief Evangelist and VP of BPM Technology

2Executive Summary

The Problem

Enterprises are facing unprecedented economic, competitive and global challenges. Traditional approaches to incremental cost-cutting and revenue growth are not succeeding. Optimizing the customer experience to increase customer loyalty and reduce customer defection has become critical. The problem is that the environment in which enterprises operate is constantly in flux. While adaptive was once merely a catchy term, it is now a requirement for organizational survival and sustained growth. The challenge is to continually adapt, leveraging customer transactional data, changes in customer as well as market behavior, and to create innovation opportunities with iterative and measurable improvements. Enterprises need to embrace a more holistic approach to adaptability.

The Solution

The solution for organizations is to understand and embrace the seven key capabilities generally found in adaptive enterprises. These capabilities are not mutually exclusive, yet they are distinct and essential. Enterprises become adaptive through:

`` Continuously monitoring optimized, and measurable business objectives

`` Continuously innovating through re-use and specialized, contextual and situational solutions

`` Continuously discovering business requirements with iterative enhancements

`` Continuously improving automated work and dynamic case management solutions

`` Continuously gaining insight, while learning and adapting with better decisions

`` Continuously adapting decisioning champions as customer behaviors change

`` Continuously visualizing, simulating and optimizing decision priorities

The Benefits

Organizations that have leveraged Pega to become adaptive enterprises have reaped tremendous benefits in four main areas:

`` Generating increased revenue through solutions such as new account opening and cross-sell/up-sell with incredible speed while innovating with new products and solutions.

`` Optimizing the customer experience through continuously monitored and optimized customer service and decision management. Pega solutions continuously adapt to new market realities and the customers changing needs, while always focusing on the value of the customer through communications and offers that fit their requirements.

`` Continuously improving efficiencies through eliminating waste and improving speed of execution while building, executing, or improving customer-centric solutions. The automation of dynamic cases is the key enabler of continuous efficiency.

`` Business transformation through the robust support for different levels of business transformation. Pega helps organizations achieve a rhythm of change through our unique software and methodologies.

1Figure 1: BPM Suites

Introduction

Business Process Management Suites have evolved from a number of disciplines. Process improvement methodologies, such as Lean Six Sigma, attempt to eliminate waste in work processing, while increasing the efficiency as well as the quality of products and services. Process automation has evolved from structured production workflows to collaborative, unstructured, and dynamic cases. Process intelligence spans business rules, decisioning, and business events. Architecture patterns such as service oriented architectures and more recently Web oriented and customer-oriented architectures are enabled and empowered through BPM Suites. These disciplines and technologies are evolving to what is called the adaptive enterprise. An adaptive enterprise can align its business objectives to the operationalized policies and procedures with complete transparency, visibility and control. More importantly an adaptive enterprise is agile and proactive in responding to change. The only constant in business is change!

A BPM Suite is the platform that enables the organization to realize the promise of the adaptive enterprise. With BPM, stakeholders will have complete visibility and control of their objectives, which are often expressed in key performance indicators. Stakeholders can see and understand what is going on with their support, mission critical actions, and management processes. More importantly, they can be proactive and make changes to improve them. BPM enables business stakeholders to be in the drivers seat: monitoring, improving, innovating through new solutions, automating work and building efficiency throughout. In other words, BPM is about running the business!

AdaptiveEnterprise

IntelligentBPM Suites

Pega BPMEA SOA WOA

ScientificManagement

BusinessProcess

Re-engineeringTQM

Lean Six Sigma

Process Automation

StructuredProductionWorkflow

DynamicCase

Management

SocialMobileCloud

Process Intelligence

BusinessRules

DecisionManagement

Business Events

Process Improvement

Process Architectures

COA

NPSBSC

Big Data

2Adaptive BPM comprises the platform, solutions, best practices, methodologies and governance for adaptive enterprises. This paper discusses the seven essential components of an adaptive enterprise, as illustrated in figure 2:

`` Continuously monitoring and optimizing measurable business objectives

`` Continuously innovating through re-use and specialized, contextual and situational solutions

`` Continuously capturing requirements with iterative enhancements

`` Continuously improving automated work and dynamic case management solutions

`` Continuously gaining insight, learning, and adapting with better decisions

`` Continuously adapting decisioning champions as customer behaviors change

`` Continuously visualizing, simulating and optimizing decision priorities Figure 2: Adaptive BPM

DataGood

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Insightful and Scalable in Learning For Better Decisions

Adaption Through Simulation and Optimization

Continuously Monitored Business Objectives

Adapt Through Contextual and Situational Execution

Adapt Through Continuous Discovery

Adapt Through Automation and Dynamic Cases

Continuously Learn Through Adaptive Decisioning

Adaptive Through Champion Challengers

CUSTOMERS

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3Continuously monitoring measurable key performance indicators and being able to drill down, improve, and adapt are key characteristics of adaptive enterprises. Due to BPM automation, the business is able to do real-time activity monitoring. The BPMS keeps track of the execution of each automated process and maintains an audit trail of the assigned task, the performance of the operators or workers, the performance of solutions built in the BPMS, or the performance of individual processes. Businesses can take any of their key performance indicators, drill down and affect change: for instance, bulk re-assign the tasks of operators who are not able to keep up with their service levels.

Figure 3 illustrates the taxonomy of the type of knowledge or insight we can get from data and the corresponding business value. Reports can be sourced from real-time business activity monitoring or data warehouses that contain historical data potentially from multiple sources. BPM suites are increasingly becoming a key source of data for both business activity monitoring (BAM) and data warehouses. Reports are useful for understanding what happened (historic) or what is happening

now (BAM). Analysis goes further in data insight and attempts to slice and dice the data along different dimensions and perspectives. Analytics is a key technology that enables companies to extract invaluable insight and infuse that insight into key business decisions. It attempts to discover trends and glean insight from aggregated data. Dashboards allow business stakeholders to have a role-specific, strategic, key-performance perspective on their operations. The users can drill down and potentially act on any detected bottlenecks.

Pega provides extensive and comprehensive support for adapting through continuously monitoring measurable business objectives. The Pega business activity monitoring can be summarized as follows:

`` Pre-built Reports and Browser Pega BPM provides dozens of out-of-the-box reports, and Pega Frameworks provide even more functionality. Pre-built and custom-built reports can be organized, browsed, searched and run from the report browser.

`` Report Viewer The report viewer provides intelligent drill-down, aggregation, and pivot table behavior, and lets users easily customize, save, and share reports with title, column, filter, and sorting changes.

`` Report Designer This business-friendly tool lets designers create even complex reports in seconds, selecting data from work tables and external systems. With easily customizable capabilities such as top/bottom results, business users can easily create reports that exactly match business needs without needing DBA or IT help.

Continuously Monitored Business Objectives

Figure 3: Taxonomy of BPMS data

Reports:BAM or Historic

Analysis: OLAP

Dashboards:KPI Scorecards

Knowledge Discovery

Busi

ness

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Operationalized (Automated in BPMSs)

Predictive Models

4Designing and viewing real-time business activity monitoring reports can help organizations manage their Pega applications key performance indicators, as well as the workload of dynamic cases. In a federated deployment, an organization often wants

to combine data from Pega systems, legacy applications, Web portals, and other sources to provide a more complete picture of the business, using data warehousing for decision support and advanced analytics. To handle complex applications, Pega stores its data in a compressed XML object format. Pegas Business Intelligence Exchange (BIX) tool extracts Pega case data into formats suitable for importing into a consolidated data warehouse or other analytics environment. With BIX, Pega case date becomes one of the key sources (dimension and/or measure) in a data warehouse. Analytical Business Intelligence tools can be then used to slice/dice that data warehouse, involving Pega case data.

Figure 4: Pega BPM reporting

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Figure 5: Business Intelligence Exchange (BIX)

5When business applications execute, there needs to be a context or business intent: the type of the customer, the location or jurisdiction of the customer, or the specific business productto name a few. All these dimensions need to be used to select and provide the best policy, user interaction, or information source for a given situation. Through robust BPM solutions, the adaptive enterprise needs to reflect the way people manage change in their organization. Businesses need to treat customers uniquely, based on a particular set of criteria. BPM can provide the context and specific solutions for their specific customers or lines of business. The BPMS needs to support optimized re-use and specialization, and then the automatic selection of the most appropriate specialized asset (policy or procedure) for a given situation.

Using BPM, enterprises should be able to adapt through easily reusing and globally specializing their business assets. The assets here are BPM assets for execution. The assets include flow fragments, business rules of different types (decision, constraint, expression), UI, information, integration etc. The multi-dimensional organization of BPM assets is the mechanism that enables the adaptive enterprise easily:

`` Introduce changes: deltas that capture specific policy or procedure changes

`` Customize and specialize so that, for instance, different customers are treated differently: depending upon who they are, where they are, what type of product/service they are requesting, when the request is made, etc.

`` Organize solutions for optimal reuse across the enterprise and then specialize for specific locations, customer categories, solution frameworks, or lines of businesses.

Think of giving special discounts for specific types of customers. The discount calculation is a specialization and it depends upon the type of the customer. It could also depend upon specific jurisdictions, locations or timeframe. That is how business manages specializations.

Through Pega an adaptive enterprise can have common policies and procedures, and then easily add specializations for specific situations. The Pega BPM engine then selects dynamically the best policy or procedure for a given context or situation. The Pega repository supports out of the box versioning, auditing, access control, testing, search/navigation, and processes for managing change. The Pega BPM assets are organized in a dynamic multi-dimensional repository.

The repository has several dimensions. This includes temporal versioning, but other dimensions are equally important. For the adaptive enterprise, the repository supports central models and constrained customization for branches or departments, or geographical locations or offices. There can also be a dimension that addresses the type of customer.

Adapt Through Contextual and Situational Execution

Pega supports a multi-dimensional repository of BPM assets that are organized in specialization layers. At execution time, the Pega BPM engine selects the best asset (UI, process fragment, policy, or service (integration)) depending upon the situation.

6The enterprise repository can adapt quickly while reusing existing solutions. Application reuse is associated with assets (flows, business rules, etc.) specific to an application and common to more than a single business unit. The exceptions to the rule that normally require extensive

coding or human intervention are handled with a simple specialized layer that captures the rules for the particular situation. For example, all divisions across an insurance organization would need some type of policy administration or billing application. The policy administration applications would all require features to support quoting, underwriting, binding, and issuing of policies. Specialization or customization can be for specific business units or global operations (countries or regions).

The ability to organize, reuse, and easily customize existing policies for specific situations is extremely important in adaptability: managing change across the enterprise while leveraging reusable practices or frameworks. Pega calls this run-time specializations and it achieves contextual execution through what we term Situational Layer Cake. Pega BPMs run-time specialization is a revolutionary approach that uses dynamic layers that are selected depending on the circumstance. The exceptions to the rule that normally require extensive coding or human intervention are handled with a simple specialized layer that captures the rules for the particular situation.

This is the core of the Pega BPM architecture and provides a robust enterprise repository of business application assets. Pega BPMs enterprise repository offers:

`` A unified repository with advanced tools for all users to speed delivery and enhance re-use

`` Heat maps and other visual tools so designers and analysts can quickly assess and navigate a Pega BPM application and its components

`` Repository controls supporting locking, versioning (including multiple active versions of the same asset), history, auditing and migration

For adaptive enterprises, the situational layer cake and the core BPM enterprise repository provide several key benefits, including:

`` Speed of development due to re-use of assets

`` Handling complex liability limitations across jurisdictions with multivariate circumstancing

`` Ability to quickly pilot and activate across lines of business using the layered approach

Figure 6: Situational Layer Cake

CustomerSpecialization

Line of BusinessStandard Offering

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Customer A Customer B Customer C

Elite Service Offering Standard Service Offering Other Service Offering

Pega BPM

Customer Process Manager - (Packaged Contact Center Best Practices)

US & Canada Offering Europe Offering

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Gold Customers Silver Customers

Pilot

7A business is a collection of policies and procedures. Business, IT, and operations need to have full transparency and visibility to all the elements of a complete business solution. An adaptive enterprise needs to provide the same consistent platform as a lingua franca between business and IT. The different platforms for business and IT approaches have simply failed. The platform should allow different portal views depending upon the role of the user.

In traditional development, the business starts with a mandate, and then uses documentation-heavy tools to capture the requirements. The requirement documents are then used or imported as artifacts with a different tool in order to do a high-level, and then a detailed specification or business analysis. This could result in voluminous documentation models or artifacts. At some point, there is a hand off from business to IT. The business artifacts are then exported and imported into other tools to do system analysis, high level design, and detailed technical design. Several modeling and analysis tools could be involved. After a number of exports and imports between tools, they are finally exported and imported into yet another tool to do coding. With the code, there are continuous reviews and changes. This inadequate export and import process and the involvement of many tools is the antithesis of managing change with agility. Soon enough, the implementation is completely isolated from the original business requirements. The code becomes the documentation of what is implemented! There is no round-tripping, and changes have to go through many tools and phases of export, import, export, import. Enormous discipline and effort need to be exerted to keeps all the artifacts pertaining to different tools consistent. That doesnt inspire agility!

We are living in the Web application age. The Web is not just for the business. It is a robust platform for both business and IT. A unified consistent platform should have a single representation for each asset in the BPM solution. Agile enterprises need a thin-client (browser based) unified platform where the business requirements are discovered, captured, automated, and executed with continuous improvements all via a single object model through browsers, with no technically heavy thick client tools involving multiple representations or multiple exports/imports. This should make it easy for business stakeholders, business analysts, and IT to use the same platform and deal with the same element throughout the lifecycle of the asset. Round-tripping is taken for granted: IT, analysts, or the business view and change the same object. At a minimum this means a single cohesive platform that supports business process flows, case types, and a complete collection of rule types, decision management, information model, UI, and integration. In other words all the assets of a BPM application.

Adapt Through Continuous Discovery

8Instead of providing different tools for business and IT, Pega provides the same consistent platform as lingua franca between business and IT. Traditional, different tools for business and IT approaches have simply failed. What is needed is a thin client (browser- based), unified platform where the business requirements are captured, automated, and executed with continuous improvements. Pega calls this methodology directly capturing objectives (DCO). Pega provides a unified environment that automatically generates the solution from requirements without cumbersome hand-offs, import and export, conversion to execution environments, and rebuilding details in multiple environments. Change is fast, but controlled, continuous, but orchestrated.

In order to adapt, the enterprise must be able to close the gap between business objectives and operations. This requires sharing ideas with operations and IT at all stages of a BPM project to ensure that the solution meets business needs. DCO is the set of capabilities in Pega BPM that lets business users capture, organize, and manage information directly in the system instead of using disconnected documents or artifacts that are obsolete before the solution is delivered. DCO helps avoid requirement gaps introduced in custom software projects, where business intent is often misunderstood. Technical changes often obsolete requirements. Long delivery times mean business needs have changed, and the business has no chance to see or provide feedback on the application until it is too late. All these challenges are avoided with Pegas DCO. Business stakeholders can generate documentation for a Pega BPM solution as needed during the life of the solution. DCO helps avoid requirement gaps introduced in custom software projects, where business intent is misunderstood, and technical changes obsolete requirements, long delivery times mean business needs have changed, and business has no chance to see or provide feedback on the application until it is delivered. The first step in building an application profile is to use the Discovery Map a process mapping tool running in the same thin-client environment as all Pega BPM tools to define the base steps for a process.

Figure 7: Directly Capture Objectives (DCO)

9For more information, please contact your Pegasystems representative, visit us on the Web at www.pega.com, or email us at info@pega.com. Copyright 2011 Pegasystems. All rights reserved.

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Application Profiler

The Application Profiler guides the user to capture the following details:

` Overview (organization, project, application) ` Actors (users, services, and agents initiating and performing work in the application)

` Work Types (high-level business functions and the use cases that act against them)

` Interfaces (required connectors and services for external data integration)

` Reporting (types and contents of reports required against the work)

` Correspondence (mail and other materials sent to process participants)

` Assumptions and Resources (project requirements for internal and external resources)

The heart of the Application Profiler is the collection of work types and associated use cases. These drive the definition of the application all the way through to testing and deployment, where use cases guide test scenarios and work types define what work can be performed by application users.

The participants (either a person or a remote system) who interact with the SmartBPM solution are called actors. The interaction between the SmartBPM solution and an actor is called a use case. In SmartBPM uses cases are atomic: each use case represents a single interaction between an actor and the system. The collection of atomic use cases describes the behavior of a work type.

The Application Profiler links captured requirements to the elements that implement the requirements, allowing, for instance, a flow implementing the use case to have a link directly to the requirements of that use case. This aids the

developer by providing a mechanism to trace directly back to the use case requirements. This allows the developer to review requirements and ensure they are fulfilled.

The Application Profiler continues gathering key requirements of the project, including the requirements for the System Interfaces, Reports, and Correspondence. The Application Profiler also collects key project planning parameters, such as the assumptions, roles, responsibilities, tasks, and resources. This project planning information is used to manage the project with the optional Project Management Framework.

Following the completion of the Application Profiler wizard, a preliminary high-level project scope document is automatically generated. Now Business and IT can access the high-level objectives right within the system.

The Application Profiler can be run again as project requirements are augmented or changed. When any aspect of the profile is updated, use cases, the proposal document, and project estimates are immediately updated for all team members to use.

PrerequisitesApplication Profiler is part of SmartBPM version 5.4 or higher.

The Discovery Map lets business teams sketch a base process, and then flesh it out with activities, sub-processes, and decisions to any level of detail. The process is always live and can be immediately shown as a Visio-based process flow and test-run so users and designers can collaboratively work to get it right.

Additional information such as actors and use cases may be attached, and blocks can be quickly added or rearranged to reflect the best thinking on the process.

Business stakeholders can generate documentation for a Pega BPM solution as needed during the life of the solution. Because this documentation is generated directly from the rules at any time, it is always up-to-date and in sync with the actual implementation. With Pega BPM, all aspects of business requirements are modeled and executed directly in the shared environment. Pega provides visual forms and wizards to define use cases, processes, and decisions in business terms. Pega BPM provides a unified, powerful, and easy-to-use Web-based environment for business owners, analysts, designers, and developers. Business and IT use the same platform and nothing is lost in translation. Changes and prior versions are automatically tracked, and access and change permissions are controlled for complete application governance.

Figure 8: Application Profile

Application ProfilerDirectly Capture High-Level Objectives

Features ` Guided work-centric process to collect information at the beginning of a project.

` Handles details of application objectives, including use cases as core of requirements definition

` Creates an Application Profile document that can be reviewed by Project Sponsors

` Information collected provides the basis for the application built in PRPC

` Works with other Agility Accelerators to drive requirements throughout development and deployment

Benefits ` Easy to use by business users and analysis initially and for changes quickly generates consistent, robust requirements

` Provides complete details to ensure nothing is missed, allowing accurate and timely estimates, development, management, and deployment of a SmartBPM application

` Documentation supports review, understanding, and justification for applications

` A complete and consistent set of tools allows all project participants to work efficiently, shortening time-to-market

The Application Profiler is a business friendly wizard that allows business process owners and business analysts to directly capture requirements and objectives into a SmartBPM solution.

The Application Profiler uses a guided approach to build an application profile, which is a document and a collection of rules in SmartBPM that capture use cases, actors, and objectives for a SmartBPM application.

The application profile is used to produce a proposal document (the last step of the Application Profiler), supply work types, roles, integration, etc. to create the application itself (in the Application Accelerator), and to provide use cases and project estimates for testing and project management (using the optional Test Management Framework and Project Management Framework). Together these tools act as agility accelerators for the life cycle of a SmartBPM application, reducing development time and effort by 30% or more, and providing excellent visibility into the development process. The Application Profiler implements best practice for requirements gathering, enforcing a consistent approach across an organizations SmartBPM applications. With the high-level objectives captured through the Application Profiler, a business/IT implementation team collaborates to develop and deploy an application based on the shared objectives.

CROSS INDUSTRY

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Adapt Through Automation and Dynamic Cases

In real-life business applications, what is being executed are actually cases, not individual or often siloed process flows. Many times, the processing of the actual case is done manually. With manual case processing, it is usually difficult to extend a case, run additional process fragments, handle unstructured processes, or visualize the case hierarchy. BPM addresses this through automation. Traditional BPM approaches with structured processes tend to be pre-determined and cannot adapt. The swim lane is perhaps the most ubiquitous representation of traditional production processes, where each of the lanes indicates a participant or a party; you have tasks, you have events, and the most important thing is that the sequencing of the tasks is rather rigid and predetermined. Enter dynamic case management, which can handle structured processes, of course, but also unstructured processes and ad-hoc changes.

A case is the coordination and collaboration of multiple parties or participants that process different tasks for a specific business objective. Some of the tasks will be planned in predetermined process flows. The tasks are most often unplanned. Cases are therefore dynamic, adding or changing any of their elements. All of these coordinating tasks in the case are for a concrete business objective or goal. Cases are dynamic and respond to and generate events. In fact, if we were to look at the anatomy of a typical case, as illustrated in figure 9, the case will have a hierarchy. It will have subcases. Various tasks will be executed in the context of the parent case or one of its subcases. The coordination of the tasks is organized in a case hierarchy (subcases). Cases will always have a subject. Often, the subject is a human such as a customer, patient, or recipient of welfare. A case, however, can be almost anything, such as a claim. The case will also have a business objective pertaining to the case subject. There will be a lot of collaboration between various case workers to resolve the case. While processing these tasks, a case will have content, often from multiple enterprise information or content management repositories. Cases generate and respond to business events. Sometimes, events need to be correlated and the cases will have service levels. There will be different types of policies and business rules, such as decisioning rules, expressions, decision tables, and constraints, which are associated with the case. Cases also have data properties pertaining to the parent case or subcases, and multiple process fragments executing in the context of the case hierarchy. Dynamic cases can adapt when requirements, behaviors, circumstances, or events change.

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Pega is the leading Dynamic Case Management platform At its core, it supports adaptive automation of work. Pega BPM supports work automation in business applications. It coordinates activities through routing work to the best skilled or available resource, and obtaining, when needed, information from external or internal sources. Pega BPM supports all types of work: clerical workers, knowledge-assisted workers, and knowledge workers. Leveraging the Situational Layer Cake, it dynamically tailors each task or interaction of the case. It supports the dynamic case contents. Furthermore, the business application keeps track of all the business activities and allows business users to drill down to control the performance of their solutions. The solution will respond to customers and interested parties with automated correspondence to minimize effort and ensure consistency. Pega BPM supports the definition, subscription, and robust handling of business events. The case work gets resolved by automating where possible and guiding users when user involvement is needed.

Figure 9: Case Hierarchies

Case Workers

Subject

Subject

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1 See for example: http://www.pega.com/forums/dynamic-case-management/forrester-ranks-pega-1-in-dynamic-case-management-what-does-dynamic-c

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Specifically, Pega Dynamic Case Management supports:`` Design and Build Complete and Holistic Case Management Applications:

Pega provides a robust case type designer. The case types with their subcase relationships can be defined intuitively through a visual hierarchy. Additional elements that could be defined include attachment types, service levels, and starting as well as ad-hoc processes or tasks that could be instantiated in specific cases.

`` Run-Time Case Worker and Case Manager Portal: This provides complete visibility and control of the cases with fast access to case hierarchy, the case tasks, the related cases, the history of their own and related cases, and upcoming goal and deadline events. Users can drill down to the details of the case and have a 360-degree view of the case and its subjects. The portal also contains social collaboration constructs, such as a case wall to share what is going on with the case or its elements. The case portal also provides complete actionable business activity monitoring of the various cases that are owned by the manager.

`` Enterprise Content. Case workers can include documents and data from multiple internal and external systems. Pega supports the CMIS industry standard to include documents from potentially multiple ECM systems in the case. Pega also supports document attachments with versioning.

`` Business Events: Case managers can create events and subscribe to them. The creation of the event can specify the name, description of the event type as well as various conditions for the event. The event will happen when the conditions on the event are satisfied. Events also have an action part for instance executing an activity. Subscribers to the event can be notified through a number of channels, including e-mail and text messages.

Figure 10: Case Management portal

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Continuously Learning Through Adaptive Decisioning

Companies that have automated decisions as part of a BPM solution can, in some circumstances, opt for a system of continuous learning and adaptation. In such a system the decision strategy (a combination of predictive models and specialized rules that effectively exploit their predictions) is one that learns from its mistakes and automatically corrects and improves itself. Popular use, for instance, is where BPM solutions are used in the CRM or Marketing space where the decision strategy adapts to changes in customer behavior or market dynamics. Customer behavior can change because of demographic trends, legislation, interest rate, or a myriad of other factors. Similarly, competitive offers or pricing can stir up things and impact how customers behave. Rather than trying to re-calibrate predictive models manually forever testing when such models get less accurate, then develop updated versions adaptive systems will update automatically without human intervention.

This capability is available in domains where the link between decisions and the feedback about the quality of those decisions is unambiguous and the time lag is relatively short. That is, its possible to give the system a slap on the wrist when it makes a bad decision (and reward it for a good decision) and the slap follows the bad decision in quick order. In such domains (and there are many that qualify, also outside CRM and Marketing) the BPM solution becomes proactive, not retroactive as is mostly the case. Its one thing to effectively measure that change is needed, and then being able to make that change in an agile manner; its quite another to fully automate this process and have the improvements implemented by a self-learning, adaptive system.

In Walker & Khoshafian, 2011 three stages of predictive guidance of processes were distinguished. In stage one, process decisions were based on traditional rules, for instance expressed in a decision table. Such processes can include complex expressions to govern flow, but those expressions cover explicit, discrete, and static knowledge, say from a company handbook. If a prediction is used in such an expression, it is as a static reference, as in if customer.likelihood_to_buy > 0.75 then sell else continue. Stage one is the realm of traditional BPM.

In stage two, the reference to a prediction is replaced by an actual predictive model. This changes the dynamics considerably as the prediction is now on demand, calculated at the time of the decision, as in if likelihood_to_buy(customer) > 0.75 then sell else continue. The predictive model (or formula) that calculates the likelihood to buy could perhaps still be expressed in a decision tree but in this case the decision tree has been discovered, i.e. inferred from historical data, and is not defined in a handbook. Stage two is called predictive BPM.

In stage three, the predictive models are not just inferred from historical data but learn continuously from correct and incorrect predictions. The flow decision would now look something like if likeli-hood_to_buyadaptive(customer) > 0.75 then sell else continue. In this case, two subsequent invocations of the rule that contains this decision might result in a different flow even when the data used in the decision is exactly the same. It is this variant, called adaptive BPM that will be explained in more detail in this section.

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An altogether different approach is using so-called adaptive (or self-learning) models. Instead of looking at a snapshot of data, they look at a moving window of data as it enters the adaptive system. Such predictive models are always up to date and never get tired. If the quality (i.e. predictive power) of adaptive predictive models versus static predictive models is comparable, it would seem that adaptive models are the better option. True as that may be in many situations, adaptive techniques have three characteristics that make them not universally superior.

The Mechanics of Adaptive Modelling

To understand at a basic level how adaptive techniques work, consider a BPM scenario that involves three choices in a flow: black, white and green. Instead of some form of decision table that governs the selection, an adaptive model will be used. This (trivial) model uses only two case attributes: customer age and customer gender (this may be some CRM process). It will also be as assumed that it can be instantly (or at least quickly) established whether a choice for any of the three directions was the right one.

The colors are chosen to make what follows generic examples of choices within process flows. For a retail bank, for instance, the actual choices supported by adaptive models could be:

`` Black: offer a Platinum card;

`` White: offer a Gold card;

`` Green: offer a Silver card.

Note that choices can also be at a higher level (e.g. whether to best pursue sales, collections, or retention for this customer), or at a more granular level (e.g. what further incentive to use while offering the Platinum card).

To start approaching any of the above examples with adaptive techniques something like figure 12 below is created first. It captures the underlying attributes, age and gender in this case, and will maintain a count of wrong and right predictions/decisions for each of the three choices we are interested in. In reality of course, the number of attributes on which the decision is based will be much larger than two and the number of possible choices will likely exceed three.

START

GO BLACK GO WHITE

GO GREEN

IF

Figure 11: BPM Scenario

2 Walker & Khoshafian, Doing the Right Thing: The Use of Predictive Analytics in Business Processes, 20113 But many predictive models would be quite complex and non-linear, and could not (easily) be expressed in a decision tree (like a very simple parabolic formula could only be approximated in a decision tree or table).4 More about that later, but the predictive performance is usually equivalent.

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Note how, for the age attribute, the different age segments (40) do not look arbitrary. They are not and this so-called grouping is dynamically calculated and adjusted, and part of the adaptive algorithm. The three adaptive models (one for each of the choices) can use various techniques to calculate the probability from this kind of table. In this case, where all the counts are equal, the three choices/decisions can be right or wrong and theres no evidence to favor any of them.

Lets assume a 60 year-old woman is impacted by this process and that a (currently random) choice is made to go with Green. Perhaps the Green process will wind up proposing to this customer a session with a wealth advisor. Consider the scenario where the woman accepts this proposition. This causes the table to be changed accordingly:

Figure 12: Tallying good and bad predictions based on Gender and Age

5 Bayesian Networks are often used.

Figure 13: Successful (Green) prediction for woman over 40

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With corresponding changes in the likelihood of going with Green for a woman over 40:

The probability of the Black choice being right remains, without evidence, 50%, and the same for going with White. But Green is now considered to have a 72% likelihood of being the right choice. But this is not the only effect! The adaptive system has now seen (very limited) evidence that women may favor Green and (very limited) evidence that customers over 40 may favor Green. Therefore, the case of a 50 year-old male will also show a slightly increased chance of successfully being resolving through the Green choice (57% per figure 15), but not as high as for a 60 year-old woman (72% per figure 14).

An enhancement to self-learning is time windowing. It is essentially the size of the memory of the adaptive models. If its small, the models will be faster to forget older responses (to their predictions) and therefore follow short term trends very acutely at the expense of longer term trends. Their predictions will be more opportunistic as they are more susceptible to spikes in customer behavior (still staying with marketing) while models with a larger memory will be slower to respond to new behavior and factor in more of the past. In practice, approaches may be used (e.g. champion/challenger as described later) that combine both short term and longer term models. Its then a (real-time) business decision whether, for a particular prediction, the fast learning (and unlearning) model or the slower one should be used.

Its important to note that all the algorithms referenced here are designed to be very scalable and support hundreds or thousands of adaptive models and billions of annual propensity calculations. As learning and predicting are separate processes, with learning potentially asynchronous, very large decisioning volumes can be handled with the right architecture.

The previous section described the value proposition of adaptive decisioning. In this section we delve deeper into Pega BPMs features in supporting adaptive decisioning.

Figure 14: Likelihood of success for choices concerning a woman over 40

Figure 15: Likelihood of success for choices concerning a man over 40

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In the example in figure 16, a simple decision strategy is shown that uses three adaptive models in the yellow shapes. Each model is trying to predict the success of the corresponding action (in the blue shape): Black, White, or Green.

In the top section of figure 17 a few details on one of the adaptive models are shown. It deals with the memory span of the models, i.e. after how many responses should the model start to forget previous experiences. In this case the model will forget its first prediction (and the corresponding customer response) when the 251st response comes in.

The second section in figure 17 configures when and how the input data to the adaptive model (age and gender in the example used above) need to be re-analyzed to potentially adapt and optimize the grouping (i.e. 40 for the age attribute).

The last section configures the so-called performance memory: to be able to compare different adaptive models with different size memories, there has to be agreement on the number of cases on which the predictive performance (i.e. accuracy of the predictions) is calculated.

The green shape in figure 16 is prioritizing the different actions, in this case, based on the likelihood of success (.pyPropensity in the screenshot). Its this last shape in the sequence that effectively deciding on the best choice reports back to the process on how to proceed. In figure 18 the metric is defined that decides on the best choice. In figure 18 its simply the action with the highest (i.e. top 1) probability that is chosen, but the field marked Priority can be used to define a more complex expression as well.

Its this final prioritization that, in the process flow, implements the decision about which direction to choose: Black, Green, or White.

Figure 16: Simple decision strategy

Figure 17: Adaptive model configuration

Figure 18: Configuring the priority

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This sample process flow in figure 19 runs the decision strategy discussed above and then, based on the outcome, pursues a Black, Green, or White action, whatever those may be. When the customer response to the chosen direction is fed back to Pegas

Adaptive Decisioning Manager, the adaptive models (also the two that werent picked to drive the flow) will learn from their successes and failures to predict the right course of action.

Figure 19: Decision strategy in process flow

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Adaptive Through Champion Challengers

The previous section described how adaptive decisioning can continuously learn and adapt through modifying the propensities of next best actions dynamically. Adaptive decisioning is a very powerful decisioning technique that learns from the responses and behaviors of customers to optimize decisioning. Strategy designers often have a champion strategy that becomes the source of the next best action options with various probabilities or propensities. The champion is their favorite deployed strategy. The champion strategy could be based on predictive models, adaptive models, scores, or other decisioning strategies. This champion is the current envisioned optimal option based on intuition or experience. In the presence of multiple decisioning strategy options, it does not necessarily reflect the true optimal strategy for a particular automated process solution. So there could be challengers to the champion strategy. For instance, the champion could be a predictive model and challengers could include an adaptive strategy and a score. It could very well be the case that one of these challengers is a better decisioning option and perhaps the true optimal strategy. More importantly, as processes get executed and customers behaviors change, the champion strategy could become obsolete. It needs to be replaced by another new champion. These two complimentary objectives drive the need for adapting to new champions through champion challengers.

The champion challenger approach therefore involves a champion and a number of challengers. The underlying decision engine selects the champion most of the time, but also occasionally tries out and selects the challengers. At design time, the percentages for the various choices can be set by the strategy designer. For instance, as illustrated in figure 20, the champion will be selected 60% of the time. The other challengers will be selected 25% and 15% of the time respectively.

Champion: Predictive Model

Select Champion: 60% of the time

Challenger 1: Score Model

Challenger 2: Adaptive Model

Select Challenger 1: 25% of the time

Select Challenger 2: 15% of the time

Record Responses and Results

Change the champion

Yes

Should the Champion be replaced by a challenger?

Figure 20: Champion/Challenger scenario

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The responses will be recorded. These responses, for instance customer choices based on the champion or one of the challengers will be recorded in a database. As time passes, the strategy designer could analyze and decide that one of the challengers should become the new champion. The champion challenger strategy map will then be updated and the adaptive cycle continues.

More than technology, this mechanism for continuous experimentation is a methodology for corporate agility, change, and instrumented second guessing. Especially for strategies that rely on assumptions, each assumption should be challenged: size, placement, tone of voice and other attributes of a web banner; product pricing and incentives; or, at a higher level, entire customer strategies. Even if empirical evidence is used rather than assumptions, for instance a (non-adaptive) predictive model, the environment is changing and processes and decision strategies thus need to be challenged and, where the challenge is successful, adapted.

In figure 21 a champion/challenger rule selects randomly from, in this case, three different methods to predict the financial risk associated with a loan application. The top yellow shape represents a scorecard, which assigns points to customer attributes to arrive at a score. The middle is a decision table to express the relationship between customer attributes and risk of default in a different way. Lastly, the bottom shape indicates a predictive model where no assumptions are made about risk but where the probability of default is automatically inferred from empirical evidence.

The champion/challenger rule in the red shape makes a random weighted selection from the three models. As illustrated in figure 22, the weighting is 10%, 30%, and 60% of the cases. Over time, detailed reports will make clear which challenger, if any, is worthy of becoming the new champion. The choice of three is arbitrary as is the level at which to challenge; instead of picking from three different types of risk models, entire strategies can be challenged.

Figure 21: Champion/Challenger strategy

Figure 22: Weightings for Champion/Challenger

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Adaptation Through Simulation and Optimization

Above, various approaches have been discussed that allow an organization to swiftly adapt processes to changing goals or circumstances. This section investigates how some of this can be achieved in anticipation of actual changes to processes or even the need for such changes. Take aviation. A pilot can adapt his or her landing skills by practicing various approach scenarios in a flight simulator and thus acquire the best practice. The process is still about landing descending, making the approach, touch down but the decisions, when and how steep to descend, when and how fast to turn, etc., can be honed to perfection. Similarly, what-if business scenarios can be defined and tried out on historic (or manufactured) circumstances so changes to decision strategies can then be tried out to find scope for improvements. This can be a very effective way to adapt to changing circumstances; simulate various strategies on real data and then commit to the strategy that delivers the best outcomes in the business simulator.

Note that this kind of simulation is not a simulation of the process to judge its efficiency, but a simulation of the business that runs these processes to judge effectiveness. This kind of simulation is not designed, therefore, to prove that a certain process meets a specific Service Level Agreement 95% of the time, but to investigate, for instance, how many more products would have been sold (and at what opportunity costs) if certain decisions embedded in the processes are changed.

This approach is only safe and meaningful when 1) the strategies being simulated are the real strategies (not a spreadsheet approximation), and 2) the data they are simulated on is the exact same data the actual strategies are (or have been) applied to. Lets consider a scenario where a bank is communicating with its customers across multiple channels about a variety of products and services. As earlier examples in this paper have also suggested, this is an area where adapting to the ever-changing whims of the customers and market dynamics is a real challenge. Assume our hypothetical bank is executing processes and decision strategies and assessing the outcomes. Outcomes, in this context, could include, but are not restricted to:

1. The volume of Platinum cards successfully offered to some customer segment in some channel;

2. The agents in the call center that have (not) been successful in converting a Platinum card offer into a sale;

3. The effect of the Platinum card strategy effect on revenues, risk, customer satisfaction, etc.

4. The effect of the Platinum card strategy effect on the sales of saving and loan products.

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Resorting to traditional Business Intelligence (BI), many organizations could probably get answers to 1 and 2, albeit most likely well after the fact. That delay in itself means its not possible to quickly adapt, should the answers to 1 and 2 not meet expectations. The answer to 3 may look deceptively simple at first glance but 4 suggests that the answer may not be that straightforward: what if the Platinum card strategy does indeed add $5 million to the bottom line but at the expense of product B? What if product B could have generated $7.5 million? This is where the business flight simulator comes into play. Suppose this bank could simulate what would happen if product B would have been part of the mix. It would be like a hindsight champion/challenger. Imagine this works across multiple strategies (for B, C, D, etc.) and it could also be used in the same situations where a champion/challenger approach would be advisable: challenging a price, an incentive, a form factor.

These two approaches, champion/challenger and simulation, are actually intertwined. Using champion/challenger approaches, the bank, in this case, would automatically create data points for various scenarios: offering B instead of the Platinum card, offering B with a lower interest rate, etc. Those data points in turn are useful when business simulations are ran of strategies that could have been in place but werent. Equally important in this respect is the use of predictive analytics. A propensity model predicting the likelihood of a customer accepting an offer can also be used in circumstances where in practice a different offer was made. Changing the eligibility criterion for a Platinum card from, say, an age of 21 down to 18 could still be simulated even when no 18 year-old has ever been offered a Platinum card before. The reason for this is that the propensity model (or credit risk model, for that matter), looking at many more attributes than age alone, might still be able to make a reasonable estimate as to the likelihood of an accept (or default). In this example the bank might be able to approximate the business implications of changing the minimum age of a Platinum card holder from 21 to 18 years old.

Organizations need to move to a level where their operations are virtualized: captured in process designs and decision strategies. Only then will it be possible to simulate change and adapt to circumstances that have not even happened yet. Less perfect, no doubt, than a flight simulator where aviation physics are supposedly a known quantity, but a big step up compared to trial and error. After all, hope is not a strategy.

Figure 23: Processes and Decisions

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Only a unified environment can ensure seamless collaboration between processes and decisions. Both can only be understood, or simulated, in close conjunction.

Take a loan origination process; the process flow is intertwined with, but independent of, the decision strategy that determines which customers are eligible for a loan based on their risk profile. The designers of the process flow need not know, are perhaps not even allowed to know, about the risk models that drive the flow. Certainly the expertise required to define the flow is very different from the expertise required to design the risk models. Change cycles are also likely to be different. The process flow may change whenever front- or back-office systems change, when actors in the process are added or removed, when different SLAs are put in place, etc. In contrast, the risk decision strategies will change when the risk appetite of the bank changes, when the risk profile of customers and prospect change (for instance due to economic circumstances or the implications of new tax laws), or when the central bank imposes different exposure requirements.

In Pega BPM this observation is used to support a simulation environment which keeps the processes static while offering deep simulation of (or variants on) decision strategies. Consider the cockpit of the bank below:

Figure 24: Visual Business Director (VBD)

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In this particular view, this Pega cockpit allows the bank to see, in real-time, how each of its products is doing in various customer segments in terms of acceptance rate. In the actual Pega product, the customer dimension can be changed to channel or any other system that implements decisions; the proposition dimension can be changed to processes; and the business metric can be changed from accept rate to any metric that can be defined in terms of the decisions taken (six of those can be seen on the back panel).

But what if this was not a cockpit but a flight simulator? Where the banks cards manager, rather than simply boosting sales for her silo wants to look first at the consequences of doing so at the corporate level? Or perhaps the overall sales manager would like to have that view before allowing her to make change. In that case, instead of deploying the new marketing weight (and any other changes she may have made), she may want to simulate her changes first.

Figure 25 shows the three steps in the simulation process: simulation, consolidation, and deployment. First the simulation itself. This involves all the cards managers changes to the strategies (an increase of a marketing weight parameter in this case) to be applied to a chosen historic timeframe. The simulation will re-run all the predictive models, rules, and decisions on the exact same data to which they have been applied during the selected time window. The results can be presented in many ways within Pega BPM but one variation is to look at the deltas of the orginal outcomes and the what-if outcomes as a result of the simulations.

Figure 26 shows, using cones instead of bars to emphasize this delta view, that the cards manager achieved the effect (in green, positive cones) of boosting credit cards sales. Quickly zooming in to the particulars reveals that, as expected, the platinum

Figure 25: Simulation in VBD

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Figure 26: Emphasizing deltas in VBD

card is causing the increase (across both the call center and web channel). Because we are looking, in this case, at the accept rate and not just volumes, this does actually indicate that customers appreciate the offer. So whats not to like? The bigger picture is whats not to like. Within the cards category, the boost of the platinum card shows no significant effect on the sales of other cards (presumably because the eligibility criteria makes them mutually exclusive to some extent). But one level higher, shown above in figure 26, the negative effect on other product sales is huge. This unified view, very hard to come by using general purpose BI tools, shows how the outcomes for mortgages, loans, and insurance products will take a big hit if the cards manager gets her way. Part of this can be opportunity costs; pushing the platinum card during any possible interaction on the Web and in the call center leaves only suboptimal channels to push other products. Apparently, in those channels the accept rate is not nearly as high as in the inbound channels. But this can also be caused by more subtle dynamics. Perhaps a less expensive credit card leaves customers more spending room to buy other products. Its only a very granular simulation that can show these cannibalization effects that will surely prompt a corporate-oriented cards manager (or her manager) to pull back on the platinum card throttle.

Reality, however, is a bit more complex than the above suggests. The cards manager may not have been the only one running simulations. Perhaps the mortgage manager is doing a similar thing. The call center manager may be interested in forecasting

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volumes over the coming months (see figure 27). To approximate the combined effect of all those changes the various simulations need to be consolidated for an overall picture. To ensure this is reliable (and different simulators dont flex the same levers at the same time) the simulations are applied after another and only one simulation can run at any one time.

This process is essentially adapting the virtual bank to historically observed circumstances. Once the consolidated results usually reflecting a compromise between the various stakeholders that maximizes globally agreed business objectives are to everyones liking, the adapted strategy can be deployed into the live system for instant change across all connected processes. At that moment, the real bank will adapt to mirror the decision strategies of the virtual bank, i.e. the banks flight simulator. This approach of planning, monitoring, simulating, consolidating, and changing the way the organization does business is an example of an adaptive enterprise making informed course corrections to stay ahead of the competition in a world of change.

Figure 27: Forecasting volumes in VBD

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Conclusion

Pega is the industrys leading platform that is holistic in its support of adaptive enterprises. Adaptive enterprises need to support change continuously. The change requirements emanate from a plethora of sources and/or reasons. Changes are also of different granules: simple policy changes to complete innovative solutions in target markets or sectors.

The seven dimensions for adaptive enterprises can be summarized as follows:

`` Continuously monitored and optimized measurable business objectives: Pega supports business activity monitoring, KPIs, and perhaps more importantly the ability to drill down from measurable results to executing cases to effect change.

`` Continuous innovations through re-use and specialized, contextual and situational solutions. Pegas unique and patented situational layer cake allows adaptive enterprises to achieve two key objectives that sometimes conflict: re-use or sharing of solutions while easily specializing for contexts involving geographical locations, specific services, or customer types.

`` Continuously discovering requirements with iterative enhancements: Through Pega, organizations can easily and directly capture the business requirements in one cohesive tool. Pega is a complete platform: business processes, business rules, case type information models, user interactions (or UI), strategic decisioning, and integration with custom or legacy solutions within the enterprise.

`` Continuously improving automated work and dynamic case management solutions: Pega supports the whole spectrum of work and workers. In addition to structured/predetermined or planned workflows, Pega supports unplanned, collaborative dynamic cases. This provides visibility to the entire case, the case subject, the case content, the case tasks, and case objectives. More importantly it allows case workers including knowledge assisted workers to dynamically discover process fragments, tasks, or to change case hierarchy as well as content.

`` Continuously gaining insight by learning and automatically adapting decisions: In its dynamic cases and processes, Pega can embed self-learning predictive models to drive flow choices or other decisions. Learning from success and failure to predict the right course of action, decisions that embed such models are continuously optimizing themselves in response to change. This built-in calibration objectively course corrects the dynamic case management solution and in addition, greatly reduces the dependency on humans for supervision and change.

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`` Continuous adaptation by challenging champions: Pega methodologically supports the challenging of decision strategies at any level of granularity by one or more alternative approaches. This encourages an innovative culture of continuous experimentation where every approach is challenged and replaced when the alternative is better. The result is an adaptive system that can respond quickly to changing circumstances.

`` Continuously monitor, simulate and control decision priorities: With a built-in feedback loop, Pega allows the effectiveness of decisions to be tracked in real-time and instant tactical changes to be instantaneously implemented whenever a course correction is required. Ahead of such a correction the business impact can first be simulated by applying the freshly adapted strategies to the actual, historic decision input data.

These seven dimensions combined and unified in one robust adaptive platform helps organizations become adaptive enterprises. With Pega, Fortune 500 companies in various industries have achieved tremendous business results including generating increased revenue, optimizing the customer experience, and continuously improving efficiencies. But more importantly, Pega helps organizations transform themselves to adaptive enterprises, with continuous iterative improvements, through thinking big but yet starting small with incremental wins.

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About Pegasystems

Pegasystems, the leader in business process management and software for customer centricity, helps organizations enhance customer loyalty, generate new business, and improve productivity. Our patented Build for Change technology speeds the delivery of critical business solutions by directly capturing business objectives and eliminating manual programming. Pegasystems flexible on-premise and cloud-based solutions enable clients to quickly adapt to changing business conditions in order to outperform the competition. For more information, please visit us at www.pega.com.

Copyright 2012 Pegasystems Inc. All rights reserved. PegaRULES, Process Commander, Pega BPM

and the Pegasystems logo are trademarks or registered trademarks of Pegasystems Inc. All other product names, logos and symbols may be registered trademarks of their respective owners.

2012-5