The new​ paradigm of process transformation

How can your IT Manager learn to stop worrying and love the bot? Most of the enterprise automation projects today are driven by human intuition. These initiatives are driven by consultants, vendors or IT partners and end up in ‘automation surprise‘ scenarios after deployment. Companies deploy automation technologies to realize substantial performance gains. They often fail to consider a data-driven process centric approach to automation, instead, they rely on personal interactions with SMEs.

In the current Robotic Process Automation (RPA) implementations, organizations attempt to thrive in the ‘Automation First’ era by automating anything and everything to take lead in their digital transformation journey. This approach has resulted in bot complexity as well as process complexity. The common questions IT executives receive in their automation journeys are: It takes 12+ hours to complete a process by this bot, Why? Well, the explanation could be “The underlying business process is too complex”. How about modernizing the process? Have you thought through the change management on automation? Where is the transparency? Have you assessed the bot risks? Have you considered human emotions? These automation efforts often end up in automation surprises and furthermore, they are not able to scale and have the inability to accurately measure the Return On Investment (ROI). The main reason is due to the lack of data-driven assessments, process transparency, underestimating change management, risks and lack of awareness for continuous improvement.

How do you achieve process transparency?

Process discovery helps to blueprint the existing as-is processes by leveraging techniques such as process mining, sequence mining and activity modelling. This has proven to be a true benefit to enterprises enabling data-driven decisions for their automation journey and process transformations. Often, the implemented processes do not just happen in your transactional systems but outside as well in the form of emails, documents, conversations, IT tickets etc. Combining the insights from both business applications and manual activities provide improved process transparency for process transformations. Some quick wins include:

  1. Identifying the right process for automation
  2. Bringing transparency in change management
  3. Improving the process complexity upfront and automate
  4. Providing insights into the complexity and eliminating automation surprises after deployment

The below illustration demonstrates the advantages with traditional RPA combined with process discovery techniques to enable a true data-driven process transformation.

Bringing automated Intelligence to Enterprise. Live Objects Process Transformation platform.

Having a process discovery platform helps with quick implementation as well as continuously monitoring the implemented robotic workforce, to verify if the automation is effective. Some immediate benefits include:

  1. Scale automation organically and efficiently by accurately measuring the ROI and improved efficiencies.
  2. Modernize the processes, bring cultural changes in the organization than blind automation

Make your IT Manager love the bot & stop worrying

Live Objects, a closed-loop business process optimization platform helps generate process maps from structured as well as unstructured sources. The platform leverages a deep neural net based AI engine for recommending ideal steps for eliminating process bottlenecks such as automation, elimination of redundant process steps and addition of new process steps.

Live Objects provides true end-to-end process discovery by following the digital footprint of actual processes and performance measurement across business applications & manual activities through AI-based sequence mining and activity modelling.

RPA alone will not help organizations in their automation and process optimization journeys, organizations also need to consider that a fair amount of business process re-engineering will be required to help RPA scale and be efficient.

Interested in learning more on how Live Objects is bringing automated intelligence to enterprise, reach out to us for a demo.

Internal Audit: Using process discovery to test for process variations

Are your process narratives and SME interviews still effective?  How does your internal audit function consume the volumes of big data and complex business processes produced from today’s technologies?  Is your internal audit team’s skill set staying ahead of your enterprise digital transformation?

With the rapid pace of enterprise digital transformation, increases in data volumes and complexity of systems and business processes, it is imperative that internal audit functions and skillsets stay ahead of this trend.  In fact, PwC’s 2019 State of the Internal Audit Profession Study identified six habits to enable a more “digitally fit risk function”.   Of these, three key areas include enabling an organization to act on risks in real time, upskill talent, and finding the right fit for emerging technologies.  These are all possibilities now, read on… 

Getting to process flow visualization with volumes of data…no problem 

Imagine dropping into your business processes and tracking them through to a process visualization, like following a trail of data breadcrumbs to a well-baked remarkable end result.  This can be achieved through process mining (process discovery).    

Process discovery leverages techniques found in data mining, process management and analysis to extract information readily available in enterprise systems’ event logs to discover, monitor and optimize business processes.  This helps complement the internal audit function allowing them to understand, visualize and analyze workflows and risks across business processes such as Procure To Pay and Quote To Cash as well as drill down into revenue management scenarios such as revenue leakage and billing.  Furthermore, process discovery spotlights areas of risk or deficiency by comparing actual visualized processes and more importantly, process variations against the standards originally set by the enterprise. 

What do you do with Implicit or Inferred Processes? 

Oftentimes, business processes and decisions sit outside of structured systems.  What happens then? For example, how does an auditor assess the risk in legal contracts, embedded excel calculations and macros, and supplier purchase orders – these are all part of a business process but the data or decisions are not always captured by an ERP.  Advances in machine learning and natural language understanding (NLU) have helped businesses take traditionally unstructured data and transform it into structured data.  This in turn, allows internal audit functions to effectively understand and analyze these implicit processes for risk and compliance. 

AI-enabled process discovery and unstructured data discovery are technologies available today to any internal audit function looking to stay ahead of their enterprise’s digital transformation programs.   Live Objects can provide the process understanding coupled with the much-needed agility for internal audit functions to proactively expand their scope of testing (for completeness), properly assess for deviations and anomalies in business processes (by following the data), as well as monitor (in real time) any material variations in workflows and approvals, from the standard operating procedures, in an unbiased data-driven approach.

The Hive CFO Forum Workshop Series: Record to Report Optimization with AI

As part of their quarterly CFO Forum Workshop series, The Hive Think Tank held their second workshop, co-sponsored by Live Objects, on May 22 and May 23, 2019, on Record to Report (R2R) Optimization with Artificial Intelligence (AI). This workshop explored the emerging role of AI, automation and process optimization in Record to Report and specifically, applications in month-end close activities including transactional processing, sub-ledger aggregation, analysis and adjustments, corporate close and consolidation through to management and financial reporting. The workshops were held in San Francisco, which was moderated by Caroline Abadjian – Principal, Deloitte, and in Mountain View, which was moderated by David Cutbill – Principal, Deloitte. The participants of the workshop were Finance and Accounting leaders in diverse industries. They shared their experiences, best practices and challenges in accelerating the month-end close whilst maintaining and potentially improving the quality and accuracy of the financials. Furthermore, the participants shared their journeys in adopting digital technologies in their Finance Transformation programs – the experiences were varied.  This blog describes the key insights and challenges that came from the session. The session explored the challenges in the month-end close process across a broad spectrum of industry verticals represented by the panelists in the workshop.  These challenges emerged in the following four broader themes:

  • AI applications in Finance Transformation and how to start your AI journey
  • Transaction Processing and Sub-Ledger Close
  • Analysis and Reconciliations
  • Close Consolidation, Management, and Financial Reporting


AI applications in Finance Transformation and how to start your AI journey

Advanced digital technologies and AI can support enterprise finance functions through (a) Automation – e.g. taking manual processes and repetitive processes and enabling technology such as RPA (robotic process automation), (b) Validation – e.g. applying business rules, thresholds, and historical trends to support reviews and reasonableness tests normally conducted by Finance professionals, (c) Optimization – e.g. helps look for and fix inefficiencies such as redundancies, bottlenecks, delays in business processes as well as the ability to suggest new content or configurations.

Participants were curious about how and when to enter into the AI journey. Namely, what types of practical AI applications are being implemented by Finance organizations.  Two important rules of thumb are to (a) know what KPI target you are trying to aim for and (b) to fully understand where your company lies on the maturity cycle.  In other words, if it is for cost reduction, then automation may be the better entry point.  However, if you are looking to streamline and scale your processes, and deliver improved operational, business and customer experience outcomes, then optimization may be a viable option to explore.

A practical example of automation includes the use of RPA bots to accelerate data engineering efforts such as the preparation of month-end close excel workbooks with populated source data on business day one for the accounting team to begin their reconciliations.

Participants who were in the beginning stages of their AI and automation journey discussed how they determined where to dive in. Some practical advice shared among the participants included picking a few areas to do a proof of concept (proof of value) first to show the immediate value and then expanding that to other use cases. To understand automation candidates, it is important to get a deep understanding of tasks performed, which includes getting an understanding down to the click level.


Transaction Processing and Sub-Ledger Close
The participants unanimously agreed that the sales cycle with it typical “hockey-stick sales structure” that tends to see deals closing on the last day and even last hour of the month, presented challenges to transaction processing and validation efforts.  A viable solution available would be to extract all contract data (both from the system as well as pdf contracts) into a streamlined and structured process that can be efficiently evaluated for completeness and to quickly understand (and action) pricing and margin decisions made by the sales team on any last-minute contracts.

The accrual process, especially with general and administrative expenses, also creates challenges at month-end.  Examples include how to exhaustively manage PO data from vendors to ensure all outstanding obligations have been properly accounted for.  AI technologies can help to mine this data in an efficient manner and furthermore take this a step further by generating accruals based on pre-determined business rules provided by the knowledgeable accountant.

In line with the aforementioned example of PO data mining, AI and automation can support the very manual process of vendor follow-up, e.g. finding out billing progress from legal and professional services firms.  Bots can be deployed to do the first few initial email follow-ups.  Any escalations that would require human intervention can be placed be integrated back into workflows and be orchestrated by AI.


Analysis and Reconciliations
A use case for AI is to search for anomalies and to raise alerts based on historical patterns or thresholds.  For Finance and Accounting, the practical use cases can be applied directly to variance analysis and reasonableness reviews.  More specifically, it can be applied to cash settlement processes e.g. transaction matching, to most GL account reconciliations where transactions are compared between source or sub-ledgers to the GL for matching, differences such as in transit items, and/or errors.

Exception and anomaly handling can be done in real time and furthermore, a best practice is to be able to create “what-if” scenarios and build this into intelligent workflows.   Furthermore, AI applications can capture how SMEs are addressing exceptions over a historical data set such that AI can continuously learn from how exceptions are handled. This also has practical applications in account reconciliations.

On the subject of account reconciliations alongside a tight timeline of the close, accounting teams have to prioritize their efforts to the higher risk accounts.  Some AI-enabled best practices among the participants have been use bots to focus on sourcing data from the multiple subledgers, scrubbing the data for initial exceptions and escalating these for SME review and finally, applying excel functions such as generating pivot tables automatically into an excel workbook.


Close, Consolidation, Management and Financial Reporting
Consolidation entries are also great candidates for AI.  Any types of routine consolidations can be automated.  Furthermore, any reasonableness reviews and checkpoints can be incorporated into pre-defined workflows.  In terms of financial report preparation where there is a standardized presentation format, the data collation and validation checking could be applied to help create initial drafts of the reports.  AI can be leveraged to highlight any anomalies and escalated to Finance and Accounting SMEs for further review.

The preparation of investor relations packages and datasheets could be accelerated with technology.  This allows for SMEs to spend more time on reviewing the investor packages and crafting their stories to external analysts.    Similarly, the preparation of the Controller’s packages for internal use could benefit from AI-enabled data handling and workflows.  Additionally, reports generated from a systemized approach provide additional levels of auditability and traceability.  This supports the heavy-lifting involved in generating audit back-up, workpapers, and PBCs.


Conclusion
The workshop highlighted multiple practical applications of AI to the Record to Report process in the Controllership organization.  Successful applications of AI in R2R included the acceleration of data engineering, validation, variance analysis and exception handling, audit support, all the way to report preparation.    Despite the diverse industries represented by the participants, common challenges such as how to decide what, when and how to implement automation and process optimization in Finance were prevalent.  A few important takeaways learned successful applications of AI included (a) understanding what KPI is being addressed (b) identifying the right proof of value projects in order to generate quick wins and in turn, provide ROI to
management (c) continue to stay exposed to emerging AI technologies such as RPA, Process Optimization, Unstructured Data Processing, Machine Learning, etc.


Background of The Hive Think Tank’s Quarterly CFO Forum Workshops
In February 2019, The Hive Think Tank launched the CFO Forum Workshop Series; this is organized as private events that explore emerging applications of artificial intelligence (AI) in enterprise finance functions. This series brings together Finance thought leaders across Fortune 500 companies, industry experts from big advisory firms and entrepreneurs creating new AI-driven enterprise finance applications. Workshops in this series will address the CFO organization’s themes like Revenue Management, Strategy & Planning, Accounting, Internal Control, Audit, GRC, Treasury etc.

How to mine contracts from unstructured data sources with AI and Machine Learning

Companies process a variety of contracts in their business operations. It can be a sales contract, a revenue agreement, a procurement contract, or a legal contract. Authoring a contract with right clauses and obligations is a complex process and requires a lot of manual effort. Many valuable resources are spent in authoring and maintaining these contracts to ensure accuracy and legal compliance. There is contract lifecycle management (CLM) systems available in the market that can streamline the contract processing in an organization. But they cannot address manual touchpoints in the system that span beyond the scope of the system.

Contracts can come from 3rd party sources, external systems, and legacy systems in the form of PDF or word documents. These documents are reviewed by business users and transferred to CLM systems manually. This entire process takes a lot of time and can involve human errors that result in compliance issues and increase the risk. It takes a considerable amount of resources to identify and correct the errors. Many organizations have dedicated resources to manage contracts and handle their complex process. AI and machine learning can learn complex contract processes and rules, and maintain contracts accurately.

The Live Objects platform can read and learn contracts data from unstructured sources like pdf and word documents. The AI algorithms will mine the various components of contracts like clauses, T&C’s and obligations, and maintain a repository of contracts in the CLM system. This will eliminate the manual effort to read, understand, and create contracts. Live objects can create orders, invoices and other related transactions from the contracts and link them to the source. This will help reconcile the transactions, renew contracts and manage mid-term contract changes.

Live Objects can maintain an intelligent clause library in the CLM system based on the learnings from the existing contracts, that can be used to author new contracts or amend existing contracts. Using AI algorithms, the system can extract obligations from unstructured contract data and help to track them during the contract life cycle. The platform can compare new contracts with the existing playbook and help the users to redline the contract based on matching accuracy. It can alert users for possible actions on the contract. This can reduce the cycle time of contracts and the compliance risk.

Live Objects can perform the process mining of an existing CLM process and identify the bottlenecks to optimize the process. This is part of the closed-loop self-optimizing business process transformation strategy of Live Objects that will discover, recommend, and transform the CLM process with process mining using AI and machine learning. Live Objects can work with any CLM product (Apttus, Icertis, SpringCM, and others) and optimize their process.

How to enable multi Legal Entity QTC business models with process mining and AI

The evolution of cloud CRM systems into cloud platforms has opened doors to develop complex business applications like the quote to cash (QTC) systems. QTC systems will extend the CRM functionality beyond the sales process to create quotes, contracts, orders, invoices, payments, and revenue agreements. These transactions are closely tied to a legal entity, which is typically an incorporated company in a country or an independent business unit under a parent company where financial reports are generated for legal reporting.

Legal entity should be a foundational building block for any QTC system. This is the critical functionality that will help transactions to flow into the downstream ERP. A legal entity will have the company data (address, name, etc.), currency, accounting calendar, chart of accounts, tax data (VAT, sales tax etc.) and the transaction data. The transaction data is generated in CRM (leads, opportunities), QTC (quotes, contracts, orders, invoices, payments, revenue agreements), and ERP(sub-ledgers and GL transactions).

When a legal entity is created in an ERP, the master data should be synchronized to upstream CRM and QTC systems in near real-time so that these applications can use the data to create transactions. When there are updates made to legal entity data, the updates should be interfaced to other systems. A process mining platform can mine the legal entity process and recommend the optimized process to interface the master and transaction data across front office(CRM), middle office(QTC), and back office(ERP) to give seamless one office experience to business users. This will ensure that the transactions are tied to the correct legal entity, and the legal reporting will have accurate numbers.

The platform can optimize other QTC processes like CPQ, CLM, and revenue management (order management, billing, revenue recognition, and payments) that will align with the legal entity process to reduce manual effort, increase efficiency, and deliver better performance to drive the organization towards the autonomous enterprise. Process mining will help to accelerate new legal entity implementation and expand the business to new locations. It can reconcile transactions from multiple data sources to respective legal entities.

Process mining can streamline accounting and period close processes for a legal entity in the ERP, once the invoicing and revenue recognition is completed for the period in the QTC system. Any organization implementing a QTC solution with multiple legal entities should consider a process mining platform that will build the foundation for the intelligent enterprise which can process structured and unstructured data, and handle transactions with an optimized business process. This will enable accurate legal reporting and compliance.

Enabling Business Agility, building “autonomous enterprise”

In the book Reengineering the Corporation written by Michael Hammer and James Champy, they stated that “Not a company exists whose management doesn’t say, at least for public consumption, that it wants an organization flexible enough to adjust quickly to changing market conditions.Business agility is vital for any firm to be able to succeed in the current highly competitive environment. Business agility makes companies capable of dealing efficiently with exceptions and uncertainties which may always occur. Companies should be ready to cope with variability and evolving business environment at all the time.

There is high competition between the old and new business enterprises caused by changes in the market, traditional enterprises are concerned with business disruption and competitive threats, especially from new digital-savvy entrants. The future belongs to enterprises with Autonomous Operations in all aspects. Decision takers and influencers have realized the value of data and are using it as a strategic asset to build better data driven decisions and organizations. In today’s AI world, where self-driving cars are on the street and powerful virtual assistants such as Google Duplex, Alexa or Siri can do or assist in most of the day to day errands.

It is not the strongest of the species that survive nor the most intelligent but the one most responsive to change.

– Charles Darwin

Most companies have BPM systems, ERP’s, CRM, workflows, ticketing systems or complex business process systems customized to enable their operational efficiencies. As data driven decisions has been applied across, finding single source of truth has become a big challenge. ‘Digital’ trend has made most of the companies invest in data consolidation and data governance tools to support operational analytics or advanced analytics. For a business owner, the ideal world is to prevent business exceptions or have suitable measures to deal with them proactively.

Is your data lake or Data Platform’s helping with this? What if this is possible with the latest advancement in AI specifically with deep neural nets and ontology based semantic deep learning.

Live Objects is helping enterprises to kick start optimizing business process exceptions leveraging AI deep learning and process mining techniques. Combining with AI techniques, Live Objects uses process and pattern mining, user behavior modeling, activity modeling, conformance checking to optimize the business processes.

Most business process exceptions are currently conducted manually either by deploying consultants or addressing manual operations by the implementation of bots or RPA (Robotic Process Automation) to automate business processes exceptions by rules or data trends. Although there is a steep increase in adoption of RPA and bots recently, we have started witnessing their pitfalls in areas such as solutions scale, unclear responsibilities, lack of focus in process selection or choosing complex process which ends up being less cost effective.

Live Objects is making the business agility possible with its unique patent pending business transformation framework built from recent advancements in AI primitives across deep neural network, sequence pattern mining and NLP. Every business has fully specified as well as knowledge-intensive and highly dynamic processes which are loosely specified. With pre-built process area domain knowledge stored in the apparatus in the form of formal knowledge ontologies, Live Objects optimizes the business process to the enterprise through AI-driven automation of discovery, design and process engineering by mining patterns in business objects, cases and transactions across all process variations.

Having domain aware process-centric Al solution, Live Objects makes it possible for enterprises to break down the silos between line of businesses on process optimizations. Live Objects is the world’s first closed-loop solution which natively integrates with systems such as SAP, Salesforce, Hybris, Oracle, Workday and other platforms.

Interested in learning more? We are happy to schedule a no-obligation product demo on our current solutions for Financial consolidationeCommerce, OTCOrder ManagementCustomer Experience, Supply chain and process migration to enable enterprises compete with their competition.

Reimagining business processes with AI

Artificial intelligence has clearly emerged as the latest digital frontier in the enterprise with most technology-led transformations inclusive of AI capabilities offered by all major cloud and on-premise platforms. These first-generation AI-driven applications and cloud-based services deliver significant enhancements in the accuracy and efficiency of activities and decisions made in the enterprise. The first generation of AI capabilities include intelligent data aggregation and correlation, conversational agents, computer vision, natural language processing and robotics. We are seeing adoption of these capabilities in business functions like marketing, customer service, operations, back-office tasks, logistics and ITSM.

Adopting AI top-down from business processes
While the adoption of AI is delivering point efficiencies in specific activities and decisions, enterprises still struggle to create enterprise-wide business outcomes with AI at the aspirational scale of an Amazon or Google. Business outcomes are generated by business processes that form higher layers of abstraction across business functions to drive consistency and predictability over activities and decisions made. Business processes rely on systems for single sources of truth about the enterprise, and synchronize the activities and decisions led by people, applications and, more recently, virtual agents. The need to drive consistency across business functions and activities creates complexity in the structure of business processes that makes them inherently less flexible and resistant to change.

The rigidity of business processes comes in the way of delivering enterprise-wide outcomes from the adoption of AI, as it severely localizes the value of AI adoption to point activities and decisions. For instance, an implementation of AI for dynamic customer segmentation is of limited value if the order management business process is too rigid to adapt the customer’s experience based on the segmentation inferred. Hence, the enterprise struggles to show growth in revenue and customer satisfaction as a result of the adoption of AI. Therefore, the adoption of AI in the enterprise has to be driven top-down all the way from the business process layer of abstraction.


Reimagining business process lifecycle with AI
The above premise is the foundation for The Hive‘s point of view that the lifecycle of business processes needs to be reimagined with AI. Business processes need to be equipped with the discretion between optimizing for consistency by holding on to the as-is structure vs. flexibility by designing & adopting new customized structures to actively address changing business scenarios. We see this form of self-awareness in action in the way ride sharing services like Uber optimize customer experience across orders and operations. This demands an AI-centric continuous transformation of business processes, where this capability constantly drives change and flexibility in the structure of the process without trading off consistency. Thus, business process transformation turns AI-centric & continuous, from today’s human-centric & retroactive form. This ambition of turning business processes self-aware and closed-loop led to the co-creation of Live Objects.


Introducing Live Objects
Live Objects realizes the “Autonomous Enterprise” vision by its path-breaking application of AI at the business process abstraction for continuous business process optimization. It is the world’s first closed-loop continuous process engineering product built upon its patent-pending technology that has been built from recent advancements in AI primitives across deep neural network, sequence pattern mining and NLP. Live Objects’ closed-loop approach comprises of process discovery, engineering optimal process design and process adaption into the as-is implementation:

  • Process Modeling: Live Objects drives process discovery by modeling activities from unstructured user-behavior manifested in emails, messages, case texts and screen behavior in the context of the current as-in implementation and transactions captured in the business systems. Process mining platforms provide such insights to human consultants for aiding traditional business transformation. However, Live Objects goes beyond process mining to build mathematically rigorous model abstractions for AI-centric closed-loop business transformation.
  • Process Engineering: The new process design is driven by restructuring and re-sequencing business process activities to optimize targeted operational, business and customer experience goals at an end-to-end business process abstraction. These new process structures are built from Live Objects’ advanced AI capabilities of association rule learning, local process optimization and causal network modeling. These structures are generated with well-defined business object associations and business system plug-ins to seamlessly migrate cases from the as-is process implementation and, if required, hand them off back.
  • Process Adaptation: Continuous business transformation inevitably requires continuous assessment of risk and conformance. Live Objects brings pre-built domain-specific risk models and adopts as-is implementations of conformance models to select process designs that successfully clear the risk metrics. These process designs become viable candidates to be brought to the as-is process implementation for production; thus, completing one cycle of business transformation.


Cycles of business transformation on autopilot with Live Objects
Live Objects’ cycles of business transformation are agile, short and customized to fine-grained segments of the enterprise’s business operations. For instance, Live Objects’ order management solution delivers targeted on-demand experiences to specific customer segments inferred in response to changing channel and competitive landscape. These cycles of transformations can occur in windows of fiscal weeks to months – thus delivering business outcomes in the form of number of orders closed at the quarter end and the customized experiences projected to customers & partners. We are also seeing strong demand for Live Objects in e-commerce, process migration & consolidation, procurement and supply-chain management.

Live Objects is laying new rails for an enterprise’s AI-centric digital transformation to own real business outcomes. It is redefining the relationships between processes, systems and people to enable enterprises to achieve actively changing business goals. We look forward to playing an active role in the emerging AI-centric enterprise business ecosystem. The Hive and its community of investors & partners are proud to herald the second-generation of AI adoption in the enterprise with Live Objects.