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Edward Jones Adds Robotic Process Automation with Lean Six Sigma

By Brooke Holmes

Automation 1.0

Robotic process automation (RPA), commonly referred to as “bots,” is a type of software that can mimic human interactions across multiple systems to bridge gaps in processes that previously had to be handled manually. RPA software applications can be integrated with other advanced technologies such as machine learning or artificial intelligence. But at the most basic level, they act like super-macros following a detailed script to complete standardized tasks that do not require the application of judgment.

Why Combine RPA and Lean Six Sigma?

Replacing manual work with bots removes the possibility of human error, reduces rework and quality checks, while also increasing accuracy. Bots can work much faster than humans and at any hour of the day so long as the underlying systems are operational. The potential to reduce overhead costs and increase process cycle time is vast. Bots also provide enhanced controls for risk avoidance.

Bots can serve as a foot in the door to gain traction for a quality program. Senior level executives get excited by the potential of this relatively affordable technology. By incorporating a thoughtful Lean Six Sigma (LSS) process review into a company’s bot deployment strategy, quality programs will gain additional visibility and leadership support.

Effective Bot Deployment at Edward Jones

Edward Jones is a financial services firm serving more than 7 million clients in the US and Canada. Their operations division began exploring RPA in 2017 and subsequently implemented their first bot into production in November 2018. Since then, they’ve deployed 17 additional bots, yielding 15 full-time employees in capacity savings, which in turn generated more than a million dollars in cost avoidance. While still at an early stage in this journey, the operations division has developed a structured approach using LSS tools to assess process readiness for automation, minimize or remove non-value-added work steps prior to development (abandonment), and redesign the process to fully leverage the benefits of RPA.

LSS Process Review

Using a questionnaire to begin their intake process, business areas submit critical data regarding process volumes, capacity needs, system utilization and risk level. This data feeds into a prioritization matrix that allows them to decide where to focus energy and time. Once a process is identified for RPA, a member of the quality team engages the business area for a LSS process review using familiar tools such as a project charter, stakeholder analysis, SIPOC (suppliers, input, process, outputs, customers) and process maps.

After thoroughly understanding the process’s current state, the practitioner and corresponding business area redesign the process for robotics. Next, they complete an FMEA (failure means and effect analysis) and business continuity plan to ensure process risk is being adequately controlled. After this LSS process review has concluded, a broad group of experts – including robotics developers, internal audit staff, risk leaders and senior leadership from all impacted business areas – are brought together to jointly review the robotics proposal and agree on a go/no-go decision.

A critical component of this process review is thorough documentation of every step along the way. Using an Excel playbook to organize all the tools in one place enables a smooth transition as the effort moves from the quality team to the robotics development team. Then, this comprehensive documentation is retained by the business area for ongoing maintenance. Specific elements of this documentation include a systems inventory, a record of all sign-off dates and approvals and a business continuity plan for disaster recovery. Having complete documentation enables the business areas to take a proactive approach when faced with upcoming system changes or unexpected work disruptions. It also equips business areas with any data points required for routine internal or external audits.

Deployment Pitfalls to Avoid

There are some specific areas of concern when it comes to RPA.

  • Communication: Provide clarity to business areas about what RPA can and cannot do, and what processes fit best with this technology. Without an accurate understanding of the capabilities of RPA, there will be an influx of unsuitable requests for this new technology and, as a result, many disappointed business areas and wasted effort spent putting together their business case. At Edward Jones, the most common misunderstanding was regarding the lack of reading ability for the specific RPA vendor being used. While the bots can recognize characters in static fields, they are not able to interpret characters in an unstructured context. This ruled out many initial RPA requests. Additionally, while comparing RPA to macros was initially an effective way to explain the technology to business leaders that were not knowledgeable about technology development, this comparison created an unfortunate misconception that coding and implementing bots was as fast and easy as creating a macro. Business areas were not expecting development time to take four to six months for what they perceived to be a simple request.
  • Change Management: Incorporate thoughtful change management throughout the deployment at all levels of the organization. Leveraging bots will take away manual tasks being completed by employees. Some employees may welcome the automation of monotonous tasks, but others may view this technology as a threat to job security. Supervisors will need to adapt and grow their skills to include oversight of the RPA technology. Strong people leaders often don’t have the same level of competency in the technical space, and they will need to quickly increase knowledge and skill to effectively manage their automated processes. Senior/C-suite leaders will need to consider the inherent risks associated with using RPA, the infrastructure and skills needed to support an RPA program, and how to obtain the needed resources and talent.
  • Human Resources: Bots may create job redundancy, creating the potential for job loss reassignment. Engage human resources early to navigate these situations.
  • Governance: Balance senior leader involvement so they feel comfortable with automation without extra levels of required approvals that slow the development process down.
  • Don’t Force a Problem to Fit the Solution: RPA is not the right solution for every bad process. In the early phase of bot deployment, it is easy to let excitement about the new technology lead to poor choices around when to apply RPA. This leads to disappointing results that could undermine the entire bot deployment. Identify clear criteria regarding when bots are an appropriate solution and use a disciplined approach to evaluate each new process improvement opportunity. Consider non-bot solutions before a final decision is reached.
  • Vendor Approvals: Any third-party vendors must permit bots to interface with their systems. Review vendor contracts or have new contracts signed to ensure bots are legally allowed to interact with vendor systems and web sites before beginning development.
  • Resource Constraints: Set clear expectations with business areas about the work involved and resources needed to design and implement an RPA solution. The quality team and technical developers do not have the knowledge required about the specific processing steps to complete this work without a subject-matter expert from the business area being heavily involved throughout the project life cycle.
  • Results: Heavy focus on capacity savings only tells part of the story. Identify other meaningful methods of communicating value from RPA implementation, such as risk reduction, faster cycle time, improved client experience or increased accuracy.

Case Study: Automating Retirement Disbursements to Charities

An example of an RPA implementation at Edward Jones involves the process of receiving, validating and executing on client requests to send monetary donations from qualified retirement accounts to charitable organizations. Prior to implementing the bot, the Qualified Charitable Distribution (QCD) process required 11 hours of manpower each day to get through the volume of donations – and the number of requests had been doubling each month.

The process had five to 10 errors monthly due to the manual data entry required, which in turn took one to three hours of leader or senior processor time to resolve. A bot was designed and implemented that would validate the original request (quality check) and then enter the appropriate data into a computer screen to issue the check to the selected charity.

Stakeholder Analysis and SIPOC

After the project charter was created and agreed upon by the project Champion and project team, a stakeholder analysis was conducted to identify any additional individuals or business areas that were upstream or downstream of the process or might be affected by a change to the process. These parties were consulted or communicated with throughout the effort to ensure process impacts were understood and considered as the automation opportunity was identified and designed.

Next, a SIPOC matrix was created to understand all the process inputs, including systems, data files and end users. Together, the stakeholder analysis and SIPOC are essential in ensuring all critical components of the process upstream and downstream are identified early in the automation effort so no processing gaps are created during RPA development.

SIPOC Analysis: SIPOC for the QCD Automation Project
Supplier Inputs Process Outputs Customer
Client, branch team Clilent instructions, intranet form message Branch team sends form message with client instructions for QCD Unexcuted client request in the retirement department queue Retirement support team
Retirement support team Form message, client account information, IRS rules, client request Retirement associate reviews client request for QCD to confirm eligibility Validated client request Retirement support team
Retirement support team Validated client request Issue check Executed request, issued check Client, branch team
Retirement support team Client request, issued check Close client request on system Completed client request for QCD Client, branch team

Current- and Future-State Process Maps

The next step was to create detailed current- and future-state process maps. The current-state process map must include enough detail to highlight all the data sources required by the process, and where that data must be entered to move the process forward. The future-state map must incorporate all of those critical points, while also accounting for the limitations of RPA technology (inability to “read”) and the advantages of RPA (directly ingesting data files, speed and accuracy).

For the QCD process, the client verification step needed to be handled differently for RPA than in the original process. Previously, an employee was comparing client names between the original client request and the account registration referenced in the request to ensure a match. Names can be difficult for RPA to match because the technology doesn’t understand common nicknames that might be used interchangeably with legal names. For example, “Bill” and “William” would flag as a mismatch by the robotic technology, while a human processor would recognize those as referring to the same individual. To avoid large numbers of false positives from the bot flagging mismatches caused by nicknames, an alternative form of identification matching was used, in this case a social security number.

In a typical Six Sigma effort, the goal is to achieve a more streamlined future-state process map with less processing steps and fewer decision points. One key difference between process maps for an RPA effort compared to a more typical Six Sigma improvement effort is that the future-state process maps may contain more, not fewer, steps and decision points. This is normal and shows that the automation capability is being fully utilized to provide a higher level of accuracy. Since the bot processes at a speed much faster than a human can achieve, these additional quality checks do not add to the overall process cycle time. Each decision point with RPA represents a quality assurance checkpoint, allowing for the final output to have higher accuracy than the original process achieved.

Figure 1: QCD Process – Before BPA

Figure 1: QCD Process – Before BPA

Figure 2: QCD Process – After RPA

Figure 2: QCD Process – After RPA

Risk Assessment

Once the future automated state has been identified, conduct a risk assessment to understand the risks associated with the current process and how the process risks may be affected by RPA. The largest risk associated with the QCD process was the manual nature of the process and likelihood of human error. This risk was eliminated by using bots.

However, automation adds different types of risks, including system failures and coding errors. By identifying potential risks and using control reports to quickly identify and remediate issues, these risks can be effectively managed.

Business Continuity Plan

The final element of the process review is a business continuity plan, specifically focused on failure of RPA to successfully perform the programmed tasks. Consideration should be given to a failure of the bot itself but also any underlying systems that the bot needs to interact with to obtain data or execute requests. Planning should include how to perform the work if the automation is not operational for a particular timespan as well as how to identify and resolve errors made by the bot if the programming becomes corrupted.

Through this planning exercise, a critical aspect of the QCD process was identified that may have led to future bot failure had it not been remedied. Volumes for this highly seasonal process rise drastically at year end, and a single bot was unlikely to keep up with the work at this peak. Programmers were able to proactively solve this issue by diverting process volume onto three separate bots to stay on top of the surge of work during these high-volume time periods.

Results

The QCD bot was implemented in September 2019 and immediately realized 11 hours of capacity savings with no errors. The total project cycle time from the initial continuous improvement analysis, through the bot design, development, testing and implementation took seven months. Since implementing RPA on this process, 100 percent of the process has been automated with zero errors. Process risk was reduced by one point on a 10-point scale by eliminating human error from manual work steps.

During routine follow-up six months after bot implementation, the project team learned that the benefits received from the automation had grown significantly. The volume of client requests for charitable distributions had increased rapidly, so the bot was now performing work that would have taken 34 hours – or five employees – to complete each day.

Conclusion

Don’t short cut the methodology when leveraging RPA and other new technologies. Technology masks a bad process, so clean up the underlying work steps first to maximize the benefit of RPA.

Robotics Process Automation

Robotic Process Automation (RPA)

Robotic Process Automation (RPA) is an affordable solution for organizations to streamline their operations and maximize efficiency.  Robots used in RPA interact with applications to perform many mundane tasks such as re-keying data, logging into applications, moving files and folders, copying and pasting and much more.

  • In banking, simple processes like deposits and transfers are perfect for RPA.
  • In insurance, filing and processing claims, underwriting and countless other tasks.
  • The administrative side of healthcare can measurably reduce cost by more than 35%.

RPA is particularly suitable for processes with high human error rates by helping to avoid rework and other error implications like reputational or regulatory risks.   We can help you explore the opportunities!

 


RPA Delivery Framework

Through a simple seven step process, TPMG delivers a low-cost solution for process improvement along with a simple and inexpensive software-based technology. It sits on top of other applications, requires no special hardware, and works well in almost any IT environment.

RPA COE Process 4.0


Robotics Process Automation Center of Excellence

The TPMG RPA Center of Excellence (CoE) functions as a Global Shared Service Center  flexible enough to fit with your firm’s business model.  Your company can rely on it to perform functions such as:

  • assessing and prioritizing processes to be automated
  • developing RPA bots and putting them into production
  • developing and implementing change management programs
  • performing the required process re-engineering before the selected process is automated
  • making sure the robots run without any issues
  • performing security and compliance (e.g. audit trails)
  • That’s not all,  you also get highest level of enterprise grade security

Given your needs, our CoE can centralize an RPA team in one location and deploy efforts remotely.  Our team can also perform in a managed services framework and deploy RPA developers across global functions and/or geographies.  Either way, we are structured to maintain constant interaction with your business in order to understand and respond to your needs.

[View Robotics Process Automation Demo]

What is your process automation project?

Contact us today to schedule a cost benefit analysis.  We can help you explore the opportunities!

TPMG RPA Center of Excellence (CoE)

 

 

 

 

 

 

 

 

 

10 Rules Entrepreneurs Need to Know Before Adopting AI

11 Feb 2020|by Rocio Wu

Business leaders are just beginning to adopt artificial intelligence and machine learning into their operations. Rocio Wu offers insights into how entrepreneurs can start riding the wave.

Although adoption of artificial intelligence (AI) and machine learning (ML) for the enterprise is still in the early days, the technology has matured enough for entrepreneurs to start gathering inspiration and evaluating opportunities for potential applications.

Every day, processing capabilities for neural networks increase, as does accessibility to off-the-shelf APIs from big tech and academic institutions that help speed up innovation. Entrepreneurs have also learned the wisdom of targeting AI applications toward specific, well-defined business problems, rather than trying to sell generalized toolkits to business users or get bogged down in custom software consulting engagements that solve nonrepeatable use cases.

There are more opportunities today for entrepreneurs to focus on solving vertical problems than creating horizontal solutions. It is in the ethos of established tech companies to build generic solutions for customers across industries. But for challengers, the more they can focus on solving core business problems, the more successful they will be. They can understand customer needs deeply and customize product features based on their client’s specific pain points. As a result, the customer will see more business value in the solution and be more likely to convert to a paid customer. (An added bonus for the startup: lower customer-acquisition cost.)

Still, it’s proven difficult for entrepreneurs to write successful AI strategies. After all, the technology is a moving target, potential customers are wary of costs and implementation complexities, and use cases, while powerful, are still lacking in many areas.

Take advantage of uncertainty

All this uncertainty is a fertile breeding ground for entrepreneurs ready to make their mark at the start of the digital enterprise era. Here are 10 rules of thumb to consider as you develop an AI strategy in an established company or sinking roots as an AI-first venture. Some are unique to AI-first startups, while others are generally applicable to enterprise Software as a Service (SaaS) companies.

    1. Understanding the business problem you are solving is at least as important as your algorithms. Even though the technology and data science behind AI is what makes these applications different from conventional software, your business customers are not looking for technology per se. They want solutions to solve problems. Positioning your service or product as “AI for health care” or “AI for sales” is not nearly specific enough. While you can sell AI tools to data science teams or IT departments, business leaders want to know you understand their problems and opportunities intimately, and that your solution is tailored for their situation. Artificial intelligence should enable better solutions.
    1. Is the market ready to support your solution? As more of the world gets digitized, “datafication” (our ability to capture data from many aspects of the world have never been quantified before) continues to accelerate. The problem: Especially in legacy industries such as health care, manufacturing, and agriculture, much of the data is not digital and unstructured, increasing significantly the effort required to extract, clean, normalize, and wrangle. Before starting an AI strategy, determine the digital maturity of the industry in terms of adopting AI. Is the industry mature, with infrastructure already in place to collect data and ready to implement? Are industry stakeholders willing to adopt AI? Do they agree with you on the value of potential benefits? Is the product intuitive enough to speed users through the learning curve? Are there regulatory hurdles to overcome?
    1. Develop your data strategy from day one. Training machine-learning models to improve based on experience often requires large amounts of high-quality data, so it’s extremely important to lay out your data strategy from day one—including how things like data sourcing, volume, diversity, privacy and security will be handled. Data can be acquired in a number of ways including crawling public data, finding data-rich partners, gathering it from customers, or generating it internally. Each has its own pros and cons and their application may be best suitable at different stages. Data strategy is a strategic business decision that entrepreneurs need to define from the start.
    1. Even if your AI is brilliant, your product still needs great user experience (UX), the right workflow, and robust reporting. You will win not because your AI is superior, but because the end-to-end product  is better. Focusing on serving your customers’ end users should be baked into the team’s DNA. In most cases, you are building more than the ML product, so collaboration and coordination is required across functions and among both frontend and backend software engineers and UX designers.
    1. AI can be magical, but sales still win. Fred Wilson, venture capitalist and popular blogger, holds the view that “marketing is for companies who have sucky products.” Similarly, many AI founders believe that if the product is amazing, it should sell itself. However, that’s not the reality of the enterprise world. Big enterprises by default are averse to change unless they are convinced the alternative is worth their business development effort and the time involved of the legal-finance team to negotiate contracts and switch to a new vendor. Value experience in your sales and marketing team, especially if they come from the industry or companies on your target list. Nowadays functional leaders have more power than in the past to decide which software to use, so founders need to figure out an entry point to the enterprise.
    1. Be careful about “AI-first” messaging in marketing. Given the hype around AI that has raised everyone’s curiosity, an AI-forward positioning might be an effective strategy to get a first meeting. However, when it comes to actual buying decisions, customers do not really care whether it has AI inside or not. Some startups have actually removed AI from their marketing and sales messages. While it might not make sense to lead with AI, there’s value in weaving it through the product presentation, especially when it comes to transparency and explaining the underlying machine-learning algorithms.
    1. Avoid the “science project” trap. What’s the MVA (Minimum Viable Algorithm)? As the saying goes, “perfect” is often the enemy of the “good.” Business strategy has come to embrace the power of injecting a minimally viable product into the market quickly and iterating based on real-world feedback. Likewise, AI projects should similarly strive to develop and quickly market a minimum viable algorithm. This approach may take some convincing; the DNA of founding technical teams is often around solving technical puzzles and increasing accuracy from 90 percent to 95 percent. Many customers are not going to see the difference, but they will notice as the product improves from release to release.
    1. Manage customers’ over- and under-expectations. When it comes to successfully deploying AI in the real world, half of the battle is over expectation management and communication. Customers often overestimate your AI’s effect, thinking of it as superhuman—especially if AI is solving complicated problems such as 100 percent accuracy in self-driving cars and medical diagnosis. You have to help them understand that the performance of ML products improves over time (it’s machine learning after all) and unlikely to deliver perfect results in the beginning. Underestimates can happen as well if AI is solving a constrained problem like back-office automation or insurance claims. Helping customers understand which problems can and cannot be solved with AI is vital, just as Lemonade Insurance, which uses AI and other technologies to determine coverages and set rates, explained very clearly to potential customers how their product worked and what was within coverage and what was not. AI is still a very imperfect technology that often fails. There should not be any surprises for customers on that score.
    1. Hire both tech and domain experts from industry. Your team needs both ML engineers (often PhD level) and top software engineers who can productize and deploy AI. (Ideally you want talent who can do both, but good luck finding them!) There’s a limited supply of ML engineers and big tech companies will pay dearly for a brand new PhD in deep learning. It’s hard for startups to attract top AI talent, but even harder for Fortune 1000 companies. However, attracting domain experts from the traditional industries you are trying to disrupt is even more important. They are critical in helping target customers, deploy technology, and understand the input needed and the internal workflow used by businesses in order to trust AI’s judgments and validate results.
    1. Organizational shift: Towards a more open and experimental culture. ML/AI engineers are still a novelty in the enterprise world. Managing an AI-first startup requires fundamental organizational changes: an experimental culture, data analytics-driven mindset, and more openness towards uncertainties. As a founder, you should help cross-functional teams understand how ML products are different from conventional software products, address potential conflicts, and promote a more open and experimental culture.

Just the beginning

Artificial intelligence continues to evolve at a breakneck pace, creating plenty of ground-floor opportunities for entrepreneurs who are disciplined in their approach, identify best markets for vertical solutions, hire talented and experienced teams, and who are successful at selling AI not as a technology but rather as a means to the best solutions.

Special thanks to Brian Ascher at Venrock for valuable contributions.

Rocio Wu is a second-year MBA candidate at Harvard Business School and a venture capitalist.

Robotics Process Excellence

We have combined Lean Management, Process Re-engineering and Robotics Process Automation (RPA) into a powerful approach to eliminate waste, improve productivity, and reduce the cost of doing business.    Robotics Process Excellence (RPEx) services help organizations:

  • Ensure process performance exceeds business goals.
  • Measurably increase productivity by more than 25%.
  • Enhance the quality of customer care and ease of doing business.
  • Streamline processes and measurably reduce the cost of operating.
  • Eliminate slow, tedious, time consuming, wasteful tasks with Robotic Process Automation (RPA).

Lean management is a proven method for eliminating waste and the cost that comes with it.  RPA  is an inexpensive software-based technology. It sits on top of other applications, requires no special hardware, and works well in almost any IT environment.  That’s not all,  you also get highest level of enterprise grade security.

 


Our Approach

Through a simple seven step process, TPMG delivers a low-cost solution for process improvement along with a simple and inexpensive software-based technology. It sits on top of other applications, requires no special hardware, and works well in almost any IT environment.

RPA COE Process 4.0

 


Cafeteria of Process Excellence Consulting  Services

We view our process excellence services as the backbone of our business improvement practice.   Our consultants provide first hand knowledge of best practices and a deep understanding of high performance organizations.   We deliver top-quality  services that guarantee your organization become more productive, cost effective and customer driven.  Those services include:

  • Lean Management
  • Activity Based Costing
  • Non-Value Added Analysis
  • Business Process Re-engineering
  • Operational Assessment and Redesign
  • Value Stream Mapping and Improvement
  • Rapid Improvement Events (Kaizen)
  • Business Transformation
  • KPI’s and Metrics
  • Robotic Process Automation (RPA)

 

Project Description:  What is your process improvement?

 

Robotics Process Automation – Demo

Robotic Process Automation (RPA) is an affordable solution for organizations to streamline their operations and maximize efficiency. Robots used in RPA interact with applications to perform many mundane tasks such as re-keying data, logging into applications, moving files and folders, copying and pasting and much more. RPA is particularly suitable for processes with high human error rates. It’s an inexpensive software-based technology that sits on top of other applications. It requires no special hardware, and works well in almost any IT environment. That’s not all, you also get highest level of enterprise grade security.


Improving Productivity with RPA

We have combined  Lean Management, Process Re-engineering and Robotics Process Automation (RPA) into a powerful approach that eliminates waste, improves productivity, and reduces the cost of doing business.    Our Operational Excellence (OpEx) services help organizations:

  • Ensure process performance exceeds business goals.
  • Measurably increases productivity by more than 25%.
  • Streamline processes and measurably reduce the cost of operating.
  • Automate slow, tedious, time consuming, manual tasks.

 


Demo – Revenue Assurance

This demo explores how robotics process automation and artificial intelligence are continually redefining the future of work. One minute of work from RPA translates to 15 minutes of human activity. RPA also provides stakeholders with additional flexibility, enabling them to focus on more demanding and value added tasks.

 


Demo – Customer Account Details

Robotics Process  Automation is an affordable solution for organizations to tackle repetitive, low – value added work.  Robots used in RPA interact with applications mimicking human actions and can perform many mundane tasks such as re-keying data, logging into applications, moving files and folders, copying and pasting and much more.  RPA has been adopted in industries with intense, manual, and administrative processes, such as financial services, insurance and health care.   (The information in this demo has been blurred to preserve confidentiality)

 


 

Project Description:  What is your process automation project?

Robotics Process Automation

Robotic Process Automation (RPA)

Robotic Process Automation (RPA) is an affordable solution for organizations to streamline their operations and maximize efficiency.  Robots used in RPA interact with applications to perform many mundane tasks such as re-keying data, logging into applications, moving files and folders, copying and pasting and much more.

  • In banking, simple processes like deposits and transfers are perfect for RPA.
  • In insurance, filing and processing claims, underwriting and countless other tasks.
  • The administrative side of healthcare can measurably reduce cost by more than 35%.

RPA is particularly suitable for processes with high human error rates by helping to avoid rework and other error implications like reputational or regulatory risks.   We can help you explore the opportunities!

 


RPA Delivery Framework

Through a simple seven step process, TPMG delivers a low-cost solution for process improvement along with a simple and inexpensive software-based technology. It sits on top of other applications, requires no special hardware, and works well in almost any IT environment.

RPA COE Process 4.0


Robotics Process Automation Center of Excellence

The TPMG RPA Center of Excellence (CoE) functions as a Global Shared Service Center  flexible enough to fit with your firm’s business model.  Your company can rely on it to perform functions such as:

  • assessing and prioritizing processes to be automated
  • developing RPA bots and putting them into production
  • developing and implementing change management programs
  • performing the required process re-engineering before the selected process is automated
  • making sure the robots run without any issues
  • performing security and compliance (e.g. audit trails)
  • That’s not all,  you also get highest level of enterprise grade security

Given your needs, our CoE can centralize an RPA team in one location and deploy efforts remotely.  Our team can also perform in a managed services framework and deploy RPA developers across global functions and/or geographies.  Either way, we are structured to maintain constant interaction with your business in order to understand and respond to your needs.

[View Robotics Process Automation Demo]

What is your process automation project?

Contact us today to schedule a cost benefit analysis.  We can help you explore the opportunities!

TPMG RPA Center of Excellence (CoE)

 

 

 

 

 

 

 

 

 

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