Daily Archives: July 23, 2021

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Automation and artificial intelligence (AI) adoption continues to grow, accelerating workflows, reducing manual efforts, driving decision-making, and making the impossible, possible targeted, personalized and elevated digital experiences at scale.

IBM found 80% of companies use automation software or plan to use this technology in the next 12 months. Fifty percent of McKinsey respondents report their companies have adopted AI in at least one business function. Both capabilities are more sophisticated and accessible than ever before. Automation platforms that previously were only available to the largest marketers have become democratized and user-friendly. Most come replete with baked-in AI tools or the option to easily add AI through third-party integrations.

The machines now run the show. Marketers today depend on automation and AI to guide profitable customer journeys, meet rigorous performance goals, compete and get-to-market.

Traditionally, martech aided the execution of digital campaigns. Today, automation and AI when harnessed and maximized by human experts are at the center of creation, execution, evaluation and optimization.

Through sophisticated and user-friendly analytics, predictive modeling, AI-recommendations and experimentation, martech is now directly influencing digital strategy and creating powerful opportunities. The machines are telling marketers what to do to improve performance.

Experimentation (such as changing a webpage layout, improving the information hierarchy, adding content recommendations, reducing webform complexity, testing visual cues) expedites decision-making through proven optimization. It replaces long-scale, potentially bureaucratic decisions (that may fail to return desired outcomes), with agile and iterative improvements.

Digital experience platforms are built around this concept and come replete with web experimentation tools, optimization-as-a-service capabilities, and customer data platforms.

Run an experiment, make a change, measure efficiency and improve digital strategy on an ongoing basis. Replace guessing and gut feelings with engagement and performance data. When optimizations fail, they fail quickly, and the strategy pivots. This is the future.

Related Article: Decisioning vs. Orchestration: What's the Difference?

Until the robot uprising, the experts running digital experiences are as critical to their success as the technology.

A digital strategist is the conductor: setting campaign objectives, ideating experiments, creating content, determining measurement plans, aligning goals and making decisions based on outcomes to fine-tune the strategy and performance.

Marketers and executive leaders need to understand the data inputs feeding the machines, algorithms and experiments, and how theyre used. Never blame the machines for disappointing results or rigor. Instead, return to the data and always be learning, pivoting and optimizing for better outcomes. Every test and every campaign produces information to better inform the next one.

Machines may increasingly run the show, but people run the machines. Investing in your team members, vendors and agency partners ensures youre leveraging every untapped opportunity and fully maximizing your tech investment.

Properly feeding the machines accelerates success and future-proofs your business. Focus your efforts in these three critical areas:

Marketers know complete and actionable data powers digital strategy. It triggers messages, builds predictions or experiments, offers recommendations and informs decision-making.

Its hardly easy. Data privacy rigor continues to test marketers, users are increasingly sensitive to disclosing data, and tech giants like Google modify their data policies on a whim (like removing third-party cookies). In fact, Forrester found in 2020, 32% of global marketing decision-makers said that managing data quality was one of their organizations biggest challenges with marketing programs.

Marketers must continue to lean harder on first-party data sources and get explicit user consent. The more reliable the data, the better campaign performance and repeatability.

Clean and quality data drives better marketing for both the machines and the digital experts running them.

To exploit every opportunity and optimization, marketers must embrace a culture of data-centered experimentation.

They must be willing to experiment and have permission from their execs to fail fast, and pivot. Leadership must be vocal and committed to this permission, removing the bottlenecks or bureaucracy that get in the way of quick and agile action and iteration.

New insights, innovations and opportunities are the direct result of trying something new, measuring its success or failure, and tailoring the strategy to be more compelling, relevant and helpful to each customer. Make experimentation a culture priority.

Related Article: The Data-Driven Organization Is an Endangered Species

This area is frequently overlooked, which tanks customer experiences.

Investing in powerful martech solutions is throwing money down the drain if the overall customer experience is friction-filled and frustrating. Googles recent Page Experience update is a direct nod to getting these essentials right, or risk losing search visibility to the competition.

Website speed, mobile experience, engaging and personal content, and SEO are minimum fundamentals that all marketers must prioritize getting right. If you dont, you wont have an audience to engage in the first place.

Automation, AI and digital experimentation give modern marketers robust capabilities and insights that mirror science fiction.

By combining these powerful technologies with actionable data, bold and empowered teams, and solid fundamentals, businesses can scale execution, solve challenges, accelerate performance and tap into unlimited unrealized potential. Tame the machines, beat the competition.

As chief executive officer of digital agency Whereoware, Michael Mathias leads Whereowares strategic vision and culture of innovation, comprehensive digital marketing, and flawless performance. Mathias comes to Whereoware with an impressive track record accelerating growth for companies at all stages, with expertise spanning marketing, software, professional services, big data, analytics, and technology.

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AI and Automation Run the Show in Marketing Today - CMSWire

When U.S. Citizenship and Immigration Services (USCIS) began moving to the cloud in 2014, much of the agencys cloud movement was lift and shift simply moving workloads as-is to the cloud. Over time, IT staff trained themselves on cloud operations and began to take greater advantage of the flexibility and scalability that cloud computing offers.

After several years, agency leadership tasked the Office of Information Technology (OIT) to digitize all of the agencys immigration benefits processes. Around that time, IT leaders realized that they needed stronger governance and standards around cloud. They moved to domain-driven design, which allowed IT to work across the agencys directorates.

Domain-driven design enabled us to work across the IT architecture, teams, and communications channels, said Rob Brown, USCIS chief technology officer. We began to understand what development teams were doing across the agency. As a result, we began to consolidate teams, platforms, and toolsets taking advantage of opportunities to reuse capabilities instead of buying more.

Then, last year, immigration services were curtailed because of the COVID-19 pandemic. Unlike most other Federal agencies, USCIS is self-funded through fees collected for the provision of immigration and citizenship benefits not through congressional appropriations. With USCIS offices closed, fee collection dropped precipitously, and agency units were tasked to employ cost-saving measures.

At OIT, leaders developed a cost-management strategy, as well as a 90-day Operation Cloud Control (OCC) project to realize immediate savings and establish processes to lock in those savings moving forward. Moving to reserved instances alone saved more than $2.5 million during 2020-2021. In total, the OCC project saved nearly $4 million during the same time frame.

Through the OCC project, OIT educated staff about the need to rightsize cloud instances, and it created dashboards to measure progress, which were visible to IT teams as well as executive management.

OIT also re-architected applications to operate more effectively in the cloud, and it rolled out design-cost principles and policies. Those policies are enforced by Robotic Cloud Automation (RCA), a library of serverless cloud automation solutions developed by Simple Technology Solutions (STS) that leverages Amazon Web Services (AWS) native tagging capabilities and Lambda scripts. The library is a one-time cost to USCIS, rather than a recurring expense.

RCA automatically identifies cloud sprawl using those tags and governance-as-code, and, in contrast to other solutions, then remediates cloud instances, environments, and resources that are over-provisioned, over-scheduled, or not compliant with the agencys usage standards. Remediation actions include moving to reserved instances, autoscaling to accommodate spikes in demand, and more.

USCISs design-cost principles and policies are manifested in the Lambda scripts, noted Aaron Kilinski, principal and chief technologist at STS. Not only do the scripts ensure good cloud hygiene across the enterprise, but they also enable USCIS to take advantage of the operational agility and economic advantages of AWSs consumption-based model.

Read more:

USCIS Gets a Grip on Cloud Costs With Robotics Cloud Automation - MeriTalk

Trade secrets of automation leaders

Anyone who has mastered a new skill or developed a new professional competency will tell you that the personal realizations they experienced during their journeys seem blatantly obvious in hindsight.Ironically, others who are embarking on similar journeys frequently find such insights to be barely credible or even counterintuitive.

An earlier article discussed the critical success factors involved in launching and scaling strategic automation initiatives.This article reveals the secretand sometimes counterintuitiverealizations of individuals who have successfully led such efforts.

Realization 1 Business impact is not necessarily correlated with transaction frequency or process scale

Conventional wisdom dictates that automation efforts should be focused on highly repetitive processes performed by large numbers of people.The mathematical basis of this belief is obvious: the more times a task can be performed automatically, the greater the savings of human time and effort.Its not so obvious that automation can be strategically beneficial when applied to infrequent processes performed by small numbers of people.

For example, international tax filings are prepared periodically by individuals with highly specialized legal skills.Public company financial reports are prepared by individuals with specialized financial skills.Data being fed into machine learning models may be manually tagged or labeled by individuals with specialized data science skills.Automating any portion of these processes not only produces a higher return on the specialized and highly compensated skills of key employees but also minimizes opportunities for human error.

Realization 2 Process re-engineering produces oversized benefits

How many times has a business partner said we dont need to re-engineer this process because we already know exactly how it should be performed, or lets just imitate the way Sally performs her job because shes the most efficient member of our team?Current process practitioners frequently believe that 10% to 20% improvements in throughput or accuracy are revolutionary.They rarely stop to consider wholly new ways of performing an existing activity or challenge themselves as to whether certain work procedures should be performed at all.

As Henry Ford so aptly observed, if you always do what youve always done, youll always get what youve always got.A willingness to consider fundamental changes to existing processes is an important metric in prioritizing opportunities within more advanced automation programs.

Realization 3 Bad data may limit your automation opportunities

Conventional wisdom suggests that greatest barriers to the effective use of automation technology are posed by the fragmentation of existing processes, frequent process changes and widespread exception handling procedures.These issues can pose significant automation challenges.However, feasibility studies of new opportunities frequently reveal that a significant portion, if not the majority of human effort is being expended to compensate for issues related to data integrity, consistency, completeness or availability.

It may be relatively easy to reach a business consensus on the ideal structure of an automated process but the implementation of that automation solution may be stymied by fundamental data limitations.

Realization 4 Citizen Developer programs are essential force multipliers

Citizen Developer programs are difficult to establish, difficult to administer and difficult to maintain.Large numbers of employees need to be trained on the use of a new technology (never an easy thing to do).They need ongoing access to automation consulting expertise following their initial training.The solutions they develop need to be carefully governedwith a light touch in some instances but with a heavy hand in others when they potentially run afoul of master data management, information security or financial control issues.

Nevertheless, the need for effective Citizen Developer programs is inescapable.IT groups will never obtain the headcount and business operations teams will never have the bandwidth to address all the low risk/high reward automation opportunities within a modern enterprise.

Many automation vendors will claim that their tools are being widely employed by Citizen Developers.These claims can be validated by spending time with the vendors existing customers and asking the following questions.Is their tool being used by a specialized, dedicated team within IT or is it being used across the IT organization by individuals in application support, infrastructure engineering, data analytics and information security?If their tool has been deployed outside IT, is it being used solely within operations teams embedded within individual business functions or has it been adopted more broadly by staff members within those functions?If its being employed by business staff members, how many business functions are currently using the tools (i.e. is it being exclusively used within the Finance organization)?

Citizen Developer programs can only be truly effective if the number of business staff developers exceeds the number of all other developers and the ratio of business staff developers to all others is increasing over time.

Realization 5 Cost savings become a byproduct of success instead of an overarching goal

One of the most serendipitous and unexpected outcomes of achieving automation at scale is the realization that doing business in better ways almost invariably increases profits and revenues.Every CFO will tell you that if you have a sound business strategy and flawless execution, your company will achieve outstanding financial results.Seasoned CFOs consider their firms financial performance to be a lagging indicator of sound strategy and meticulous execution, not vice versa.

The same is true of automation programs.As such programs mature they go beyond trying to perform existing processes faster and cheaper.They seek to redesign existing work practices to do things betterbetter for customers and better for employees. Guess what happens?Companies that have happy customers and engaged employees routinely outperform their competitors.

Benefit tracking dashboards will never go away but there will be a dawning awareness among business leaders that sustained enterprise-wide automation programs are positively impacting their firms bottom line.Contentious debates over the accuracy of automation benefit forecasts will fade over time as leaders come to realize that cost savings are simply a byproduct of doing things better than their competitors.

These are the secret realizations of individuals who have led successful automation programs at an enterprise-wide scale. Dont share them with your competitors!

This article is based on conversations with many automation leaders.Special thanks to Max Cheprasov (CEO, Ubersuggest), Dave Duvall (CIO, Discovery), Andy Fanning (Managing Director, CIGNA), Rich Gilbert (Chief Digital & Information Officer, AFLAC), Prakash Kota (CIO, Autodesk), Cindy Miller (Chief Digital Officer, Hanesbrands), Mohit Rao (Head of Intelligent Automation, Atlassian), Eric Reiner (CIO, Rapid7) and Randy Swanberg (SVP, Automation & AI, State Street Bank).

See the article here:

Discovering The Secrets Of Automation At Scale - Forbes

The Biden administrations cybersecurity executive order contains 37 pages of important new guidelines and requirements that will help protect our networksbut it remains silent on the critical issue of how automated testing must become a key part of that defense.

The order requires the federal government to accelerate migration to the cloud, adopt zero-trust architecture, and implement multi-factor authentication. It also demands supply chain vendors bake security by design into their software development process, and expects private-sector vendors to increase communication and collaboration with government agencies in order to harden cyber defenses.

All well and good. And to be sure, moving to the cloud can improve network security. Cloud service providers invest heavily in security innovation and provide a more homogenous and easily-secured footprint compared to on-premise infrastructure, much of it hobbled by years of security debt. Ultimately, however, security comes back to people, and people will always be prone to make mistakes.

Given the resources that Americas adversaries are pouring into their campaigns to find new ways to penetrate U.S. systems, a simple checklist of security best-practices will always leave us one step behind. We must think in terms of cyber readiness and become proactive, not just reactivewhich means doing more than checking compliance boxes; we must also do our best to attack and defeat our own network defenses. It is the only way to keep ahead of adversaries who are doing the same. And we must do so continuously, matching the cadence of adversaries who, by one measure, are sending 36 million malicious emails a dayto the Defense Department alone!

We know that penetration tests and red team exercises are the gold standard of cyber defense. But these are expensivefar too expensive to keep a continuous eye on every corner of the U.S. militarys networks, let alone the rest of Americas systems. Machine-based automation is the sole path to reaching the scale required. Artificial intelligence and machine learning can automate and scale security testing methods to the point where they can take on much of the work of cyber defense.Automation can help ensure that software not only enters production in a secure state but remains that way over time. Only via automation can authorizing officials ensure that inadvertent changes to systemsso-called configuration driftdo not open glaring chinks in the armor. Finally by continually probing the changing application landscape, AI can help ensure defenses remain effective over time.

AI is already being used for defensive cyber operations to automate monitoring, detection, and response to actual attempted breaches. Offensive penetration testing is more technically challenging, but progress is already being made on developing AI tools that, for example, can carry out better network reconnaissance, that can operate with enhanced stealth to avoid detection, or that are more efficient in cracking passwords.

The costs of building AI testing programs for our own systems are not trivialadvanced password cracking to test the integrity of our network security, for example, requires significant computational powerbut those costs will come down as technology progresses and the overall cost of compute falls. The real barrier to their adoption is conceptual: persuading budget appropriators that paying for prevention now is better and cheaper than paying for the cure later.

If the executive order is to be a watershed in this nations cyber defenses, rather than a missed opportunity at a critical moment, now is the time to embrace the power of AI to support our smartest minds in out-thinking even our most determined adversaries.

Kevin Tonkin leads product management for Rebellion Defenses cyber readiness products. Before joining Rebellion, Kevin led product and engineering at Coalfire, a global cybersecurity firm.

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The Automation Gap in Biden's Cybersecurity Order - Defense One

All the sessions from Transform 2021 are available on-demand now. Watch now.

FloQast, an accounting collaboration, automation, and close-management platform, has raised $110 million in a series D round of funding at a $1.2 billion valuation.

Financial close management is the business process of checking and adjusting account balances at the end of each month or other predefined period to publish compliant financial reports for regulators, managers, shareholders, or any other stakeholder that needs to know a companys current financial position. But getting a companys books into shape is easier said than done, particularly for larger businesses more often than not its a manual, time-consuming process rife with human error. This is where FloQast comes in.

Founded in 2013, Los Angeles-based FloQast works with a slew of high-profile customers, including Snowflake, Zoom, Lyft, GrubHub, Twilio, and Yelp. The companys flagship FloQast Close product serves as a centralized place for teams to manage all their recurring close-management work, including outstanding tasks; collaborative checklists and review notes for assigning tasks and viewing who needs to do what; and a transparent roll-up of all account reconciliations, replete with ERP and Excel integrations to automate part of the auditing tie-out process.

Elsewhere, FloQast includes support for automated alerts if amounts change after sign-offs are finalized, and users can configure the platform to automatically request more information from relevant parties if anything is missing during a close. Built-in analytics can also help managers track efficiencies by using historical data to spot month-end close trends over time and identify bottlenecks.

FloQast had previously raised around $93 million, and with its latest cash injection, the company is well-financed to capitalize on a growing push toward automation across the accountancy sphere. In the past few months alone, companies including Osome, Auditoria, Zeni, Lockstep, and Georges have all raised sizable sums to help accountancy teams automate repetitive and time-consuming tasks.

But perhaps more than that, FloQast is well-positioned to support the burgeoning remote work trend that is likely to linger long after the global pandemic subsides. Team collaboration tools such as Zoom, Slack, and Microsoft Teams have already benefited from a more distributed workforce, and FloQasts shared workspace is designed to fulfill a similar function for finance teams.

FloQasts series D round was led by Meritech Capital, with participation from Insight Partners, Redpoint Ventures, Sapphire Ventures, Coupa Ventures, Polaris Partners, and Norwest Venture Partners.

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Accounting automation and collaboration platform FloQast raises $110M - VentureBeat

While automation is not intelligence, the real value of automation, especially software automation comes from removing the bot from the human. The global pandemic has shown just how valuable automation has become for companies and governments alike to make sure that operations run smoothly, bottlenecks are avoided, and repetitive tasks can be taken away from humans to free them up for higher value tasks.

Timothy Goodwin, Deputy Director, Office of Organizational Policy and Governance at the United ... [+] States Patent and Trademark Office

However, if you want to see real value from automation, and in particular Robotic Process Automation (RPA), its important to know what these bots can and cant do, and how AI is being applied to help handle more complex tasks. USPTO uses automation and AI to improve operational efficiency and empower their highly-skilled Examining Corps. Additionally, they are automating various processes to lighten the manual load on their Examiners.

Timothy Goodwin, Deputy Director, Office of Organizational Policy and Governance at the United States Patent and Trademark Office (USPTO) shares how they are leveraging automation and cognitive technology at Americas Innovation Agency. Timothy will also be presenting at an upcoming ATARC CPMAI Methodologies and Best Practices for Successful RPA Implementation event on July 21, 2021, 2:00-3:00 p.m. to dig deeper into some of the questions below.

How are you leveraging automation at USPTO?

Timothy Goodwin: The depth and breadth of which automation technologies are being leveraged within USPTO is vast. It is a critical enabler for driving business value. Recently we have used AI/ML to reduce the manual patent classification actions performed by an examiner; RPA to free up valuable time performing suspension checks on trademark applications; and virtual Data as-a-Service (vDaaS) to increase quality of applications in development through on-demand provisioning of test data. All of which has helped propel more, and more automation capabilities and is enabling our agency to deliver higher quality services to the public.

How do you identify which problem area(s) to start with for your automation and cognitive technology projects?

Timothy Goodwin: I am going to narrow this question and focus on RPA. When we first started our RPA program in 2019, we were looking for any USPTO process that could be used to demonstrate capabilities. This started with a first-in-first-out model where requests being submitted were only helping individual or low number of users. Since then, we have evolved our intake process to look more broadly at the automation request and find critical problem areas impacting USPTO business lines. A recent example was developing RPA solutions to help reduce the backlog created from the high volume of trademark applications submitted over the past twelve months.

How do you measure ROI for these sorts of automation, advanced AI and analytics projects?

Timothy Goodwin: Measurements are always based upon the business value derived from the automations demonstrated capabilities. This can come in many different forms depending on the solution being implemented. For provisioning of cloud infrastructure, it can be something as simple as creating a routine that terminates idle virtual services when not in use, avoiding unnecessary expenses. For RPA, it can be looking at the number of productivity hours recouped from a single or multiple process instances automated. The key metric is always centered on asking ourselves how does this help disseminate and issue timely and high quality patents and trademarks?

What are some of the unique opportunities the public sector has when it comes to data and AI?

Timothy Goodwin: In very general terms, the public sector is stewards and has access to vast amounts of very unique data that is equally inaccessible by any other entity in the world. This of course, coming from the totality perspective and not from views available through open data platforms. There is immense potential combining these unique data sets with AI to advance research into every single discipline known today. Quite simply it is boundless. The challenges, on the other hand, are all over the place and span legal, technical, and ethical boundaries. However, Id like to point back towards our responsibilities as data stewards and ensuring public trust is being upheld. For me, this is the fundamental topic that should be addressed when determining how data should be used. Ultimately, the dilemma of how to use data and for what purposes related to AI have to be explicitly defined and vetted before pursuits are made to ensure we are exceeding the publics expectations.

How do analytics, automation, and AI work together at the USPTO?

Timothy Goodwin: USPTO data is unique and with that we have unique challenges and opportunities. The three areas are naturally woven together and build upon each other to enable advanced capabilities. Automations help feed our patent and trademark data lakes where preparations are made to address data quality and security. This in turn, mutually feeds our AI/ML models and eventually gets rolled out and provides data insights and visualizations to broader groups. All of this helps create a sustainable environment for conducting data driven decisions for the agency and ensuring USPTO can continually provide high quality services.

What are you doing to develop an AI ready workforce?

Timothy Goodwin: Workforce development within advanced technologies is already a challenge for many federal agencies. At USPTO we are fortunate to have strong leadership within the data science, analytics, and AI space from Scott Beliveau and our new emerging technologies director, Jerry Ma [For additional insights Jerry Ma presented at a previous AI In Government event, and Scott Beliveau will be sharing insights at the October 2021 AI In Government event]. With support from their teams, they are forging a new path for other USPTO personnel to follow by creating opportunities and allowing innovation to be explored. Enabling focused experimentation within AI that provides strong business value is one of the best tools we can leverage for developing our workforce. In the more practical sense, we have also been growing our workforce through traditional training and have had many employees participate in various levels of AI/ML and advanced analytics courses. [The best practices approach to doing AI and big data analytics is the CPMAI methodology, which large organizations are increasingly adopting.]

What AI technologies are you most looking forward to in the coming years?

Timothy Goodwin: I am really trying to keep an eye on how AI is evolving in the domain of cyber security research and development. There has already been a vast amount of work and success achieved in this area, to the point that any modern AV product is utilizing AI for static analysis and trending better with dynamic analysis. What I am most interested in is seeing how AI can heal vulnerable or compromise systems in real time. Knowing how vulnerability research is traditionally conducted, there are ample opportunities to utilize AI to prevent the viability of a bug from being exploited. Recognizing and disseminating AI driven patching actions before compromise occurs is what I hope matures in the coming years.

Read more here:

How Automation And Best-Practices Methodologies Are Transforming The USPTO - Forbes

Businesses are building a new kind of assembly line and this one is digital, staffed by software bots.

Why it matters: For all the hopes and fears around industrial robots, more progress is being made in the realm of digital workers: Bots that can perform a growing number of often tedious and time-consuming tasks in an increasingly online business world.

How it works: Intelligent automation takes the logic of a physical assembly line where the work of making something is broken into discrete, individual tasks that can be done more efficiently in sequence and moves it into the digital world.

Details: Kingdon uses the example of how Blue Prism works with banks on reducing credit card fraud.

By the numbers: Gartner projects that business spending on robotic process automation a part of intelligent automation will grow by nearly $1.5 billion in 2021.

The big picture: Just as Henry Ford and his peers were able to revolutionize manufacturing in part by breaking tasks down into an assembly line, intelligent automation works best when knowledge work can be broken down into discrete micro-tasks that can be handled by bots.

The catch: Like any other form of automation, intelligent automation can make human workers more productive individually but it also carries with it the longer-term specter of job loss as the bots get more capable and companies look to cut payroll.

The bottom line: Most companies are "still very, very early in the journey" of intelligent automation, says Kingdon.

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Software bots could be the future of business automation - Axios

It ismid-July as I write this. There arefour months until Regulation F is implemented. (I know some of you may think, "But the extension!" However, the effective date on the CFPB's pageremains 11/30, so we're going to stick with that.)

As many are no doubt aware, there are a lot of moving parts, not just with implementation of these new requirements, but management of these new requirements. And what has become clear, moderatingwebinars, listening in to other webinars, talking with complianceand operations people,and just paying attentionis this: If you aren't automating, you are setting yourself up for failure. Generally, mistakes can be mistakes and learning opportunities. For the debt industry, mistakes are actually dollar signsat best.

Here's a fun question for two different audiences:

Owners, CEOs: How many of your processes are automated and regularly audited?

Compliance and Operations: How many of your processesaren'tautomated and the audit process is, "Well, Terry just does that part and we have no idea how and look, it gets done, we can just never, ever, fire Terry or let Terry leave"?

To the owners, CEOs, compliance managers and operations managers who felt deeply seen by those questions: don't judge yourself. We're in a highly regulated industry with thin margins. That initial outlay for automation can look steep when you have not just one Terry -- but six or seven.

When tasks that are better automated are, instead, the responsibility of employees, the risks may not seem immediately apparent, especially if you have someone capable in charge of that task. Here are five reasons you want to invest in automation:

1) Terry Leaves.Or goes on vacation. Or has a catastrophic event. That can be the first wobble in a spinning top you thought was going to spin forever. In the worst case, Terry was doing things you didn't know Terry was doing, and you only find out once Terry is gone. You now have a gap in your processes.

2) You Can't Replace Terry Once Terry's Gone (We Miss You Already Terry). In situations where an automated process is automated by Terry, a human, and not T.E.R.R.Y., the latest in collection technology, you are more vulnerable than you've ever been. This key task is now going undone.

3) No One Knows What Terry Is Doing/Did/No Longer Does. Again: processes that rely on a human person will stop working when that human person is gone. The other key point:Terry might not even know that Terry's doing something that needs auditing or automation.Terry just thinks, "This is part of my job." Leaving automated tasks unautomated puts your company at risk of non-compliance.

4) The Longer You Wait to Automate What Terry's Been Doing By Hand, the Messier Untangling Those Processes Will Be. Automation isn't as simple as flipping a switch. And the longer Terry tracks what Terry's tracking, and fiddles with what Terry's fiddling, the less sure you can be that you're capturingallthe data-points necessary, or even fully understanding what Terry does.

5) You Can't Afford to Not Automate. Automation smoothes out processes, making them reliable, measurable, and dependable. It also allows Terry to focus on things Terry might be better at. (And, of course, this is just life under capitalism: you may find that you don'tneedTerry. Which can free up resources.)

There is never a good time to switch everything about your current business. It is absolutely going to be disruptive and there will be headaches.

But having said all that: Can you really afford not to?

For an indepth look at the ins and outs of automation, check out Telrock's whitepaper:Automation is Key to Collections Success: What You Need to Know

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What If You're Not As Automated As You Think You Are? - insideARM.com