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The automation conversation guide: How COEs and LOBs can find a common POV for business impact

You say ‘dayta,’ I say ‘dahta’

 

Writer George Bernard Shaw once famously observed that, “England and America are two countries separated by the same language.” The same can often be said of automation centers of excellence (COE) and their line of business (LOB) counterparts – with data as the common language to bridge the two worlds. Harnessing and leveraging data – especially in data- and document-intensive sectors such as insurance, financial services, and commercial real estate, as well as enterprise global services organizations – can be the key for COE to unlock productivity, operational efficiency, and competitive advantage for LOB operations.

The challenge is, as you’re likely keenly aware of, the path to engage with line of business partners can be a thorny one if expectations aren’t clearly set and met, if both sides aren’t equally invested, and if the technology involved isn’t best-fit for the people, processes and purposes in question. “Sometimes it feels a bit like family counseling,” says Chris Wells, VP, Research and Development at Indico Data and former Chief Data Scientist at Chatham Financial, a leading global risk management firm. “LOB might say, ‘I need to solve invoices.’ COE might want to think more broadly about how automation can work across applications.

And, ultimately, you need the combination of concrete thinking together with abstract thinking to get to real value. Because, in the end, everyone wants to get to the moment we shout, ‘We solved this!’” The other big challenge: 85% of all data in large enterprises is unstructured. As its name implies, unstructured data by its nature does not follow an established format or model, making it challenging to search and analyze. It can be generated by humans or machines. It can be text based, or not.

So, while some forward-thinking organizations have attempted to leverage their proliferating data with technologies like Robotic Process Automation (RPA), they’ve only been able to harness a meager 15% of what they have at their fingertips. “Too often, LOB has experienced that technology over promises and under delivers,” says Brandi Corbello, VP, Transformation, who heads up the automation center of excellence (COE) at global real estate leader Cushman & Wakefield. “We all want to perform better. But we need to generate familiarity, comfort and trust that the change we create will actually make a positive impact, without seriously disrupting the business.”

We all want to perform better. But we need to generate familiarity, comfort and trust that the change we create will actually make a positive impact, without seriously disrupting the business”
Brandi Corbello, VP, Transformation, Cushman & Wakefield

Featuring insights from automation leaders at MetLife, Cognizant, Cushman & Wakefield and more leading enterprises, this conversation guide will make it plain for COEs how they can:

• Better understand LOB’s perspective and better collaborate

• Speak effectively with LOB colleagues

• Uncover the right use cases to solve with automation technologies

• Move forward together with LOB in order to achieve their goals

What’s more, this guide will introduce a powerful new platform that empowers COE and LOB to more easily and effectively work together, while simultaneously addressing the unstructured data imperative.

COE and LOB: seeing the world through each other’s eyes

 

COE and LOB leaders are subtly divided by stylistic differences. It’s just how they’re wired: LOBs typically skew toward the black and white, more concrete, urgent matters; COEs, on the other hand, are focused more on the grey in between, the “what-ifs” of what the organization can (and should) become.

When the relationship between the two groups falters, the result can be stifling inertia – a danger during a time when the speed of business has never been greater. For COE to successfully partner with LOB, it’s important to see the world from the other’s perspective.

COE strategies for supporting lines of business

 

1. Focus on business value first

Talk about business impact, then discuss how technology can address your organization’s challenges in new ways. Demonstrate how new automation technologies – or further leveraging and maximizing existing ones – can deliver impact on the bottom line, from mitigating risk and accelerating revenue to driving out costs and improving customer/employee experience. “Most challenges stem from the fact that COEs take too much of a technology-based approach,” says Cushman & Wakefield’s Brandi Corbello. “Too many times, COEs say that they have a great new tool and that we should use it, instead of asking what gives the LOB real heartburn.

There’s a lack of storytelling and education – a lack of articulation of the business value. We need to clearly demonstrate what any automation use case means today and for the future of the business.” “For relationship management, we have a robust, mature process for approaching new or existing LOBs,” shares Sean Nicolello, VP of Intelligent Automation for MetLife. “First, we look to understand their full strategy – what they want to do, what they need to do, gaps, problem areas, opportunities, et cetera. Then we understand their processes – what are they, review any documented processes and have working sessions to help them identify areas for automation. We use this as a catalyst to start building a pipeline of use cases.”

2. Start small, move fast, prove performance

Begin by focusing on how to get quick wins alongside LOB by solving problems that drive business value. Identify the simplest and highest value use cases from a business perspective, proving value quickly, including the value of your COE/LOB collaboration. MetLife’s Nicolello shares his approach: “Our first attempt is to find use cases where we can quickly and easily provide value – quick wins that we try to produce in a matter of two weeks to production. This shows the technology is real and it’s not a year-long turnaround. Then we go to higher value use cases with greater complexity and keep the wins coming.”

Chris Wells offers an analogy to help kickstart automation projects: “When you start to write a government grant proposal, you ask what the smaller question you could ask is. I recommend the same for automation. Ask if you can do something the ‘dumbest way possible.’ Is there an integration with an existing technology? Can we avoid big builds? I’m a believer in laying out a big project with lots of little gates to get through – so that you can quickly see what you can and can’t do.”

3. Build In commitment from the beginning

Set meetings with partners from all corners of the organization. Talk innovation, talk automation, talk processes, and keep discussions broad and open. But be sure to require shared engagement and commitment. The goal is to get stakeholders believing – and participating – in the process. “I would take a meeting from anyone who wants to talk about automation, in any context,” says Chris Wells. “And then I’d give the LOB leader ‘homework’: gather these three or four requirements. If they did the homework, I knew they were serious. If the business wasn’t committed, we wouldn’t spend the time on trying to engage in an automation project.”

Engagement from LOB also must be encouraged and consistent. Brandi Corbello suggests, “LOBs often feel like automation initiatives are conducted in a black box, without being consulted on the business requirements. COE needs to keep LOB super-involved in all sprints, so that they can see development as the project is progressing. As a result, they’ll enjoy it more because they have visibility and exposure.” By the same token, Chris Wells adds that LOB needs to be continuously engaged – and accountable. “I’ve seen this play out too many times. If LOB isn’t coming to the meetings, you’re not going to get anywhere. You need to marshall LOB and make the case that this makes the difference for them.”

4. Empower rather than eliminate

LOBs and their employees are often uncomfortable with the concept of automation, thinking that a digital workforce will replace human resources – or that it will never be as accurate. In some instances, yes, automation may alleviate the pressure for additional headcount and machines are only as accurate as their training allows. But, more often than not, COE leaders find that people remain missioncritical to any automation – and should not only reassure LOB leaders of that fact, but inform them that their people are pivotal to success.

Enabling enterprises to reach the highest levels of efficacy very often involves a human review at the end of a process – there needs to be a “human in the loop.” That really gets to the concept of “citizen data scientists,” where the business people who perform the process are the ones who actually use the intelligent tools involved in automating processes. “Larger application vendors will say ‘We can do all of this,’ but there’s a human component to interacting with that,” contends Brandi Corbello. “I don’t disagree [that applications can do much of the work], there’s transactional type work that needs to be done. But the real question needs to be about how we embed automation stacked over human involvement.”

Humans are also absolutely essential to training the AI models that make automation possible; effective training data is one of the biggest challenges in AI. The ability to accurately capture real-world data is pivotal to the creation of high-performance machine learning models.

Abhinav Kolhe, Senior Director, Digital Strategy and Automation Solutions with Cognizant, agrees: “You need the ability for business people to use the automation tool to tag and annotate data to create models. In the context of unstructured data, the ability to give feedback to the model, the learnability part of the tool, is one of the most important things. Otherwise, you continually have to go back and retrain the model.”

5. Be prepared for tough conversations about process

Many COE leaders find that the introduction of an automation platform can reveal problems in existing business processes. By its nature, automation technology will break down and/or amplify biases, weaknesses or inconsistencies in a human-driven process. Of course, this can be a touchy subject with LOB counterparts, but being candid and data-driven will ultimately drive greater efficiency and longterm sustainability for the business.

“With a human workforce, organizations can paper over the cracks, because humans can adapt and react to change with a degree of flexibility that bots don’t have,” states Vishesh Bhatia, a process automation expert with Cognizant. “The bot continues to perform the old steps. It will either terminate or perform an undesired activity that might have a negative impact, kind of like a train going off the track.”

“Sometimes, a line of business brought us a ‘really defined process’ for us to automate,” recalls Chris Wells. “Then, we would take the process into an automation tool and LOB would claim the tool didn’t work [when the results weren’t satisfactory]. The truth was, the AI model couldn’t learn, because there was a hidden human variable. No two data labels were the same, and LOB teammates were treating data differently. Success all depended on who was doing the work.” Wells adds, “By investing in automation, you can truly capture a process, reinvent it and make it scalable.”

6. Measure success by KPI and OMG!

Help business partners learn to embrace failure in the context of innovation. Shaking up the status quo leads to thinking differently and experimenting for the sake of evolution. Set up science-worthy forms of measurement and assessment to empower quick pivots as needed, with clear data to back your decisions. And set clear expectations that achieving results can and will take time.

Chris Wells jokes that, in his experience, “Business leaders go straight to accuracy metrics to judge success like a drunk looks for their keys under a streetlight – because that’s where they know to look. You have to help redirect everyone’s thinking to focus on incremental successes and be honest about humans not being one-hundred percent accurate either. You need to work together to figure out how to make solutions more effective.” Automation leaders also agree that the true measure of automation impact is as much emotional as it is mathematical.

“I know we’ve been successful when the business says they’re realized the benefits of the automation and want to do more – when the process subject matter experts are excited about the automation,” says Cushman’s Corbello. “There’s a look,” observes Chris Wells. “It’s a look of excitement, almost incredulity. That’s how you know: the ‘I’m gonna go tell everyone I know about this’ look. That’s when the new automation projects start rolling in.”

New tools to empower your LOB partners

 

At a time when only 20% of AI-enabled initiatives make it to production, the COE leaders cited in this document, as well as many others in data- and document-intensive industries, are empowering LOB with automation by implementing solutions from Indico Data.

Indico Data’s pioneering Unstructured Data Platform allows enterprises to ingest unstructured data at massive scale and add structure, enabling them to do what’s been impossible with traditional automation and analytics tools: realize the unlimited potential of their unstructured data.

With the Indico Platform, enterprises can:

The Indico platform can handle the gamut of document processing needs, whether it involves highly structured documents, completely unstructured or something in between. It’s effective because it’s built on a database of more than 500 million labeled data points, providing a deep base of knowledge that gives it the context required to “read” and understand virtually any type of content.

Empowering LOB: enabling citizen data scientists everywhere

What’s more, the Indico Platform was built to automate processes without advanced data science expertise. The process through which companies use Indico to build data models is simple and highly effective. Business subject matter experts label the data points they deem most important to whatever process they’re looking to automate.

As they apply labels, the model is updated on the fly and will start to show predictions on subsequent datasets. Once you’re comfortable with the predicted results, you’re done building your model. The beauty of this approach is that the people who understand the business problem and the desired results – those on the business side of the house – are the ones who train the model.

With Indico, there’s no need to try to explain to a data scientist what you’re after and then hope you get the appropriate results. Citizen data scientists can create models themselves. Everything is in plain English and users can have fully working models in a matter of hours or days, not weeks or months.

Real-world success stories

Indico’s approach has delivered a 97% success rate in moving customers’ unstructured data projects successfully into production. From there, the business impact of the Indico Unstructured Data Platform has been immediate and pronounced. Here are several examples from the COE experts who contributed to this guide, and more:

About Indico Data

Indico is The Unstructured Data Company, enabling enterprises of all sizes to automate the intake and understanding of unstructured documents, emails, images, videos and more; analyze unstructured data, extracting actionable business insights and intelligence; and apply this data to create new application experiences to transform manual and inefficient processes into powerful solutions to solve complex business challenges. Through the Indico Platform, enterprises can gain rich insights and maximize the value of their existing software investments, including RPA, CRM, ERP, analytics and more. Indico serves leading insurance, financial services, banking, real estate and other document-intensive organizations, including MetLife, Chatham Financial, Cushman & Wakefield, and Waste Management.

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