The New World of AI-Powered RPAs in Business

The Brave New World of AI: Disrupting the Old Guard of Insurance and Real Estate

The insurance and real estate sectors have long been mainstays of the global economy. Their structures, workflows, and operations, in many respects, still echo the ways of bygone eras. With piles of paperwork, manual underwriting, and prolonged property appraisals, they offer a stark contrast to the rapid digitalization that's reshaping industries worldwide. Yet, the winds of change are finally blowing, and Artificial Intelligence (AI) stands at the helm.

The Old Guard: Insurance and Real Estate's Time-Tested Models

To truly appreciate the seismic shifts AI is inducing, let's briefly revisit the foundations upon which these industries were built.

• Insurance: Traditionally, the insurance industry thrives on assessing risk and underwriting policies. Human underwriters sift through an applicant's details, make judgment calls on potential risks, and then assign premiums accordingly. Mistakes? Inevitable. Delays? Expected.
  

• Real Estate: Picture this - a potential homeowner decides to purchase a property. This instigates a long chain of events: property visits, discussions with brokers, price negotiations, and finally, legal paperwork. Each stage is painstakingly manual, and often feels archaic in today's digital age.

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The AI Revolution: Timely Interventions and Value Additions

What if we could streamline these processes, make more accurate risk assessments, and even predict housing market trends? Enter AI.

• For Insurance:‍
• Automated Underwriting: Machine learning algorithms can evaluate potential risks based on vast data sets, past claims, and even social media behavior. No coffee breaks, no biases, just rapid, accurate assessments.
• Predictive Analytics: With AI, it's not just about assessing the 'now'. Predictive models can forecast potential future claims, allowing insurers to be one step ahead.
• Chatbots and Virtual Assistants: Need to make a claim at 3 am? Why wait for office hours when an AI-driven chatbot can guide you through the process instantly?

• For Real Estate:‍
• Virtual Tours: With AI-powered VR, potential buyers can tour properties from the comfort of their homes. It's like binge-watching your favorite series, but for houses.
• Property Valuation Models: Instead of waiting for human appraisers, AI algorithms can assess property values in real-time by analyzing market trends, recent sales, and even neighborhood safety
• Predictive Selling: What if you knew a neighborhood was about to become the next big thing? AI can analyze trends, news, and other data to predict which properties or areas are set to appreciate in value.

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Time is Money: AI's Gift of Time

The adage "Time is Money" has never been truer. Let's do some quick math. If an underwriter spends approximately four hours assessing a policy, and an AI can do it in fifteen minutes, we're looking at a time-saving of 87.5%! And that's just one policy. Similarly, for real estate, consider the cumulative hours potential buyers spend touring properties, sometimes revisiting, and then deciding. Virtual AI tours and instant valuations could slice this time by more than half.

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The Contrarian's Perspective

But let's play devil's advocate for a moment. What about jobs? And can we truly trust machines over human judgment?

It's essential to remember that while AI can enhance efficiency, the human touch remains irreplaceable. The idea isn't to replace jobs but to augment them. Human underwriters and real estate agents can focus on more complex tasks, leaving the routine, time-consuming ones to AI.

And as for trust? It's all about balance. The perfect insurance or real estate model would be a blend of AI efficiency and human intuition. Kind of like the perfect latte – a blend of robust coffee (AI) and warm milk (human touch). ☕

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Riding the AI Wave

The infusion of AI in insurance and real estate isn't just about innovation for the sake of it. It's about addressing glaring inefficiencies, adding unparalleled value, and most importantly, catapulting these industries into the modern age.

For those still clinging to the old guard's methods, it's time to adapt or be left behind. As we stride into a future where AI becomes the norm, the question isn't whether we should embrace it, but how swiftly can we?

AI is no longer just a subplot in a sci-fi novel. It's here, it's now, and it's reshaping the way we perceive value, time, and efficiency and the way we implement robotic process automation (RPA). 

RPA sits among the group of emerging technologies with great potential, including AI, IoT, and augmented reality. While these technologies have an almost sci-fi feel and make for jaw-dropping demos, at their core they are designed with one thing in mind — to help businesses succeed.

While the robotic part may sound ominous, robotic process automation software can transform how your business operates. Bots perform rote manual tasks at a higher speed and accuracy than human workers can. These bots operate through user interfaces, so they can connect to IT systems and applications without APIs, automating a variety of legacy processes.

Scale for success While businesses that have deployed RPA bots have seen success, they aren’t always maximizing their investment. Forrester Research found that 51% of companies with RPA implementations have deployed 10 or fewer bots. That percentage drops down to just 21% of companies with 11-49 bots, and 8% with 100-199 bots. While committing to the investment in RPA technology is a solid first step, many RPA deployments have failed to scale.

So how can businesses maximize their RPA investment? By treating it as one of several automation options. Organizations that succeed with automation take an end-to-end approach and identify the right type of automation to apply at each step. (Actually, Forrester Research VP and Principal Analyst Craig Le Clair and Nintex Chief Evangelist Ryan Duguid broke down all things RPA in their recent webinar: The Real Truth About RPA (and its role in the Process Automation landscape)).

Everyone will have different opinions about this next thought-bubble: What are the right steps to take to achieve "success" in automation:

Combining Robotic Process Automation with Business Process Management RPA bots mimic human keystrokes, but they cannot make decisions. As Le Clair explained, BPM platforms structure the ways in which RPA bots can function, creating tangible use cases where RPA can makes the biggest impact.

These are among the most common:

RPA bots take work from a BPM queue The most straightforward method sees RPA bots pull work from the traditional back-office workflows in the same way a human would.

BPM routes around failed bot to human queue It will take time, along with trial and error, to fully streamline RPA bots within a business’s existing IT systems. BPM orchestration can route failed bot processes to a human queue, where a worker can determine what went wrong and complete the work.

RPA serves as middleware to core systems and web sites Unlike BPM, RPA doesn’t require any data integration to other IT systems. RPA gathers data from core systems and websites without integration and can connect with systems that don’t have APIs.

Understanding the real-world implementation of robotic process automation software is the first step to making it work for your business. The next step is translating that understanding into a defined RPA initiative.

3 steps to RPA success

1. Start simple When you’re eager to start automating, it can be tempting to change processes immediately and on a large scale. But the trick is to start simple. Find quick and easy wins: Fix processes that are obviously broken and solve meaningful problems. This shows people how automation can have a tangible impact on their day-to-day responsibilities and gets them excited about what can be accomplished next.
   

2. Be agile It’s important to remember two things about process: It’s always changing, and it’s never perfect. Striving for the perfect process will usually mean you don’t get anything done. Focus on getting started, getting feedback, making changes and adapting. This allows you to continue updating processes to match the evolution of your business.
   

3. Start the change movement As process automation grows within your organization, people should feel empowered to take charge of improving their own workflows. This often starts at the departmental level, and then naturally rolls out to assisting colleagues and other departments. Form a structure around this and you begin to create a culture of change.
   

RPA — despite its impact, potential, and popularity — is only one part of the full automation tool kit. Process excellence requires a comprehensive approach to improving how work is conducted, from start to finish. We break this down into three steps:

1. Manage Discover, map, and document your existing business processes. Share that information across the organization so every individual understands how work gets done.
2. Automate Once processes are mapped, you can make informed and intelligent decisions about how those processes can be automated. Find the perfect match — from RPA to Forms, Document Generation, eSignature, mobile.
3. Optimize Newly automated processes are rarely perfect. You’ll find bottlenecks, breakdowns, and opportunities for improvement will appear over time. Process analytics will help you monitor, measure, and improve processes to steadily improve them.

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The Future of RPA 

As RPA matures, it is increasingly being combined with other cutting-edge technologies like artificial intelligence, machine learning, and natural language processing. This allows RPA bots to handle more complex tasks that previously required human judgment and discretion.

For example, RPA bots can now:

• Read and understand unstructured data like emails, scanned documents, and handwritten notes using optical character recognition and natural language processing. This allows them to extract relevant information without strict data formatting requirements.
  

• Make smart decisions and recommendations by incorporating rules-based logic, decision trees, and machine learning algorithms. Bots are not simply executing repetitive scripts anymore - they can adapt to new situations and learn over time.
  

• Communicate naturally using chatbots that understand speech and text. Bots can have conversational interactions with end users via voice assistants, live chat windows, and more.
  

• Mimic human behaviors like mouse movements and micro-pauses during data entry. This makes bot activities virtually indistinguishable from a human user.
  

As RPA integrates more AI capabilities, we're seeing the emergence of hyper-automation - where bots are orchestrating end-to-end processes with little to no human involvement. This level of automation was unthinkable just a few years ago.

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Benefits of Intelligent RPA 

RPA and AI promise many benefits. Just looking around at the types of companies that are also rapidly growing, the main themes seem to be the same:

• Higher automation rates for complex business processes that previously required human judgment.
• Increased efficiency, accuracy, and consistency when dealing with large volumes of unstructured data.
• Lower operating costs by reducing human labor requirements.
• Improved scalability that allows processes to adapt quickly to changing business needs.
• Enhanced compliance by minimizing errors and providing detailed audit trails.
• Higher job satisfaction for human employees by removing tedious repetitive tasks from their workload.
• Quicker ROI as bots ramp up in capability and business impact.
  

Of course, AI-enabled RPA also introduces new challenges around change management, bot training and governance, system security, and more. But the possibilities are endless when intelligent bots can replicate almost any manual task.

RPA is Evolving into a Central Nervous System 

As RPA merges with artificial intelligence, it is taking on an increasingly crucial role as the connective tissue that links disparate technologies like AI, analytics, IoT sensors and more. RPA provides the integration layer to orchestrate data flows and tasks between systems in an automated fashion.

Gartner refers to this concept as a "digital brain" while Forrester calls it "RPA plus" or "connected RPA." But essentially, RPA is evolving into a central nervous system that coordinates activity for the broader digital ecosystem.

This positions RPA as a critical building block for the future enterprise tech stack. The new capabilities of connected RPA open the door for innovations like:

• Event-driven automation that can respond to real-time data from IoT devices, website analytics, customer databases and more.
• Predictive automation where bots proactively take actions based on machine learning forecasts and recommendations.
• Emulation of complex human decision-making for advanced use cases like fraud detection, customer service and medical diagnosis.
• End-to-end intelligent automation where bots manage the orchestration of workflows across multiple platforms.
• Hybrid human+bot collaboration on cognitive tasks using augmented intelligence.
  

And since so many of my insurance & real estate industry friends have asked me “well what can we actually do with this UB? This all sounds great but what does it do for us?,” I want to provide some examples of how this actually can be utilized within the insurance and real estate sectors.

These sectors are prime candidates for transformation through robotic process automation because they still rely heavily on manual paperwork, data entry, and human reviews - processes that intelligent bots can optimize.

In insurance, RPA is automating critical workflows like underwriting, claims management, document processing, customer service and more. Bots can rapidly pull data from documents, systems and forms to expedite assessments and approvals. They can also speed up customer communications for policy changes, renewals and reminders.

McKinsey estimates that enterprise-wide automation can reduce insurance operating costs by 25 to 40 percent. Top insurers like Japan's Fukoku Mutual Life Insurance are targeting a 30% workload reduction from RPA. Others like Spain's Generali see bots enhancing productivity by the equivalent of 200 Full-Time Employees (FTEs).

The benefits also extend beyond cost savings. Insurance processes automated by RPA have much faster turnaround times. Bots operate 24x7 with no delays. Consistency and accuracy also improves dramatically when manual errors are eliminated.

In real estate, RPA bots are optimizing back-office processes around lease abstraction, tenant billing, document indexing, property valuations and transaction management. Automation handles huge volumes of paperwork and forms involved in real estate deals rapidly.

According to JLL, large real estate firms have over 1,200 use cases for RPA, ranging from simple data transfers to complex process automation. Portfolios with thousands of properties can leverage RPA to reduce the manual overhead of tracking lease details, paperwork and contracts.

While RPA was the gateway, the next evolution is incorporating intelligent capabilities like machine learning, natural language processing, and predictive analytics:

For example, in underwriting:

• ML algorithms leverage thousands of data variables and past claims data to assess risk profiles of applicants and predict potential payouts. This enhances underwriting accuracy.
• Bots use NLP to extract unstructured information from insurance questionnaires, doctor's notes, social media profiles and other sources to get a 360-degree view of the customer. This provides deeper risk insights.
• Predictive analytics models are developed by bots using techniques like regression analysis. These models identify high-risk applicants and forecast renewal rates. Insurers can price policies more precisely.
  

And in claims management:

Computer vision and NLP allows bots to read various claims documents and extract relevant information without any manual effort. This accelerates processing and settlement.

Bots can categorize claims based on type of coverage, analyze bills and estimate settlement amounts autonomously by looking at historical patterns.

Predictive models identify claims likely to result in litigation based on the claimant profile, attorney involved, type of injuries etc. Insurers can proactively intervene to settle faster.

Anomaly detection techniques detect potential fraudulent claims early. Bots initiate additional validation processes automatically in suspicious cases.

What about front-facing process automation? In customer experience, we can build:

• Chatbots using conversational AI handle customer inquiries efficiently via voice assistants, mobile apps or websites. This provides instant 24/7 support.
• Virtual assistants guide customers through processes like updating policies, filing claims and scheduling medical exams. This reduces inbound call volumes.
• Smart FAQ recommendations based on customer queries improve self-service rates. Customers get faster resolutions.
  

For real estate, we’ve been approached by many to build fragments of tools, however the real bread-winners will be workflows like:

• Computer vision compares images of listed properties with comparable sales to estimate valuations. This automates appraisals and speeds up deals.
• NLP is used by bots to extract key attributes of properties like size, amenities, age, etc. from listing descriptions. This populates structured listing data.
• ML algorithms analyze market trends, recent neighborhood sales, and other data to predict which areas or properties will appreciate in value. This provides an investment edge.
• Chatbots respond to buyer inquiries on listings, provide property details, schedule tours and assist in deal documentation. Much of the buyer interaction is automated.
• As RPA capabilities advance, we will see end-to-end automation of processes like claims settlement and mortgage processing with little to no human intervention. Already bots are handling sub-processes in a seamless supervised manner.

Your head should be spinning by now - there are endless workflows that can be built. Because intelligent RPA in insurance and real estate drives faster growth, improved customer experience and competitive differentiation for companies that embrace it. RPA is quickly becoming the "central nervous system" underpinning digital transformation in these sectors.

As RPA interconnects with more emerging technologies - fueled by intelligent automation capabilities - we will see it enabling the self-regulating, self-optimizing and autonomous enterprise of the future. The smart, adaptive enterprise where manual intervention is minimized thanks to AI-guided software robots.

For business leaders today, it is essential to start viewing RPA as a launch pad to scale AI across the organization. Connected RPA should serve as the foundation for automating higher value tasks and accelerating digital transformation initiatives.

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There are Infinite Possibilities with Intelligent Process Automation

While pure RPA provides some workflow efficiencies, the real game-changer is intelligent process automation (IPA) - which combines RPA, AI, analytics and other emerging technologies. This delivers automation possibilities that are virtually infinite:

• Automating complex business processes end-to-end without any human involvement. Bots can incorporate rules engines, AI and analytics to handle processes autonomously.
• Scaling automation across the enterprise by having bots monitor enterprise systems, trigger processes proactively and maintain automation workflows.
• Developing cognitive bots that learn from user feedback and past activities to improve performance over time. Bots evolve "digital brains" to become smarter.
• Creating a seamless experience by blending robotic automation, AI and human activities. Bots and humans work together on collaborative tasks.
• Discovering unseen process optimization opportunities by mining process data with algorithms. Machine learning reveals bottlenecks and improvement areas.
• Providing intelligent process monitoring capabilities where bots can generate insights from real-time analytics.
• Enabling natural interactions using speech recognition, NLP and machine learning for advanced communication with bots.
• Extracting value from unstructured data using AI for document processing, image recognition and language understanding.
  

The more intelligent bots become, the more they propel automation from simple repetitive task replacement to assisting humans handle complex cognitive activities. This makes IPA a critical driver of enterprise digital transformation.

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Overcoming Resistance to Intelligent Process Automation 

Let’s shift gears: Some of you may think this is great, but others in your organizations will think differently. As you nudge your co-founders or your department heads to adopt intelligent process automation (or bring in a company like Revscale Digital™ so you can stay focused - sorry, shameless plug), you WILL face organizational resistance. Since IPA involves disrupting existing ways of working, employees may see it as a threat. Concerns around job losses, skill redundancies, and loss of control are common.

Leadership plays a key role in driving change management to ensure a smooth transition. So do your employees / your co-workers. What can you do? Here’s what I did when I was building a consulting firm many years ago:

• Involving staff / c-level early to listen to their concerns. Get buy-in by having them help assess automation opportunities.
• Being transparent on how automation will impact roles and responsibilities. Provide clarity on redeployments.
• Highlighting how automation will free up staff capacity to focus on value-add activities.
• Investing in upskilling programs to prepare staff for working alongside bots.
• Starting small, delivering quick wins, and iterating. Build confidence in automation through incremental success.
• Having ongoing communications to share automation progress updates and results.
• Ensuring the right governance model for bot management and oversight.
• Recognizing employees who embrace and promote automation in their work.
  

With a thoughtful change management approach, organizations can turn staff into automation champions. People will be more motivated if they feel empowered by automation versus threatened by it.

Sustaining Automation Excellence - What if your workflows suck? Or worse…they’re great but no one knows how to use them?

RPA and IPA offer game-changing potential to transform business operations. But the key to securing long-term success is building competencies to sustain ongoing automation excellence after early bot deployments:

• Developing Centers of Excellence to provide governance, best practices, training and support.
• Creating Automation Academies for upskilling staff to thrive in an intelligent automation future.
• Using process mining and analytics to keep optimizing processes. Continuously monitor automated workflows.
• Leveraging Hyperautomation platforms that integrate RPA, AI, analytics, BPM, BI and other tools.
• Architecting reusable components and libraries to maximize bot redeployment.
• Exploring Citizen Development to empower business teams to build bots without heavy IT involvement.
• Tracking automation metrics like usage, throughput, ROIs and customer satisfaction to showcase benefits.
• Maintaining security hygiene as usage scales. Use tools like credential vaults and access controls.
• Promoting a culture of automation across teams to drive adoption from the ground up.

Sustained success requires both a strategic roadmap and tactical discipline - from core development skills to change management programs. The automation journey is a marathon, not a sprint.

Human and Bot Collaboration A common concern around intelligent automation is that bots will replace human workers entirely. However, the most transformative impact occurs when humans and bots work together in a blended workforce.

Bots are great at handling repetitive, rules-based tasks but still struggle with complex cognitive capabilities that come naturally to people. Combining the strengths of both drives productivity and performance to new heights.

There are so many ways to work together (instead of against or around) with these bots.

Hand-off Model: Humans and bots each own distinct steps in an end-to-end process. The hand-offs are structured for seamless transitions between human and bot tasks.

Co-creation Model: Bots assist humans by providing recommendations, analytics and decision support. The human leverages bot capabilities to perform at an optimized level.

Augmentation Model: Humans train bots through demonstrations, feedback loops and knowledge transfers. Bots continuously learn from users to improve over time.

Hybrid Model: Certain process steps are fully automated while other steps involve human-bot collaboration. The mix is designed for optimum efficiency and outcomes.

With a collaborative approach, humans get freed up from repetitive tasks so they can focus on the work only they can do. Intelligent bots empower people to achieve more.

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Plus, tools like conversational interfaces are making human-bot interactions feel more natural as well. The future workplace will be a flexible blend of automation, augmentation and teamwork.

But you need to bring these together in a central source. We use Slack (no, this is not a sponsored post). Slack has many integrations, but the downside is that Slack can get expensive. Anything can really…just be smart about using your credits / startup programs that are available to you. Or if you’re a corporation / part of an enterprise, you need to triage what’s more important (that company softball off-site or making your life easier at work?)

You NEED to have a centralized orchestration platform to coordinate all bots, processes, workflows and data. Off the top of my head, I can name a number of things that we’ve implemented ourselves at Revscale (and other companies that I have launched or invested in):

• A unified dashboard to monitor and control all automation activities from one place
• Centralized bot credential and permissions management
• Shared access to bots, workflows, objects and data sources
• Recorder tools to easily create new bots without coding
• Analytics and dashboards for process mining and optimization
• Role-based access control and security governance
• Scheduling and workload balancing capabilities
• Connectors to link with apps, databases, APIs and other systems
• Management of the end-to-end automation lifecycle
  

Orchestration enhances resilience, scalability and governance for automation programs. It prevents fragmented siloed bots that lead to inefficiencies and risks.

With a robust orchestration framework, organizations can:

• Reduce bot sprawl with reusable libraries and templates
• Accelerate bot deployment by composing workflows visually
• Improve compliance with centralized auditing trails
• Promote collaboration across toon development teams
• Scale bots seamlessly across departments and geographies
• Enable IT and business teams to control automation democratically
  

Orchestration powers the shift from basic task automation to an intelligent digital workforce. It is automation maturity in action.

Unlocking Transformative Value with Hyperautomation ← Sounds like a bullshit buzzword title for SEO purposes, right? Well yeah, it is. Anyways…

The future of intelligent process automation - and the enterprise - is hyperautomation. This is the concept of end-to-end automation across the breadth of operations, enabled by technologies like RPA, AI, ML, analytics, BPM and IoT.

With hyperautomation, manual processes are fully automated without human intervention. But more than just simple robotic task replacement, hyperautomation leverages AI capabilities to handle dynamic and complex work, achieved by connecting RPA bots with other tools:

• Natural Language Processing helps bots understand human inputs and conversations
• Document Understanding extracts info from unstructured content using AI
• Machine Learning allows predictive and prescriptive recommendations
• Rules Engines and Process Mining optimize workflow steps
• Integration with IoT devices and digital twins enables real-time automation response to sensor data

…which allow us to deliver:

• End-to-end process transformation versus siloed task automation
• Increased automation penetration for unstructured processes with AI
• Higher quality and consistency versus human work
• 24/7 automation provides massive scalability
• Frees up human talent to focus on innovation
• Data-driven continuous optimization of operations
  

Hyperautomation is the pinnacle of intelligent process automation. With the right strategy, it can help forward-thinking organizations realize their digital transformation vision. My best advice from all of this? Be real with yourself: Do you need this? Yes. Will it 5x your revenue tomorrow? Not likely. But will you be able to do more, in less time, for less money, with greater efficiency? Yes BUT only if it is done right. My cousin paints

Because the future enterprise will run on hyperautomation, both internally and externally.

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More to come…

Thanks, UB

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