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Is enterprise automation heading into turbo gear?

This article [part 1/2] has been written Tom Henriksson, GP at OpenOcean and originally published here.

The terms “artificial intelligence” and “robotic process automation” have been subjected to a lot of hype. For a good half of the last decade, AI and RPA have been lauded in boardrooms, but often shunned by their employees, resulting in countless proof-of-concepts, and few truly intelligent automated processes and workflows. But are things about to change?

OpenOcean is a European VC focused on early-stage data-intensive B2B software startups. We believe that automation will become so seamlessly integrated and easy to use by organisations that it will just work in the background, providing a myriad of intelligent services and solutions. We set out to further explore this topic via our DataSeries community of global data leaders, gathering a roundtable of experts, top practitioners and leading vendors of automation technology with the goal of unpacking the real state of enterprise automation and the role of AI.

A pandemic pushes progress.

With employees now conducting most of their business processes from their computers, the pace of and need for technology adoption has accelerated. Many companies previously made some commitment to automate, but the pandemic has shifted what was strategic thinking into what is now considered an operational imperative.

Many have jumped in with both feet, in some cases investing capital expenditure budgets in order to accelerate digital transformation. Changes that most insiders thought would take five or more years have, in some cases, happened in 12 months.

C-level executives now see the make-or-break critical importance of ensuring automated solutions are seamless to deploy and easy to adopt. Now that more employees across the enterprise have morphed into users, C-level executives are more apt to drive additional processes forward to automate more complex tasks. This maturation has resulted in increased strategies and budget allocated to automation toward projects that have been waiting in the wings for acceleration, particularly at large international organisations. Debraj Dutta, Partner at EY, summed up the adoption of enterprise automation, “It is no longer nice to have, it is bread and butter for survival.”

However, some corporations are falling behind. While many companies have embraced technology adoption and transformation in order to push through the crisis, others have pulled back on all their budgets as a precautionary method to weather the storm. That is creating what Guy Kirkwood, Chief Evangelist of end-to-end automation leader UiPath, is calling “a bifurcation between haves and have nots.” He predicted that by the end of 2021, the companies that have lagged behind adopting AI in process automation may be in serious trouble.

Other corporations have handled the pandemic differently. For example, one half of large financial institutions were able to work through their internal policies rather quickly and let their employees work from home, resulting in adaptation in a matter of days. Of the other half, those that outsourced their operations to third parties encountered multiple challenges. Outsourcing a specific workforce abroad led to struggles with different covid regulations and complexity in planning ahead. These seismic challenges have many questioning the operating model as a whole.

Automation: top-down or bottom-up?

Implementing a holistic enterprise automation process is tricky with only so much change that can be done at a time. The pandemic has forced many companies into adapting automation, subsequently preparing them for the decade to come, but where do we see most automation solutions coming from within the enterprise?

The panelists were somewhat split on how much power to automate should be put in the hands of employees who are further down the corporate hierarchy. There was general agreement that putting more automation power in the hands of employees is a good idea, but it must be coupled with robust governance structures in order to prevent potential problems or abuse. Larger enterprises may rightfully fear giving too many employees the power to implement and administer their own solutions, potentially causing miscommunications or, worse, corrupting data and making processes more opaque.

Coca-Cola will enable some of their employees to come up with their own automation solutions, but with a governance framework around it. Simultaneously, enterprises are placing greater emphasis on holistic process mining and analytics, discovering a lot of cycle timing and efficiency improvements that enable new opportunities for automation.

Adam Bujak, CEO of KYP.ai appreciates bottom up efforts to identify automation opportunities, but only based on data. With the existing tool sets available in the market, the holistic automation approach covering people, process and technology dimensions combined with data insights on where to drive improvements generating a strong business case is no longer a vision. The acceleration of top-down ambition can be well matched with data and bottom-up efforts.

Shobha Singh, Group Head of Operations Planning at Mizuho, shared that they had tried both bottoms up and top driven approach for RPA. They had set up two different teams to test the methods for adoption, leading to a new model for how automation is handled across the enterprise. One team was highly specialised with the relevant know-how for creating automation solutions and the other team was a business-level team. The goal was to see what would happen by freeing them to come up with their own solutions. The results were very clear and in favour for the technical team having the lead role. As a result, Mizuho is not promoting bottom-up adoption as it takes more time to reach a level of sophistication whereby the level of effort results in clear business benefits and provides an optimum user experience.

Top priorities: accessible technology and upskilling

Technology adoption must be both equally accessible across organizations and it must be internally scalable. Contrary to the urban myth in the technology industry, technology adoption is not simple, especially if it aims to effectively solve enterprise level problems.

Although no code / low code solutions are in vogue and are often seen as low cost and easy Drag and Drop tools that are able to create nifty interfaces for internal apps that collect data from employees. In practice, they are typically none of these things as they can run into integration problems with existing systems. Only by looking at analytics from those disparate systems, or by implementing an automation orchestration solution, can no/low-code apps be a keystone piece in the larger automation architecture. Technology adoption requires large-scale, end-to-end strategic planning to avoid future architectural problems. It also requires heightened cybersecurity. Andrei Brasoveanu of top VC firm Accel said, “Areas like security or DevOps also require purpose-built functional automation solutions. As a result we will also see big companies emerge out of these areas.”

From a cultural lens, all strategic technology adoption must include people and their processes. As part of assessing their technology needs and potential solutions, organizations must also address the needs and tech-savviness of their employees and the various systems and individuals involved in their processes. Without a holistic approach to technology adoption grounded in the knowledge of how existing processes work and how employees interface with systems, new technology adoption cannot succeed in any enterprise.

As part of that approach, there must be an upskilling of employee capabilities, particularly to train them on how to get the most out of their new “digital coworkers” and be in charge of their work process respectively. This mindset shift and general C-level acceptance that upskilling workers is a fundamental pillar of digital transformation opens up many new opportunities, both for employees and firms. This is a visible, positive step forward for digital transformation with potential social impact that might mitigate some of society’s biggest fears about automation and AI.

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