Introduction
Robotic Process Automation (RPA), Lowcode (LC), Natural Language Processing (NLP) and Artificial intelligence (AI) are all transformative technologies that can create great value for your company. Yet, according to McKinsey and Ernst & Young up to 50% of all Automation and AI implementation fail for various reasons. A recent qualitative study from Turbotic shows that midsize and large companies basically face the same challenges. One common challenge they have is end-to-end visibility of their implementation, and the lack of tracking value of such implementation.
One reason for this is the lack of orchestration of the full workflow, from Discovery to Value tracking and that much of the work is still done in silos between departments and in various documents such as Excel and PowerPoint. This is also the main reason why some pockets of Automation and AI implementation can be successful, but scaling is almost impossible. Automation is very much dependent on a team’s collective knowledge and experience but with no clear workflow or efficient way to track success it becomes more and more difficult to take your Enterprise Automation and AI to the next level.
According to a study made by Bain & Co, (Beyond Cost Savings: Reinventing Business through Automation, March 2021), companies plan to adopt a broader range of technologies to achieve more value from automation. As they do so it becomes increasingly more complex, not only to discover the potential but also to build, deploy and track value of such implementations.
Discovery
While many companies start their RPA journey building a robot to carry out a task or develop an algorithm to make predictions a successful Automation and AI project starts with a good Discovery. Discovering and mapping every process and variant across your organization analyzing and recommending the best processes for automation, as well as generating a good business case is the first step. Some companies use consultants to do the Discovery, others a combination of experts and process mapping tools. To shorten the path to successful transformation, organizations need to have a clear process discovery methodology to identify opportunities, analyze productivity gaps, and build a solid business case with attached KPIs. In many organizations, the level of knowledge around processes and technologies is low. Implementing RPA, AI and other technologies is not like implementing a new ERP system. A lot of this work must be done manually and for the first time, hence the high risk of failure. Working with high-performing employees or experts in this area, can result in significant higher success rate, however, it is still very much dependent on individuals working in various documents and homebrew project management tools. Turbotic’s experience is that it is not uncommon that companies use PowerPoint to describe the project, use Jira to manage building the solution, use one or several Automation and AI vendor platforms to manage the technologies and try to import data ad hoc into Tableau to try to track the value of the implementations.
Most of the Hyperautomation technologies have certain characteristics that make them suitable to implement. For example, characteristics for RPA is:
- High manual and repetitive tasks
- Rule based
- Mature and stable
- Not subject to methodology/process changes
- Low expectation handling required
- Contains readable inputs
- High volume transactions
"Very few individuals in a typical organization can master all, and understand the full potential of these technologies, hence the need for a digitalized ways of working and system support."
Same goes for each of the transformative technologies and as organizations start to crossbreed and merge more than one technology the characteristics change and becomes very complex to understand. Very few individuals in a typical organization can master all, and understand the full potential of these technologies, hence the need for a digitalized ways of working and system support. With a digitalized workflow where organizations can collect data on their projects end-to-end comes a visibility and measurability that is of essence to be successful. When your “transformation engine” analyzes this information, identifies specific tasks, and automatically starts building your business case, optimize your deployments, predicts your operations and uncover your success, then you are ready to scale, and then your success rate will be higher.
As you digitalize your workflow to implement Enterprise Automation and AI technologies it also reduces the number of human resources involved. By giving your employees tools that increase their efficiency, you can decrease the number of work hours – and, thus, the financial cost – involved in identifying potential, evaluating them, build, and operate. This is especially important once you start working. Not to forget, it gives your organization a standard that is not dependent on the knowledge from a specific individual or consultant. It will give you full control over your Automation and AI journey.
Once your organization begin using RPA, Lowcode, OCR, NLP, AI or other transformative technologies and see the value it offers, you will most likely want to expand your use of it and scale. There’s a good chance that you’ll want to increase both the number of robots, AI models and solutions, making up your virtual workforce and the number of processes these perform throughout your organization. While this could give a powerful improvement to your overall efficiency, it also can entail highly manual tasks just to carry out the Hyperautomation itself, which could consume a significant amount of time and resources.
According to Gartner (Move Beyond RPA to Deliver Hyperautomation, Dec, 2019), to accelerate business transformation, enterprise architecture and technology innovation leaders should:
- Plan a long-term strategic roadmap by aligning business goals, identifying processes to optimize and choosing complementary technologies.
- Build an integration strategy that enables end-to-end process automation by helping your organization to assemble RPA, business process management (BPM) and other tools from Gartner’s DigitalOps toolbox.
- Augment business processes by progressively integrating AI applications with DigitalOps tools to unlock long-term business value.
Even though this is very much true 2021, our own studies and experience in this field would recommend two additions to the list:
- Digitalize your workflow to collect data from all your projects end-to-end in order to identify the bottlenecks and visualize the success of your Hyperautomation.
- Take more control of your Enterprise Automation and AI transformation. It may not be a core function today (in best case it is a well-established CoE), but as the digital workforce grow with new technologies and your company becomes more “self-driving” you must start looking at this as an integrated part of the business.
"A recent study by Turbotic show that 35% of organizations that have started to use RPA work with more than at least one additional technology for their Hyperautomation program."
HFS Research wrote 2020 (RPA is dead. Long live Integrated Automation Platforms) hailing “RPA is dead. Long live integrated automation platforms” and championing the need for technology, people and process to be integrated. The research firm writes: “Forget about leveraging RPA to curate end-to-end processes, most RPA adopters are still tinkering with small-scale projects and piecemeal tasks that comprise elements of broken processes. Most firms are not even close to finding any sort of enterprise-scale automation adoption.” In later research HFS found respondents believe that the combined use of emerging technologies is more beneficial than using any of the technologies in isolation. A recent study by Turbotic show that 35% of organizations that have started to use RPA work with more than at least one additional technology for their Hyperautomation program. Looking at the next horizon in Enterprise AI and Automation it will become more and more complex to manage a landscape of vendors, projects and deployed solutions, hence the need for a more advanced process management and orchestration system.
Even though the success of Hyperautomation is generally measured in how many hours an organization can save, or direct cost out, investments in Enterprise AI and Automation is first and foremost a long term strategy to build a new operating model. This new operating model is supported by a robotized and AI-field workforce to augment the work of humans and to free up time to work on growth oriented tasks. However, to do this IT-leaders must look beyond cost savings, and more importantly they must look through another lens. Hyperautomation is not first and foremost technology, it is business transformation that demand a systematic and thorough approach end-to-end. The more your organization can digitalize such process the more you will be able to scale. A surprisingly large number of companies that Turbotic have looked at still manage their Hyperautomation program in a very un-automated and manual way.
Findings
- To scale your Hyperautomation initiatives organizations will have to investigate more than RPA
- To be successful organizations must take a full workflow, and end-to-end stance for their Hyperautomation program. From Discovery to Value enablement
- Organizations must digitalize such workflow to manage a complex landscape of vendors, projects and deployed solutions
- Even smaller organizations that just started need to ensure that they do the right things from start or fail scaling at a later stage
About TurboticOS
TurboticOS is the world’s first AI supported Operating system to handle the full workflow of Enterprise Automation and AI. With its four modules; Discover, Build, Control and Value, users can uncover the potential, manage a build project, operate thousands of Hyperautomation solution in RPA, AI, OCR NLP and Lowcode, and measure the value in real time. TurboticOS simplify Hyperautomation and increase your success rate.
