According to an IBM and IDC joint study, companies that are first movers in AI-powered automation initiatives are more likely than laggards to track innovation (42% vs 17%), efficiency (35% vs 26%), and customer satisfaction (32% vs 14%), as seen in Figure 1.
Figure 1. Business outcomes from intelligent automation. Source: IBM
However, it is important to note that implementing automation solutions without proper orchestration across the enterprise can cause solutions to work in silos and result in redundant use of automation tools with inconsistent governance, as Forrester suggests.
Business leaders must develop a solid strategy to ensure the long-term success of their automation efforts. In this article, we’ll explore 5 steps to a successful intelligent automation strategy.
1. Start with defining your business strategy
As with other strategic enterprise initiatives, automation initiatives’ success depends on developing a business strategy and aligning with it. Intelligent process automation shouldn’t be implemented for its own sake. Instead, it should deliver positive business results and return on investment (ROI). So, businesses should determine:
- The desired business outcomes of implementing intelligent automation,
- Whether intelligent automation tools are suitable to achieve these outcomes,
- How intelligent automation fits into their overall digital transformation and hyperautomation strategy,
- How to measure the success of the initiative.
Determining these will enable organizations to:
- Review their business strategy and align it with what intelligent automation offers,
- Improve their intelligent automation strategy according to success metrics,
- Avoid costly failures.
2. Establish an automation center of excellence (CoE)
An automation center of excellence (CoE) is a business unit that oversees and coordinates all automation initiatives within your organization. Consisting of automation strategy leaders, analysts, and developers, such a dedicated unit is crucial for enterprise-wide efforts because:
- It develops a unified vision and standardized practices for intelligent automation across the entire organization,
- It closes the gap between technical implementation of the automation technologies and business decision-making,
- It tracks performance and identifies improvement opportunities,
- It helps adopt automation at scale as it coordinates automation efforts across different business departments.
3. Leverage process intelligence
Before implementing intelligent automation to specific business processes, businesses need to ensure that:
- They automate the right processes,
- Selected processes are optimized and ready for automation.
Having a comprehensive understanding of how different business processes function and how they relate to each other is essential to ensure these outcomes. On the other hand, real processes and workflows are often complex and can have unnoticeable discrepancies than assumed processes.
Process intelligence is an ideal technology to understand the as-is state of existing business processes. It involves collecting real-time process data to identify improvement and optimization opportunities and prepare processes for automation, with tools such as:
- Process modeling
- Process discovery
- Process mining
- Task mining
- A digital twin of an organization (DTO)
For instance, by using process mining during robotic process automation (RPA) implementation, businesses can increase the business value by 40%, reduce RPA implementation time by 50%, and decrease the RPA project risk by 60%.
4. Select the right tool
Vendor selection can be challenging and time-consuming because process automation tools come in different types, and some have additional capabilities, even when marketed under the same name. For instance, within the RPA & intelligent automation landscape, some factors to consider when choosing a tool include:
- Cognitive capabilities
- Attended vs. unattended bots
- Low/no code bot creation capability
- Process mining capability
- Self-learning bots
- Integration with other enterprise software
Choosing the wrong vendor can be costly when selecting an intelligent automation tool because almost all platforms are proprietary, and the bots you build with them will remain on that platform, resulting in vendor lock-in. Therefore, businesses must include a vendor selection strategy within their intelligent automation strategy.
We have written comprehensive articles on how to select an RPA & intelligent automation vendor to help businesses in their vendor selection process:
- Top 6 Intelligent Automation Tools & Solutions
- 7 Steps to Select the Best RPA Tool & Vendor
- Technical Buyer’s 11 Point RPA Checklist: In-Depth Guide
- RPA Pricing: Comparison of Leading Vendors’ Fees
5. Ensure employee engagement
Most executives believe that their organization does not have the necessary skills for process automation. Moreover, employee resistance is one of the top challenges when implementing RPA and other automation technologies. Around two-thirds of executives say company culture is a barrier to digital transformation.
These facts demonstrate the importance of employee engagement for the success of intelligent automation and other digital transformation efforts across the enterprise. Artificial intelligence (AI) and RPA have many myths surrounding them, most notably employees’ fear of being replaced by intelligent technologies. Thus, companies should:
- Develop a change management strategy and create a company culture around AI and automation,
- Provide reskilling and upskilling opportunities for employees,
- Ensure that all stakeholders are on-board with the changes,
- Exchange feedback with employees.
- 6 Components of a Successful Enterprise AI Strategy
- Top 25 Use Cases / Examples of Intelligent Automation
If you don’t know where to start, an RPA consultant can help you formulate an intelligent automation strategy. Feel free to check our comprehensive article on RPA consultants. If you have other questions about your intelligent automation journey, we can help:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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