Advances in data capture and document recognition technologies are leading more and more organizations to integrate them into their content management solutions.
Embedded with advanced artificial intelligence (AI) capabilities, these systems scan all incoming communications, import documents, recognize and classify data, images or videos. But it doesn't end there. After the content type is identified, classified and data captured in a digital format, this information is routed within the business environment.
While all this is happening, the AI engine enriches the meta-data by reading the content and making decisions as if they were done by a human.
Enterprise capture management empowers all employees to access the right content at the right time, by streamlining the lifecycle of their information. This means that from data capture to distribution and archiving, your organization is able to virtually eliminate paper-based information and enhance content visibility. Not forgetting to combine the necessary content compliance and information security functionalities.
Surprisingly, the lack of effective adoption is the single largest factor impacting the ROI of implementing enterprise capture technologies. From a technical perspective, most requirements are feasible. Robotics, automation tooling and content intelligence are becoming more and more accessible, requiring less and less in-depth expertise on each technology. However, reluctant users who avoid bringing the new technology into their day-to-day tasks means you’re not making the most of your technology investment.
User adoption is oddly related to resistance to change.
Although the premise of user adoption is changing from an outdated or redundant system to a newer, more efficient one, getting everyone on board isn't as easy as you would expect.
Here are a few tips I've picked up over the years that can help you get through these challenges.
1 - Document your content management processes
Take implementing content management process automation powered by artificial intelligence as an example. One of the biggest challenges is getting users who know every step of the (manual) process to put it in words. Generally this isn't documented and it’s the key to identify what steps can be automated and where AI can help make their job easier.
To prevent this, make sure your organization is documenting processes by using models and diagrams but keep them simple and understandable so that those with no knowledge of business process management (BPM) ) can understand the workflows and suggest improvements.
2 - Follow the KISS principle
Implementing content capture technologies can seem daunting for organizations with large amounts of critical information based on paper and no in-house technical background or know-how.
It also takes a lot of effort, starting from the teams involved in gathering requirements, to the consultants and AI engineers implementing the solution. All AI is data-driven, so configuring these types of knowledge management solutions is labor intensive due to the sheer amount of data samples it demands.
By promoting an agile iterative implementation approach we start with a very small reference set, work on that, and then move to production as soon as possible. Receiving input from all the teams involved in the project and keeping them up-to-date on progress and milestones is key. Users need to feel encouraged from the first iteration and throughout the development and implementation stages – keeping it simple and straightforward helps.
3 - Make sure your teams are involved
Having worked as an ECM Consultant for organizations of all sizes for nearly ten years, I can say that most content capture solution implementations are technology driven. There’s usually no clear change management strategy to ensure smooth adoption on the end-user level.
Sure, the company probably has a business case and a business sponsor, but when it comes to communicating to users what’s changing and how and getting them excited about the project, it’s normally a reactive event. Very often we see user adoption initiatives being limited to assigning a team member to handle User Acceptance Testing (UAT), after which the system is implemented, and an educational video is expected to do the job.
End-users of any technology you’re looking to implement should be involved from the early stages of the project.
4 - Use a timely, frequent and transparent communication
What is the purpose of process automation and expected improvements in day-to-day work? Have specific timelines been set or will the implementation be completed in phases?
Sometimes the business case is just not clear. And while you don’t need to share every detail about the project, communicating key information with your teams early and often will keep them in the loop and allow them to know what to expect.
This happened on a project I worked on. After requirements intake and building the content capture solution, we moved to UAT. An end-user started working with the application and all its functionalities: automated data extraction and classification. Basically text boxes and fields are automatically populated so less manual input is needed from the user. Suddenly, she said: “If this is automated, what am I going to do all day?” She had no clue what was going on because the organization didn’t focus on change management.
"Success consists of going from failure to failure without loss of enthusiasm."
- Winston Churchill
Focus on your strategy´s goal, make it clear and refer to it often. Content capture, artificial intelligence, natural language processing and machine learning are all intimidating subjects in the workplace with a lot of associated misconceptions. Remember that the human factor drives the success of any technology deployment - after all, success is measured by the extent to which people embrace and incorporate new practices and behaviors into their daily work routines, how they adopt and adapt the technology for their own purpose.