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Buy or Build? Evaluating Options for KM & Related Technologies

23 Jan

typewriter

By Caitlin Peters, Senior Manager of Business Operations, Ropes & Gray and Stephanie Godley, Senior Manager of Knowledge Management & Library Services, Ropes & Gray

Once you have identified a pressing need for a technology solution to a knowledge management, marketing, or practice support issue, often the question arises – should we buy or should we build?

The first step is to outline your core requirements. Once these are identified, take time to research third-party solutions. Then it is time to go through the exercise of considering whether to build your own custom solution. Even if there is a product out there that you can buy or lease, thinking through the pros and cons of building your own solution will help you better understand your needs. Do not forget to look at products your organization already owns. With a little ingenuity, could those be modified, combined, or re-engineered to solve your current problem? Whether you are looking at an experience management system, a knowledge base, a workflow tool or something else, conduct your due diligence before proceeding. Time spent up front evaluating your options will pay off in the long run. Below are some key considerations and questions to ask when determining whether to build an in-house solution or pursue a third-party option.

Bespoke Versus Out-of-the-Box. If there is no vendor tool available, you may have no choice but to build your own solution. Alternatively, if there is a product that does just what you need, don’t reinvent the wheel. Since it is rarely that cut and dry, you will need a highly detailed requirements list and the stamina to put third-party products through a close review. Match your requirements to what the tools on the market offer. Where are the short-comings? How critical is the functionality that the third-party product does not offer? Are there one or two functions you could do without? Take a hard look at how important and unique your needs really are. Would a vendor build them into the product for you?

Best Practices & Consultations. Keep in mind that experienced vendors have often developed products or features based on customer needs and feedback. Vendors can provide recommended best practices, benchmarking, and other guidance based on their work with other clients through implementations and user support mechanisms. Plus, you get the benefit of other customers’ enhancement requests that you may not have even considered. Ask to speak to other customers who have implemented the product.

Control. Is the vendor established and likely to support the product for the foreseeable future? How often does the vendor release updates? Do they allow for customizations? How do they handle enhancement requests? What are their support hours and what is their service level agreement? What are the remedies for outages? Be sure you understand what the relationship with the vendor is going to be and how responsive they are going to be to your needs.

Cost. Adding up all the costs associated with buy or build options is not an easy feat.  Buying means purchase and annual maintenance costs; leasing means annual subscription fees; and don’t forget to calculate any implementation, integration, or customization costs, whether directly with the vendor or through consultants. Watch for contract clauses that raise costs significantly based on an office expansion or increased lawyer headcount. Building in-house eliminates the purchase, maintenance and subscription fees. However, internal teams must often be supplemented by new hires, consultants, and temps. Internally, there is also the very important question of opportunity cost: If your IT team builds your tool, most likely something else gets pushed down their priorities list or your solution ends up at the end of a long list of projects. Where does your need fall into the bigger picture?

End User Documentation & Support. If you pick a third-party solution, make sure to understand what the vendor will provide by way of marketing materials, tutorials, training, and call support for both in-house administrators as well as end users in your organization.  If these are well done, they can save you countless hours of drafting your own materials, providing training, and meeting the need for on-demand support. Putting time and effort into building a system is great, but getting the adoption and usage is essential to have a successful product. What will your support look like during and after launch from either your in-house or vendor team? Can the assistance be maintained?

Internal Bandwidth & Expertise. Legal organizations have relatively small IT departments and an unending pipeline of projects. Is your need a priority? How quickly can your internal team develop a solution? Will there be a project manager to ensure that development stays on pace, on budget, and in scope? Does your team have the necessary skills to build the product?  How agile are they with making fixes and changes?  Will they develop a tool that can easily be maintained, scaled up, and integrated with other core systems?  If the developer leaves, will others be able to step in? Make sure you understand the full extent of the time, expertise, and effort required.

Security. Your IT security team will want to vet any third-party options, whether these are on-prem, cloud or hybrid tools.  Make sure security is reviewed early in the process to address any potential concerns. Don’t let security issues be overlooked and become a road-block at the end of your project.

Speed & Urgency. Are people clamoring for a quick start? If so, third-party tools are often faster to implement unless they require a great deal of customization or integration with your other internal systems.  On the other hand, they may be slower to add features and enhancements.

Build or buy decisions need to take into account a number of factors. Weighing the pros and cons including cost, support, and time can take a fair amount of time. Putting the time and effort in upfront to answer these questions will help you make the best decision for your firm’s needs.

Knowledge Management Round-Up for 2019: What Isn’t KM?

28 Dec

close up of eyeglasses

By Gwyn McAlpine, Director of Knowledge Management Services, Perkins Coie LLP

Merry new year!  Please enjoy this fourth annual catalog of International Legal Technology Association (ILTA) programming of interest to Knowledge Management (KM) professionals.  Because we all get busy, I assume that you might have might have missed some of ILTA’s 2019 peer-powered programming.  Plus, let’s be honest, it can be difficult to know what is out there when the content is spread among podcast and blog platforms, magazines, conferences and recordings.  In this post, I gather all those links together and organize them by topic.

Each year, I make judgment calls on what is relevant to KM professionals.  KM is a varied and evolving discipline that seems to have a hand in a lot of parts of the business.  Thus, I include a broad range of topics, focusing on programming within those topics produced by or targeted to the KM community.  It’s a bit subjective and not a comprehensive catalog of content produced by ILTA in 2019.  I encourage you to do your own exploration.  Know that there is much more content in the ILTA archives, particularly if your area of interest is one adjacent to KM, such as adoption, data analytics, information governance, litigation support or project management.

In developing this list, particularly for four years running, some themes emerge for me.

  • Each year, the topics become broader. This year, we also see many new contributors alongside industry leaders. This growing variety highlights for me the continuing evolution of KM, the expansion of its scope and its integration into more business processes.  As the reach of KM grows, more people join the community as both contributors and consumers.  We are taking over the world, or at least legal tech.
  • We continue to see a lot of interest in big, abstract topics like innovation and artificial intelligence, but like last year, the emphasis is on how to apply these concepts and technologies rather than the theory. In addition, the programming reflects a practical bent with project management, adoption and change management content increasing.
  • I was surprised that there was relatively little programming on more traditional aspects of KM, such as practice resource collections, search and document automation. Last year, we saw renewed focus on these topics with a more strategic approach.  Does the reduced programming reflect a shift in focus or simply that we don’t need training and discussion about it?  Based on conversations throughout the year, I believe that focus is still strong among the community, but perhaps we feel comfortable enough with our approaches that we are using our learning time on thornier topics.

Some links below require ILTA membership to access.  Be sure to log into the ILTA website as a first step. It’s a long list this year, so refill your beverage and grab a comfy chair before digging in.

Adoption & Change Management

Artificial Intelligence

Bots

Competitive Intelligence & Research

 Collaboration

Data Analytics

Search & DMS

Experience Management

ILTACON

Innovation

 Intranets

KM Strategy & General

Leadership & Professional Development

Marketing Collaboration

Matter Profiling

 Other Software

 Project Management

There will undoubtedly be much more programming on these and other topics in 2020.  To receive notifications about future programming, go to your Member Dashboard to select the topics for which you would like to receive virtual event alerts.  Also join the Knowledge Management community (among others), if you have not already, to see announcements of publications and events.  Lastly, add the ILTA KM blog to your blog reader so you don’t miss a post.  And if you have ideas or requests for programming you would like to see, click on the light bulb in the upper right to submit your suggestion.

If you want to review past years’ programming, click on the links for the 2018, 2017 and 2016 round-up posts.  Altogether, these blogs posts represent hundreds of hours (or pages, as the case may be) of programming to scratch that learning itch.  Enjoy!

Buying Intelligence: Navigating the AI Supermarket

28 Oct

person holding compass

By Amy Monaghan, Practice Innovations Manager at Perkins Coie LLP

There are numerous options for artificial intelligence (AI) products in the legal technology marketplace and more options are being added on what seems like a weekly basis (2019 has already seen a $1.2 billion explosion in legal tech investment). With the prevalence of options, what do you need to know in order to be an informed customer? The blog post will help you ask questions to identify the problem you are trying to solve and guide you through considerations when evaluating AI solutions.

Identifying the Need

Defining your business problem is the most critical task when determining whether AI is the right solution and, if so, which products meet your needs. Without a clear understanding of your firm’s or legal department’s needs, you cannot make an informed decision and could wind up wasting time and money. Potential business problems include: streamlining large volume transactions, particularly reviewing and analyzing documents; researching and drafting more efficiently; automating routine legal analysis or documents; or analyzing data, including predictive analytics. Talk to your stakeholders to identify what their specific needs are and prioritize accordingly. Once you have done so, gather formal requirements from the ultimate consumers of the solution (this can be the users and the consumers of the output), separating them into two categories: “must have” and “nice to have.” Create a matrix or checklist of these requirements for use when interviewing vendors and evaluating demos of products. Your nice-to-have’s might be on a vendor’s roadmap so including them in your matrix can help inform your product selection.  Be sure to build flexibility into your requirements for future needs—talk to your stakeholders about their business and strategic plans so you have an idea of what solutions they will need in the future.

If your need is to “try out AI,” I urge you to keep digging into that request to uncover the root issue. Similar to the recommendation to not go grocery shopping without a list, do not go AI shopping without a clear need or strategy for how you will use it. That being said, sometimes you do need to test out a technology to know what’s possible before identifying what’s necessary. In this case, your requirements will be more flexible and your exploration will likely inform your ultimate needs.

Going Shopping

With your requirements in hand, it’s time to evaluate your options. You’ve likely done some research already and have an idea of the vendors you want to speak with.  Given the rapid change in the marketplace, do a second pass to see if any newcomers have viable options. Reach out to your peers to ask about their experiences with different solutions. There is a wide range of potential solutions, from open source options to full-service, ready-to-use products.  Be sure you understand what you are evaluating and the flexibility or complexities that come with that approach. (For a wonderful analogy to brownies, see Gwyn McAlpine’s contribution to this ILTACON panel recording at about the 9:30 mark.)

When reviewing the capabilities of a product, ask vendors to tailor their demo to your specific use case. On a panel at the recent Emerging Legal Technology Forum in Toronto, Al Hounsell from Norton Rose Fulbright shared that, when evaluating no-code platforms, his firm gave each vendor the exact same use case and requirements.  As a result, they were able to hold an objective bake-off. Similarly, I participated in an initiative where we gave machine learning contract analysis vendors the same data set and asked them each to train models using the set. We then evaluated the models’ performance on test data. This approach may not be applicable for all scenarios but can be a useful way to objectively evaluate how well as solution meets your requirements.

In addition to planning use cases to direct the demo, educate yourself about the different flavors of AI and which are applicable to your scenarios.  Unsupervised AI largely deals with classification and clustering, whereas supervised AI is used for more targeted tasks like language extraction. Expert systems can use if-then-else logic along with relevancy or inference prioritization, which is ideal for expertise automation or guided interviews. A few vendors are starting to combine machine learning with other forms of AI, like expert systems, to provide multi-tasking solutions, which can be helpful for more complex use cases.

Be sure to evaluate both technical and non-technical considerations related to products and vendors.  Below are some examples of questions to ask vendors to determine if your requirements are met.

Technical Questions

  • What type of machine learning is used? Supervised? Unsupervised? Both?
  • Does the product come pre-trained with ready to use models? If so, what is the best use of the models? How often are they updated? Who are the trainers? What training data is used? Where sourced? How are the models QA’ed?
  • Do you have the ability to self-train models? If yes, do you understand the process for training models and the data and skills that are needed? Depending on the type of models you need to train, you may need very different sizes and types of data sets. Note that most products that offer pre-trained models or pre-configured templates largely rely on publicly available data, which may not be appropriate for your needs. In this case, you will want a product that allows for customization or self-training.
  • Does the vendor offer best practices or other guidance on model training?
  • Can you collaborate on model training with others outside of your organization? This is a developing request we are seeing in the industry.
  • What technical skills are needed to build applications that use logical reasoning for application building or document automation?
  • Does the vendor have an open API? This is necessary if you will need to leverage multiple solutions to solve problems.
  • Does the product directly integrate with other products? If so, how are product updates handled? Do the vendors coordinate and QA prior to updates?
  • Does the vendor offer a user acceptance testing (UAT) environment or other test environment where you can preview new features prior to release?
  • For cloud solutions, where does the data reside? Do they offer encryption keys? Who holds the keys?
  • What technology resources are needed to assist with implementation?

Company Questions

  • Always ask for the roadmap. This will give you an idea of where the company is headed and if it will be a good partnership.
  • What is their primary revenue source? Is it their product(s) or professional services? Or a split between the two? If the latter, this could indicate complexities in implementation.
  • What is the maturity of the company? Established? Startup? Working with startups might involve greater risk, but the product and company could be a great partnership. For example, I began using Kira Systems (back when they were Diligence Engine!) not too long after they came to market. It’s been a great experience working with them and their product and they have now grown to be a market leader in the machine learning for contract analysis space.
  • Do you feel good about the relationship? This will be a partnership so it will be critical to get along and trust the vendor.
  • Ask the vendors for references and talk to those references about their experience, including support.

Implementation and Support

  • Will the vendor help with implementation and rollout or provide change management guidance specific to their product? Do they have best practices and help resources? If not, preparing your own materials and programs can be very time consuming so plan ahead.
  • Does the provider offer a trial period or proof of concept pricing? This is often the best way to determine whether the product is right for your needs.
  • If subject matter experts/ultimate consumers of the product were involved in the vetting process, ask them to share their impressions and use cases for your rollout materials.
  • Consider asking your early adopters to co-present with you during rollout or provide a testimonial in another form of communication. During our Kira rollout at Perkins Coie, I asked a senior counsel to co-present with me at a firmwide M&A meeting on how he was using the product and provide guidance to his peers. His insight resonated with his colleagues and alleviated their concerns about incorporating AI technologies.
  • Will you and your users have regular opportunities to provide feedback to the vendor? What will the format be? How will the vendor use your feedback?

Conclusion

As you can see, there are many considerations when choosing and implementing AI products. Many of the current offerings come with a steep price tag.  By doing your research and knowing your use cases, you can avoid an impulse purchase at the register!

 

Introducing Robotic Process Automation (RPA) Into Your Organization

15 Oct

high angle photo of robot

By Berys Amor, Director of Technology at Corrs Chambers Westgarth

My first encounter with Robotic Process Automation (RPA) was through an introduction in early 2018 to a Melbourne start-up (Ci-Gen) who were providing intelligent automation services to businesses in the Asia-Pacific region. While the banking and insurance sector had seen rapid interest and uptake of RPA, I didn’t really understand how it could be utilised in a law firm.

At the first meeting I asked the Ci-Gen directors to talk to me about some use cases that they had been involved in. They described how a large hotel chain were using software robots to analyse expenses overnight, looking for any anomalies or unusual expenses. The aim was to capture any incorrect or unusual charges immediately, to avoid any disputes or unhappy guests.

This example made me think about how we analysed timesheet entries on a daily basis – this involved a finance team member running reports from the practice management system each morning, extracting the data to an Excel spreadsheet and then sorting and analysing the data to look for any unusual entries, as well as missing timesheets. Maybe this was something a robot could do? We identified three other processes that we could automate and worked with Ci-Gen to scope a Proof of Value (PoV) initiative. We wanted to demonstrate that software robots are able to accurately access and interact with various nominated applications, accurately capture input data, reformat/repurpose the data where required and pass data between applications as directed.

The objective of the PoV was to ascertain if RPA would be an effective approach to reducing effort in areas where there is sufficient volume and repetitive processes, with an end goal of increasing efficiency, accuracy and timeliness.

The four processes that we identified for the PoV were:

  • Client WIP Reporting – run WIP report using our BI tool and then upload the report to the client portal site for client access.
  • Timesheet Analysis and Reports – look for missing timesheets and send alert to the fee earner.
  • Non-Billable Timesheet Analysis – analyse non-billable time entries to search for narrations such as matter, client, or research to verify non-billable.
  • Partner Profitability Report – run BI profitability report for each individual partner and send via email.

The PoV was performed over a three-week period – if the project failed the investment was small, but if successful the project could scale up and be converted to a production-ready system.

The PoV was a clear success and the automation of these processes demonstrated that the software robots could interact with a range of applications, data types and data sources, and also assist with sorting and identification of mass data sets (150,000+ lines).

Fast forward 12 months and we have a fully functioning Corrs Automation Services team, made up of our Data and Automation Manager, a Process Automation Business Analyst and our two robots, Robbie and Dexter! It was really important to launch the automation services with a dedicated business analyst and this was factored into the business case for RPA. If the task of looking for processes to automate was part of our already busy team of business analysts, then we would not have achieved immediate results, quick wins and the project could have floundered.

Since the implementation of RPA the Corrs Automation Services team have analysed and automated numerous business processes. We created an RPA Evaluation form to determine whether a process is a good fit for RPA. It has questions such as: is the process documented, what systems are involved, what inputs are required and what frequency, and what are the perceived benefits of automating that particular process. It is also important to measure improvements by documenting the before and after results.

The team have worked closely with the finance team to automate many reports and processes, for example we created a workflow template for a robot to run utilisation reports for each partner from our business intelligence application. Each report is bespoke to the partner and is distributed to them automatically with 100% accuracy. This robot template is also a being used to compile and distribute many different reports to various groups

While RPA is typically used for back office processes and functions, we were keen to let our robots work within the practice groups themselves. Our first opportunity came early in the project when we were migrating our conveyancing service from an on-prem SQL database to a cloud-based e-conveyancing platform. We estimated that it would take a paralegal six to eight weeks to re-enter the existing property developments into the new platform – a tedious task with lots of room for error.

The first step was to populate the cloud platform with 8,000 solicitor records. The team then programmed a robot to read an Excel file, validate each data field and then log into and input this data into the new cloud application.

The second step was to migrate over 400 property lot records for the first development, which  involved the robot logging into the old database, extracting all the records and inserting them into a spreadsheet, validating the format and performing a split and merge, before logging into the new platform and entering each record. The migration was completed by the robot in 12 hours with 100% accuracy. This workflow is now being used to migrate other projects.

The RPA project has allowed Corrs to transform roles rather than replace them, giving employees time to invest their talents in more engaging and interesting work. Robots are extremely accurate and consistent – they are much less prone to making mistakes or typos than a human worker and operations can be performed 24/7 as robots can work tirelessly and autonomously without requiring manual trigger. As a result, process cycle times are more efficient and can be completed at a faster speed compared with manual process approaches.

SharePoint as a KM Tool

8 Oct

sharepointBy Holly Hanna, KM Firm Solutions Manager at Perkins Coie

Many law firms and law departments use SharePoint, either on premise or as part of Office 365, to host their intranet. As a hosting platform, SharePoint allows firms to publish content and link to important resources.

However, legal organizations can also use out of the box functionality to quickly build prototype knowledge management solutions, gather feedback from stakeholders, and then publish to targeted practices.

Lists and Libraries

SharePoint lists and document libraries are core SharePoint features. Files are stored in document libraries, and then linked in a list that is displayed on a page and made available to end users. This simple framework provides an ideal platform for prototyping legal knowledge management solutions.

Libraries can be used as a common repository for key practice documents and workflows can be added, so that when documents are submitted, they go through a review process before being given the ‘seal of approval’ and made available to practitioners.

In addition, many practices track resources and best practices in either Excel or Word. Such resources can be easily moved to a SharePoint list, giving attorneys additional functionality and flexibility.

With either a list or a library, you can create a scoped search that returns results against the specified target, making it easy for your end users to find the one piece of information they’re looking for in that specific data repository.

All About the Metadata

With both lists and libraries, the true strength of SharePoint as a knowledge management tool lies in the ability to apply metadata columns and enforce data types and field entries – functionality that doesn’t exist with a Word document or Excel spreadsheet.

For example, if you’d like to create a searchable list of state blue sky laws and associated requirements, you can create data columns based on whether there are exceptions available, whether plan and regulation D filings are required, fee information, etc. You can set some columns to be required, and you can also give users a dropdown of options to select from, ensuring that data remains normalized; users can also attach associated documents for reference purposes. Views of your list can then be created (e.g., show only laws with an employee benefit plan exemption) and those views can be added to a SharePoint page that is available to practitioners.

SharePoint on Steroids: Office 365

To be successful, any knowledge management repository needs to be easy to contribute to, easy to update, and easy to search. On premise SharePoint solutions are both easy to update and easy to search, but adding new content can be challenging for end users. Workflows are difficult to create, and intake forms need to be created and managed using InfoPath 2010 (a deprecated Microsoft product) or a separately licensed product such as K2 or Nintex.

However, with Office 365, we can take advantage of Flow and PowerApps to easily create intake and workflow solutions. Workflow solutions can display different fields to different users depending on their level of permissions, so an approver can see a set of data prompts that a submitter cannot. The form itself is modern and intuitive. And since it’s a cloud solution, it can also be made available from a mobile device if desired.

Microsoft also has a robust artificial intelligence (AI) platform that holds promise for legal organizations looking for new and innovative ways to interact with key data resources.

Conclusion

While many legal organizations use SharePoint as an intranet platform, it is also a valuable knowledge management tool that allows you to quickly spin up prototypes and proofs of concept, validate with your stakeholders, and publish to your practice groups. Practices that are currently using Word or Excel to manage key resources will be better served by moving those solutions into SharePoint. And Office 365 provides new avenues for knowledge management solutions with a focus on the end user experience and ease of use.

Leveraging Artificial Intelligence to Populate Knowledge Management Repositories

16 Sep

abstract blackboard bulb chalk

By Gwyn McAlpine, Knowledge Management Director at Perkins Coie LLP

Among the Knowledge Management (KM) community, one question we commonly hear is this: “How can I use artificial intelligence (AI) to populate KM repositories?”  I have good news and less good news.  There are pockets of success out there, but you need to be realistic about what AI excels at today.

The Problem

KM can involve significant manual effort that entails subject matter expert involvement.  Developing forms banks, practice-specific content, and matter and experience databases requires someone familiar with the area of law to parse through documents and matter information to identify standard practices and relevant data points.  Because our subject matter experts are often timekeepers or otherwise have limited bandwidth, initiatives that require their input can often stall.

Not surprisingly, as technology advances, inquiring minds want to know if AI can replace some of this manual effort.  The nirvana that many want to attain is for technology to successfully analyze piles of documents and data to build gold standard documents and provide insights into our matters and people.  Complete nirvana is still a dream—or at least a lot more work than we imagine it to be.  Nonetheless, creative KM professionals are coming up with ways to use today’s technology to augment the work of the KM team for the benefit of the practices.  Note that this post is not discussing attorneys using AI to supplement their client work, for example, to aid in research and document review (technologies that KM may manage), but rather KM professionals or attorneys using AI to supplement their work in developing KM content.

Today’s Successes

At ILTACON 2019 in August, a fantastic panel presented on “AI-Powered Knowledge Management,” which directly addressed this question.  Click here to listen to the recording.  If you are interested in this topic, it is well worth an hour of your time.  You can also read a recap of this session from the Legal Executive Institute here.

The four panelists presented on efforts at their firms to use AI to further KM projects.  A common thread among their projects is that they are all using AI technologies to extract data and clauses from final documents with software often used for contract analytics, such as ContraxSuite, Kira or iManage RAVN Extract, which then populates a database for easy reference and/or further analysis.  Stated differently, the panelists are applying the technology to unstructured documents and pulling structured data points, such as dates, amounts, and specific clauses, from them into a database.  Use cases were largely around collecting data from transaction documents for two main purposes: (i) to get a big picture of trends and (ii) to be able to find a matter in which a specific set of circumstances occurred.

Interestingly, most of these projects are still in pilot or limited production, validating that these types of use cases are still relatively new.  A KM peer I interviewed for this blog post noted that the relevant technologies have been used by law firms for only a few years, and the early days were likely spent on rolling them out for their core purpose, typically contract review for due diligence.  Firms may only recently be exploring other applications for these technologies.

The panelists were united in their lessons learned.  These projects require investment—from up-front project design and model training, to subject matter expert validation, augmentation, and analysis of results.  Like with many other applications of AI, technology is streamlining the work, but not replacing it.  Someone must dedicate time to and prioritize the effort.  But once you get started and become more familiar with the capabilities of AI, you will think of more use cases that leverage the ability to extract data points.  Extracting data alone is useful, but coupled with metadata tagging and data visualization tools, such as Power BI, it can drive powerful insights—and attorneys get very excited about these insights.

Tomorrow’s Hope

My nirvana state of using AI to build gold standard documents is not quite there yet—at least, not at a level of accessibility to apply broadly.  If you have a very specific use case and the volume to justify the work, you can combine professional services, technology, and a lot of elbow grease to achieve this goal.  But for most use cases, the level of investment required today may be too great for the payoff.

However, if we focus on using AI to supplement and streamline steps in the process, rather than rely on it to achieve the end goal, it is clear that AI can contribute toward document repositories as well as databases of information.  One way to employ AI to create KM document collections is to use contract analytics software to isolate provisions in work product that require redacting, so you can add sanitized precedents to your forms bank.  Another is to extract similar clauses across sets of documents to speed up the work in analyzing standard, alternate, and nonstandard language.  The challenge here is crafting a human-technology partnership that leverages the abilities of each in an efficient manner.

Technology is changing rapidly and may lead to different possibilities within the next few years.  In the meantime, though, AI can be used to further KM projects provided that you set realistic expectations and design your project to take advantage of its current capabilities.

What are the Best Ways to Drive Adoption and Reduce the Risk of Resistance to Change?

25 Jul

orange and and brown chess pieces

By Sharon Ford, Technology Education Specialist at Perkins Coie

There are many interesting changes in the Knowledge Management (KM) area including AI and machine learning, chatbots, automation and more.  Many of these initiatives involve technology changes, but they also result in process changes including how people work, and they may evoke emotions including concerns about job security.  Regardless of the type of change, one thing you need to keep in mind is organizations don’t change; people do. If individuals need to change, then there needs to be a focus on people when initiatives are implemented and that is where change management comes in.

Whether a change being implemented is defined as a KM change or any other change, I encourage you to evaluate every project you are implementing with a change management lens.  How can you do that? While this is not an official change management model, I’m going to leverage something you probably learned back in grade school – the Five Ws. The Five Ws (sometimes referred to as Five Ws and How) are questions to be answered for basic information gathering or problem solving.  They are often mentioned in journalism and research, and they comprise a formula for getting the complete story on a topic.

The 5W example can be applied to provide a change management lens for your projects including:

1) What’s the Why?

2) Who’s your Sponsor?

3) Who is impacted?  And, what is the impact?

4) How will you engage stakeholders? And, where can they find more information?

5) When and how will you measure adoption?

Below is a brief overview of each.

What’s the Why?

The Why or the Vision should identify how this change makes your organization competitive and should explain the WIIFM (what’s in it for me) for different stakeholder groups.  It should include why the change is happening and the risk of not changing. Whenever possible, tell the why in a story to allow people to connect to the vision not only with their heads but also with their hearts.

Who’s Your Sponsor?

Prosci has conducted multiple studies on successful change efforts and found the most important factor for success is sponsorship.  A Sponsor should remain active and visible throughout the life of the project, not just identify the need for change then announce it and walk away.  Often, the Sponsor comes from the organization that is causing a change, but a project will be more successful if leaders from the impacted areas are engaged and show visible support for the effort.  Part of the change management activities should include providing a roadmap for the Sponsor(s), so they know the activities and the commitment required.

Who is Impacted?  And, What is the Impact?

People typically identify stakeholders by role, but you should also consider other aspects, for example, their typical workflow, their current frustrations, and their usage of a mobile device.  These and other factors influence the level of impact. Personas, which are often used for marketing purposes, can also be created as part of stakeholder identification for a project to capture the different needs and expectations of various stakeholder groups.

How Will You Engage Your Stakeholders?

A quick and easy answer is to engage them early and often.  You may have heard of the “Rule of 7,” a marketing principle that states prospects need to come across an offer at least seven times before they really notice it and start to act.  That is a good rule for stakeholder engagement as well. Also, ensure the communications for a project incorporate the listening side (e.g., meetings, surveys, an email address or a project page where people can provide input, ask questions and get more information).  Remember the perception of a change can differ by stakeholder group, even by individual, so to help reduce resistance, you need to listen to concerns and feedback. You should also consider creating a Change Agent Network for your project. Change Agents are early adopters who receive training and advance details on the initiative, so they can proactively engage others, assist with adoption and help to reduce resistance.

A term that is gaining some traction is change engagement instead of change management.  Wouldn’t you rather be engaged than managed?

When and How Will You Measure Adoption?

Remember the quote from Peter Drucker, “If you can’t measure it, you can’t improve it.”  Understanding adoption behavior is helpful in identifying whether a change is providing value.  Identifying adoption metrics has its own subset of the Five W’s. You need to think through what to measure, when to measure, how to measure and who should measure.  When you are identifying and tracking metrics, think of adoption as a phase reported over time rather than a single event.

Keep in mind that people know how to do their jobs the current way.  Even if the current way is cumbersome and inefficient, it’s comfortable.  Thinking through the Five W’s can help you focus your change management lens and guide people through their concerns and resistance.

It is also important to note that people often fear change because they fear failure or criticism.  Building a culture where people are willing to risk failure in order to change and grow is also a major factor in successfully implementing change – perhaps a topic for future blog.