Software & Technology

Lucy® is unlike any other knowledge management system on the market. Built for the Fortune 1000, she reads, listens, watches and learns all of the data that you share with her—a one stop AI-powered knowledge platform for all the data you own and license. 
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Lucy doesn’t require you to move your data, instead she operates with your data where it lives without disrupting internal systems and processes. Through automated integrations and tagging, Lucy removes the time consuming, manual processes of uploading, tagging and curation and effortlessly ensures new and/or updated information is continually added into the system. She brings you to the exact spot in your data asset where the answer lives versus leading you to a long list of possible results to read through.
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She was shaped by the needs of our clients and she continues to evolve with the market. Lucy exists to amaze, delight, and empower knowledge workers.

Lucy

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Meet Lucy -The AI companion to knowledge workers. She reads, listens, watches, and learns all of the data that you share with her. 


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<p>To say ChatGPT has blown up in the last 8 months would be an understatement. It has not only sped up the pace of digital transformation; it has revolutionized search. A space that was previously dominated by a long list of hyperlinks—now has the capability to interact with results and beyond.</p>
<p>While Generative AI has captured the attention of the masses, questions emerge on how this technology can be infused into the apps employees use at work.  And what are the ramifications and policies related to working with Generative AI?</p>
<p>As we contemplate this, we need to understand that there are two major avenues for Generative AI. One is Public Generative AI which works with the mass of public data—think Bing, Bard, and <a href="https://www.greenbook.org/insights/tags/chatgpt">ChatGPT</a>. The second, Private Generative AI is a very similar technology that can be deployed inside of a company&#8217;s current applications and works with the data your company owns or licenses.</p>
<p>The policies, benefits, and use cases are very different between these public and private applications.</p>
<h2>Public ChatGPT:</h2>
<p>Open AI&#8217;s ChatGPT is trained on vast amounts of publicly available text from the internet. They have been fine-tuned to generate creative responses, provide information, and engage in open-ended conversations. Public ChatGPT models excel at a wide range of tasks, from answering questions to providing recommendations and even generating human-like content.</p>

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            <h4><a href="https://www.greenbook.org/insights/the-prompt/how-to-employ-generative-ai-deliberately-for-better-b2b-research-results">How to Employ Generative AI Deliberately, for Better B2B Research Results</a></h4>
        </div>
    
<p>What has captivated the public’s attention is the actual interactive experience they have with it. Instead of simply typing a keyword or asking a question users can now chat back and forth or give commands to complete a human-like task. For example, a query could look like this: “Please write a 2000-word essay on the origins of the Civil War” and then add in “Can you write this for a 5th grader?&#8221;. I have had my fair share of conversations with AI since the launch of ChatGPT, and this is something that until late last year—was purely in the realm of science fiction.</p>
<p>Despite being a publicly accessible tool, there exists a valid justification for enterprise employees to utilize the capabilities of Open AI’s ChatGPT. Just like the need for employees to access <a href="https://www.greenbook.org/insights/tags/google">Google</a>, leveraging ChatGPT would extend their scope of utility in a manner that surpasses traditional search engines. In order to provide employees with the chance to leverage the capabilities of public ChatGPT, companies need to implement policies and standardized procedures that safeguard the interests of both the organization and the users involved. This is crucial due to the uncertainty surrounding the accuracy and hallucination of data, as well as the ownership of copyrights.</p>
<h2>Generative AI in the Enterprise (Private):</h2>
<p>Businesses are eager to reap the advantages of this incredible technology. They have already embraced its use as consumers, so why not enable users to harness its potential in a business context?</p>
<p>By employing Private Generative AI, businesses can efficiently utilize this technology within their day-to-day business applications. This approach provides a higher level of control concerning contextual understanding and data privacy. It offers users the opportunity to enhance their search capabilities exclusively within their organizational data, thereby empowering them to derive valuable insights while maintaining the confidentiality and security of their information.</p>
<p>For instance, consider a market researcher seeking to inquire, &#8220;What percentage of our sales in 2020 comprised Generation Z?&#8221; With Private Generative AI, the researcher would receive answers solely based on data available within the company, ensuring confidentiality and limiting the scope to internal information.</p>
<p>Private Generative AI provides three essential attributes: accuracy, safeguarding copyright for the generated content, and the exclusion of data sharing or training of extensive language models. This ensures that the generated output is highly accurate, addresses concerns related to copyright ownership, and upholds data privacy by refraining from sharing or training the models with external data sources. These attributes collectively contribute to a more controlled and secure AI environment for businesses.</p>
<h2>Conclusion:</h2>
<p>As companies formulate AI policies to govern the utilization of this transformative technology, it becomes crucial to acknowledge the inherent disparities between public and private sources and applications. Embracing a one-size-fits-all to generative AI is not the answer. Instead, a nuanced approach that recognizes the distinct characteristics and challenges of public and private implementations of these tools is essential. By adopting tailored policies that align with the specific needs of their organization, businesses can navigate the intricacies of AI deployment, fostering responsible and effective utilization while safeguarding privacy and maximizing the benefits derived from both public and private AI sources.</p>
<p>&nbsp;</p>


ChatGPT: Public vs. Private—where do we go from here?

<p>We, as humans, are making decisions every single day. While some are bigger than others, at each intersection we call upon all the information we have in order to make these decisions. What happens when we can’t find information to inform our decisions? Specifically when it comes to decision making at work, where we are often left with only partial information to inform our strategy.</p>
<p>The <a href="https://info.talend.com/rs/347-IAT-677/images/DH_Survey_report.pdf">2021 Talend survey</a> states that 36 percent of executives rely on intuition to make the majority of their decisions. If top level executives go with their gut to make decisions, how do less experienced employees make informed decisions?</p>
<h2>When data is spread out across the organization, no one can find it to make an informed decision</h2>
<p>Organizations have vast repositories of data. For most organizations, this includes millions of pages of PowerPoint presentations, PDFs, word documents, Podcasts and Video content, plus so much more. Much of which holds a wealth of carefully curated insights and research about the business. And with each passing day, more and more information is being created.</p>
<p>With this influx of data spread across departments, repositories and geographies, decisions are too often left to be made with only what employees can readily find due to the speed and demands of the business. Usually, the quest for knowledge stops when the first answer is found, which most often is not the best—merely an answer to in order to move on.</p>

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            <h4><a href="https://www.greenbook.org/insights/market-leaders-deep-dive-data-038-analytics">Market Leaders Deep Dive: Data & Analytics</a></h4>
        </div>
    
<p>Take for example an employee who is trying to access information to simply check off their next to-do. According to a recent <a href="https://www.sellingsimplifiedinsights.asia/asset/Miscellaneous-Content/Workplace-Knowledge-and-Productivity-Report.pdf">Workplace Productivity and Knowledge Management Survey</a> 57% of employees state that they experience difficulty finding the right information decreasing their productivity and 49% spend between 30 minutes to two hours trying to track down information they need to do their jobs.</p>
<p>This struggle to locate knowledge deters teams from making data-driven decisions. It also decreases productivity and increases frustration across the organization.</p>
<h2>Why investing now can mean so much more later</h2>
<p>How companies use their data assets in times of uncertainty is their biggest competitive advantage. Relying on the data you own and license allows the content to become the single source of truth, empowering everyone to make decisions faster and more confidently. No matter what may arise, these companies can investigate the historical data to anticipate what may occur in the near future, understand successes and failures, and potentially what changes should be implemented to better perform and compete.</p>
<p>According to StrategyR, the global <a href="https://www.greenbook.org/insights/insights/the-importance-of-automation-for-knowledge-management">knowledge management</a> market is estimated at $318.5 billion and is to reach $1.4 trillion by 2027. Knowledge management systems such as Lucy provide a centralized knowledge hub, employees can simply ask questions and receive a precise answer back, as they need it and no matter where it resides,</p>
<p>Consider what could happen if organizational-wide teams were able to make every decision a data-driven one. This could drive new business opportunities, improve products, enhance customer-centricity, and improve employee experience.  Investing in a knowledge management platform now will help to inform decisions and strategies that will impact the future and be leveraged by generations to come, opposed to a company that continues making decisions based on intuition.</p>


Insights or Intuition? How Knowledge Management Improves Data Driven Decision Making

<p class="p1">Next-generation <a href="https://www.greenbook.org/insights/insights/from-insights-to-action-walk-before-you-run-with-ai" target="_blank" rel="noopener">AI-powered insights</a> platforms are cutting-edge technology. As such, many businesses find themselves uncertain about how to choose the right one for their organization. Where are the current inefficiencies in finding information? What even <em>are</em> the pitfalls to watch out for? How do you even know if a platform is lacking a capability your organization would benefit from?</p>
<p class="p1">Fortunately, these four core questions can help clear things up considerably.</p>
<ol class="ol1">
<li class="li1">Does this ai platform address your institution’s pressing pain points?</li>
<li class="li1">How easy is this ai driver platform to deploy?</li>
<li class="li1">What does the user adoption process look like?</li>
<li class="li1">How will data scientists manage and kept their data current?</li>
</ol>
<p class="p1">The following sections will highlight what a good answer to these questions should look like, as well as some concerns to watch out for.</p>
<h2 class="p1">1. Does the ai insights platform address your institution’s pressing pain points?</h2>
<p class="p1">Identifying the pressing pain points that an insights platform can alleviate is a process. That process starts before deployment and should involve comprehensive help and guidance from the team behind the platform you’ve chosen.</p>
<p class="p1">Different businesses, and even different insights departments within the same business, will have different pain points. Here are just a couple of examples we’ve run into at Lucy:</p>
<ul class="ul1">
<li class="li1">A Global Packaged Goods company found that the leading form of information gathering is over social channels like Teams and that 1000s of people rely on a very few SMEs to answer their questions. This made a huge impact on the SMEs &#8211; they spend so much time answering questions they can’t do the strategic work that was most valuable to the business.</li>
<li class="li1">One office in North America conducted an extensive, multi-million dollar market research project. Only after the research project was nearly complete did they identify a nearly identical research project that was executed by their European branch the previous year. Ultimately, they discovered they were spending hundreds of thousands and sometimes even millions on redundant research,</li>
</ul>
<p class="p1">Both of these examples required distinct functionalities to be solved effectively. Look for an insights platform with a team that is responsive, collaborative, and capable of creating a solution that’s fully optimized for your needs.</p>
<h2 class="p1">2. How easy is the platform to deploy?</h2>
<p class="p1">Deployment shouldn’t be the hard part of adopting an insights platform. If it’s going to be an arduous process just to get the thing in place and operational, that’s ultimately low-value hours spent chasing a high-value dream.</p>

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            <h4><a href="https://www.greenbook.org/insights/market-research-predictions-for-2022-part-1">Market Research Predictions for 2022 (Part One)</a></h4>
        </div>
    
<p class="p1">Ask the team behind the platform roughly how long deployment could take. While, of course, an estimate is <em>only</em> an estimate, it can provide you with a general idea of what’s required. Some platforms need months or even years to be fully optimized, thanks to the complexity of their data ingestion processes.</p>
<p class="p1">That leads us to another consideration regarding ease of deployment: How much work will you and your staff have to undertake? If you’re looking for a solution that can provide accurate, comprehensive answers across your complete range of files and systems, what&#8217;s the process to get all of that into the platform? And can it interact with the documents and data you currently employ?</p>
<p class="p1">The data integrations process is often described as &#8220;garbage in, garbage out&#8221;. However, it doesn’t have to be that way. You can instead look for a platform that can index all documents, regardless of type or quality, and learn to spot what’s important. Such a platform eliminates the data grooming task and the human burden of maintaining the quality of your answers.</p>
<h2 class="p1">3. What does the user adoption process look like?</h2>
<p class="p1">As anyone who has ever tried to implement a process change knows, user adoption is where the real work begins. Trying to shift ingrained behaviors in employees is a challenge. As bad as the status quo of searching is, it has been that way for so long that most simply aren’t aware of how much easier things can be. So your insights platform should be ready to help tackle this challenge head on.</p>
<p class="p1">First, the team behind the platform should be able to demonstrate to you that they have experience in successfully driving user adoption. It should be expected that your vendor will be actively engaged in the process, hosting trainings, providing resources, answering questions and troubleshooting problems if and when they come up.</p>
<p class="p1">Second, using the platform should be intuitive and rewarding. Look for features like:</p>
<ul class="ul1">
<li class="li1"><span style="text-decoration: underline;"><b>Questions can be asked in natural language.</b></span> Instead of restricting users with the limitations of keywords, natural language processing allows them to ask their questions just as if they’re having a conversation with a colleague.</li>
<li class="li1"><span style="text-decoration: underline;"><b>The platform can be accessed from anywhere, anytime.</b></span> Cloud-based insights platforms allow users across the world to ask questions and get their answers in real-time. This has become particularly essential over the past few years with the rapid increase in hybrid work.</li>
<li class="li1"><span style="text-decoration: underline;"><b>Answers instead of search results.</b></span> Instead of receiving an endless list of documents that you are then compelled to painstakingly comb through, your insights platform should be able to bring you to exactly where the answer lives. And regardless of if it&#8217;s specific page in a document, a timestamp in a video or a line in a spreadsheet, the best answer should be ranked at the top.</li>
<li class="li1"><span style="text-decoration: underline;"><b>Works across all your systems.</b></span> In order to truly get the best answer, your insights platform should be able to integrate with just about every database, every dashboard, and every filetype your company uses.</li>
<li class="li1"><span style="text-decoration: underline;"><b>The platform is easy to use.</b></span> The more complex the user requirements are, the less inclined users tend to be to engage. Your insights platform should replace all the interruptions and low-level questions that would normally be asked of your subject matter experts. That means it <em>must</em> be as easy as it is to send an email or post a message.</li>
</ul>
<p class="p1">When your insights platform has these features, most of the major barriers to user adoption are eliminated upfront. That means faster uptake. And the faster the uptake, the sooner that high-value functions can be prioritized.</p>
<h2 class="p1">4. How will the data be managed and kept current?</h2>
<p class="p1">Finally, the data itself. As we’ve already mentioned, the data ingestion process can be incredibly costly if it’s not managed well.</p>
<p class="p1">Some insights platforms require you to curate and upload every document you want to include in the platform, making a file-by-file decision about what’s valuable for the platform and what’s not. That process continues even after the platform has been deployed. Decision-makers need to be trained in which future documents should be included and which should be ignored. It’s ultimately time-consuming, labor intensive, and often results in mistakes that can influence the quality of the results the platform returns.</p>
<p class="p1">To avoid these problems, look for a platform that, with machine learning, can automate all forms of data indexing that can read, watch, listen, and study every document to understand its value and rank it accordingly.</p>
<p class="p1">You’ll also be best served by a platform that can integrate into other systems, rather than requiring that everything be shifted onto it. For example, many organizations already have dozens or hundreds of SharePoint sites. Choosing a platform that goes to those sites and scans their documents in place saves time and ensures the platform stays up-to-date as new information is added.</p>
<h2 class="p1">How time becomes more valuable</h2>
<p class="p1">When low-value tasks are streamlined and automated, insights professionals are freed up to focus on the high value. Tasks that solve problems, build relationships, develop products and improve profitability. Think about how many minutes per day each of your team spends looking for answers. Think about how often your subject matter experts get pulled off their strategic priorities to answer redundant questions, simply because people can&#8217;t find the answers. What does that add up to across your entire company, over months and years?</p>
<p class="p1">Insights platforms are a game-changer when it comes to eliminating low-value hours. But for your business to see the greatest reward, the platform you choose needs to address your specific needs without introducing new challenges.</p>
<p class="p1">The best insights platforms will be:</p>
<ul class="ul1">
<li class="li1">Easily managed</li>
<li class="li1">Easily accessible</li>
<li class="li1">Intuitive for users</li>
<li class="li1">Well supported by the business users behind the platform</li>
<li class="li1">Easily integrated with your existing systems</li>
</ul>
<p class="p1">And on the flip-side, they will <em>not</em>:</p>
<ul class="ul1">
<li class="li1">Be burdensome to deploy</li>
<li class="li1">Require labor-intensive data grooming</li>
</ul>
<p class="p1">With an insights platform in place that meets these criteria, the value is tangible. Just add up all the time you’ll no longer be spending on low-value functions. That all becomes truly profitable time, time that can be used to make things happen and return results.</p>


Choosing Between AI Insights Platforms

<p>In the typical Fortune 500 company, research and insights teams have “access” to millions of pages of content. Think about the years and years of research that accumulates: Volumes of content, survey data, and thousands of charts, videos, and documents, all created over time.</p>
<p>Yet, how accessible is all of that valuable knowledge? Typically, 95% of accumulated data goes unused, and even though companies spend millions of dollars on this data a year, it&#8217;s often forgotten on the proverbial shelf, and those millions of dollars spent were wasted.</p>
<p>Why does this happen? Answers range from “I don&#8217;t spend any time hunting through old data because it&#8217;s not worth it” to “It’s a waste of my time, either I won&#8217;t find anything or it&#8217;s going to take me forever to find the data I need.” There are some that actually spend the time searching for the data, but it takes them days or even weeks &#8211; more time wasted.</p>
<p>In working with brands across the globe, we hear six common issues that cost businesses productivity and money due to the difficulty of managing enterprise structured and unstructured knowledge:</p>
<h2>Turnover</h2>
<p>People come and go. They get promoted, move from one brand to another, and change geographies and companies. The thing is, in any of those scenarios when they move, the data that stays behind becomes an artifact. Everything they worked on, every project, every report that they know about, is effectively unavailable to the organization. It&#8217;s there, somewhere, in a file. Yet, if nobody knows about it, they&#8217;re not going to find it or use it. It&#8217;s effectively dark or organizationally lost.</p>

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            <h3>Related</h3>
            <h4><a href="https://www.greenbook.org/insights/insights-industry-news/how-ai-is-completely-transforming-knowledge-management-for-businesses">How AI is Completely Transforming Knowledge Management for Businesses</a></h4>
        </div>
    
<p>On the flip side, if you are starting a new job or are the one that got promoted, you know there was a predecessor before you. But how do you know what they worked on?</p>
<p>Hence, the movement of people, which is really great for individual careers (as well as organizations) is terrible for the reuse of data because tribal knowledge is constantly lost.</p>
<h2>Redundant Research</h2>
<p>The next challenge is around redundant research. No one consciously thinks, “I can&#8217;t wait to redo this study again” or, “I know this assignment I am on for the next month is one that we must have done in the past.” But it happens every day at large brands.</p>
<p>Leaders assign work without knowing that other people in the company may have done similar work. Individuals receive assignments not knowing that the work was done by a peer or predecessor. There is no simple way of knowing otherwise.</p>
<p>And it&#8217;s not just what internal teams have worked on. It&#8217;s all of the research commissioned. The strategy received from agency partners. The syndicated data licensed from great research entities. There&#8217;s just no way to get your arms around it. The redundancy it creates has all kinds of inefficiencies and results in millions of wasted dollars.</p>
<h2>Organizational Disruption</h2>
<p>Another common scenario is organizational disruption. Mergers, acquisitions, and internal corporate restructuring are all common. With people and data moving around, knowledge sheds. This equates to volumes of really valuable data permanently lost.</p>
<h2>Survey Data</h2>
<p>Another challenging component is survey data. The market research industry spends hundreds of thousands of dollars a year on survey research. Yet, most organizations do not have a way to search through those valuable data files. For example, if you&#8217;re getting back a banner book from the supplier that is not searchable, plus it&#8217;s aggregated data, you are handcuffed to the data cuts that were created in the banner book.</p>
<p>In addition, many brands aren&#8217;t collecting the original data files from the supplier even though they contractually own the data. The end result is a lot of costly survey data thrown out or unutilised.</p>
<h2>DIY Tools</h2>
<p>Another reason for this proliferation of wasted data is that so many DIY tools exist. The tools in themselves are fantastic; however, when you have five &#8211; or even ten &#8211; used across the company, they create bigger silos.</p>
<p>DIY tools also require people to know the login for each one of those platforms. This becomes a huge barrier for simply accessing information. Plus, most of these DIY platforms do not have a search capability, making it even more difficult to find information.</p>
<h2>Personal Hard Drives</h2>
<p>Finally, there are individual hard drives. People today still use their individual hard drives as the source of truth. Yes, it&#8217;s a convenient place to house things, but individual hard drives become a black hole that data goes into and never comes out.</p>
<p>It&#8217;s no wonder the average research and insight professional has over 20 tabs open every day and spends anywhere from four to 10 hours per week searching for knowledge.</p>
<p>There are just too many tools, files, and drives; too many different interfaces, too many subscriptions, and too many logins. Simply too many repositories.</p>
<p>These common challenges can be solved with emerging technology. Platforms for knowledge management, such as <a href="https://www.lucy.ai/benefits">Lucy</a>, and solutions for survey data search and analysis, such as <a href="https://www.knowledgehound.com/">KnowledgeHound</a> are partnering to provide organizations with a one-stop-shop to fully leverage and retain all of their knowledge. Through this partnership, organizations can search through and find answers from all of their structured and unstructured data in one place.</p>
<p>This means one source, one login that key stakeholders can access, a place that stores and maintains all institutional knowledge and data. Which turns hours of painstaking search into mere minutes, dramatically increasing ROI and productivity across the board.</p>
<p>Where to start? We suggest starting with understanding your specific use case and calculating the ROI to be gained with platforms such as Lucy with KnowledgeHound’s integration. Watch our recent <a href="https://www.lucy.ai/esomar-strategies-to-reveal-the-roi-behind-hidden-data">Esomar webinar: Strategies to Reveal the ROI Behind Hidden Data</a> to learn more.</p>


Six Ways Insights Teams Lose Valuable Knowledge

<h2 style="text-align: center;">“If you build it, they will come”.</h2>
<p>Within corporate environments, despite best strategic intentions, digital transformation often fails to be successfully integrated into working practice, due to the many competing demands of time-poor research and insights professionals.</p>
<p>So, argue <strong>Scott Litman </strong>(Managing Partner, <a href="https://www.lucy.ai/benefits">Lucy</a>) and <strong>Andy Whitmore </strong>(Strategy Director, <a href="https://www.438marketing.com/">438 Marketing</a>), it has never been more instrumental to implement effective audience-adoption and engagement strategies, <em>alongside</em> the development of any technological innovation.</p>
<p>Albeit from very different viewpoints, Scott as a tech and product innovator; and Andy as a brand strategist; the two collaborate as partners on global martech projects for Fortune 1000 brands. Together they offer a unique perspective on tech implementation and audience communications.</p>
<p>Their experience shows that to land with impact, disruptive change needs both a comprehensive and sensitive introduction; plus, compelling, enduring communications to ensure the technology is firmly woven into the fabric of daily operating practices, and the potential realised. Here is a summary of ten tips to keep in mind from their recent whitepaper:</p>
<p>&nbsp;</p>
<h2>1. Know your audience</h2>
<p>When defining your tech, it can’t just be about a vision of the tech. Your approach needs to be about a real problem that solves in a material way. Choose and implement your tech to benefit the user which will make a process dramatically better.</p>
<p>To do this start by developing and understanding one user story to focus on solving their problem directly and from this you’ll be able to define the goals you want the tech to solve. It’s crucial to work with tech vendors who partner with you and understand the user, challenge, and goals. They need to know what they are solving too.</p>

        <div class="post-related-post">
            <h3>Related</h3>
            <h4><a href="https://www.greenbook.org/insights/one-journey-three-paths-a-tale-of-technology-adoption">One Journey, Three Paths: A Tale of Technology Adoption</a></h4>
        </div>
    
<p>&nbsp;</p>
<h2>2. Solve problems. Methodically. One. At. A. Time.</h2>
<p>When you understand the capabilities of the vendor, that’s great. But it’s fundamentally better to focus on the problems you and your team are experiencing, determine if they can be solved for, proved with a big gain, and to start there with a pilot program.</p>
<p>It’s crucial not to boil the ocean – even one role/position (for example in research) could have six use cases. Pick one get that right and build from there. What you learn from 1<sup>st</sup> benefit future use cases. It’s the same tech but multiple parts/solves.</p>
<p><strong> </strong></p>
<h2>3. Walk before you run</h2>
<p>There are three approaches to implementation; the third approach (a hybrid of the initial two speeds of adoption) is recommended for most instances.</p>
<p>This approach focuses on measured advancement and spreading budget across phases. The system learns and becomes smarter as each group of users and data are added which supports a successful user adoption process in the longer term.</p>
<p>&nbsp;</p>
<h2>4. Technology Adoption is About More Than Technology</h2>
<p>Technology doesn’t work in vacuum. In fact, for technology to be successful, three waves need to be in play – people, process and content.</p>
<p>Even the world’s greatest tech with poor support/communication/focus from will fail while just ‘ok’ tech with great support of the three will work fine. And among developers blaming tech is common. ‘I tried that social, CMS or whatever. It didn’t work…’ Well, look in mirror did it have the support? If not, it is the wrong root cause.</p>
<p>&nbsp;</p>
<h2>5. Do it on Purpose.</h2>
<p>The fact is, successful tech implementation and adoption is PURPOSEFUL. It doesn’t start with WHAT or HOW, but with <u>WHY</u>.</p>
<p>Developing your purpose is not the same as publishing a Corporate announcement.  It needs to identify why the tech exist and why it is going to <a href="https://www.greenbook.org/insights/brand-strategy/research-should-make-a-difference">make a difference</a> to the user and the organization.</p>
<p>&nbsp;</p>
<h2>6. Start Communicating the minute you start coding</h2>
<p>Don’t wait to communicate.  In fact, we urge our clients to begin to communicate from the earliest possible opportunity – even though the product may be months from launch.</p>
<p>Signal the intent.  Invite them in.  Share the development journey.</p>
<p>You might find not just that early engagement paves the way for a more impactful launch.</p>
<p>&nbsp;</p>
<h2>7. What’s your story?</h2>
<p>Think about it.  How many stories do we all know and fondly remember that have knowingly or unknowingly helped guide us through life?  Stories are the primary way that our ancestors transmitted knowledge and values. They are still our ‘trojan horse’ for educating our kids.  And the most effective technique for message recall.</p>
<p>Every product-brand has a story to tell.</p>
<p>Tell yours. (and live happily ever after…).</p>
<p>&nbsp;</p>
<h2>8. Disrupt and be different</h2>
<p>The world today is messy, noisy, congested, and cluttered.</p>
<p>So, to stand any chance of cutting through to users, new tech must get noticed.   And that demands being different.  No REALLY different.</p>
<p>Spend time understanding what else is competing for users’ mind share.</p>
<p>And be different.</p>
<p>How do you know when you’re doing different?  When it scares the hell out of you.</p>
<p>So go on.  Scare yourself a little.</p>
<p>Be Bold.  Be Brave.  Be different.</p>
<p><strong> </strong></p>
<h2>9. Do it again. And again.  And Again.</h2>
<p>The key to getting noticed is to bore yourself rigid.  Keep putting the same messages out there again and again.  The same colours, the same brand identity, the same propositions, the same benefits delivered in the same tone of voice.  Mix the channels, sure, but stick at it.  Develop a full communications plan to support implementation from pre-deployment through the early phases of adoption.</p>
<p><strong> </strong></p>
<h2>10. Collaborate. COLLABORATE.</h2>
<p>Don’t look for one to do it all.</p>
<p>By choosing partners that are open to developing your organization’s vision &#8211; and will work with other vendors to create the big picture &#8211; you receive the most value in return.</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p style="text-align: center;">For an example of one brand’s ongoing journey with enterprise technology, watch <a href="https://t.sidekickopen70.com/s3t/c/5/f18dQhb0S7lC8fmCKrW2_yQHv2B9nMJW7t5XZs3MPwBCW3N1JMv3LjvSWW7d-33j56dKvCf7rwp_602?te=W3R5hFj4cm2zwW4mKLS-3Z_TFfW1JwHrf45NqbrW3SYMF71GGQC1W4cQ9YR43my2SW3_XXhx3zhs82W3JF4Fl43YlVm4Pc0M1&amp;si=5637367936057344&amp;pi=ba25ebfc-1c37-434f-dc03-88434ecda2ad">PepsiCo&#8217;s quest to democratize knowledge</a>.</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<h3>About the Authors</h3>
<p><img decoding="async" class="aligncenter wp-image-26335" src="https://greenbook.org/mr/wp-content/uploads/2021/01/Lucy_Scott_Litman_.jpg" alt="Scott Litman" width="200" height="200"   /></p>
<p><strong>Scott Litman. </strong>Co Founder, Lucy</p>
<p>Scott is an entrepreneur with a broad history of building businesses that help clients, Fortune 1000s and large ad agencies, take advantage of cutting-edge digital transformation.</p>
<p>&nbsp;</p>
<p><img decoding="async" class="aligncenter wp-image-27567" src="https://greenbook.org/mr/wp-content/uploads/2021/05/Andy-Whitmore.jpg" alt="Andy Whitmore" width="200" height="200"   /></p>
<p><strong>Andy Whitmore. </strong>Founding Partner &amp; Strategy Director, 438.</p>
<p>Andy is a brand strategist with 25 years of experience and a proud track record working consultatively at the C-Suite level in the positioning and repositioning of brands of worldwide renown.</p>
<p>&nbsp;</p>
<p><em>Photo by <a href="https://unsplash.com/@marvelous?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Marvin Meyer</a> on <a href="https://unsplash.com/?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Unsplash</a> </em></p>


Innovate and Communicate: The Symbiotic Keys to Successful Tech Adoption

<p>Over the past 20 years, I have had the good fortune to work with some of the world’s biggest companies in the implementation of emerging digital marketing technology. Along the way, I have learned, through both successes and challenges, quite a few things about organizational adoption of new technology.</p>
<p>&nbsp;</p>
<h2>My biggest takeaway: Technology adoption is a journey.</h2>
<p>Many businesses think that the journey is the exhaustive vendor review or RFP process and the selection of the software. They then deploy the software. The end.</p>
<p>Later on, they wonder, why didn’t it work as expected?  It is because technology adoption is a journey and the vendor selection is simply the prologue—the real journey lies ahead of this. The rollout is the starting gate with a vision of ending the journey with successful adoption and beneficial usage of the new technology.</p>
<p>In thinking about the journey, we need to think of the three different paths of the journey. These are being taken simultaneously and play equal parts to a successful conclusion—the path of the individual, the organization, and the platform.</p>
<p>&nbsp;</p>
<h2>The Path of the Individual</h2>
<p>For the individual user, their path is one of ongoing learning and behavior change. Nobody picks up new software and immediately becomes an expert at it. Often the thought of using new software, no matter how beneficial, seems harder than just dealing with the known challenges it is meant to solve.</p>
<p>There is a continuum starting with first exposure, initial training, and first use. This is often the biggest struggle—getting started. Then, seeing others use and adopt the technology, hearing benefits other users are discovering, drives momentum. As their usage progresses and they start to gaining added proficiency, achieving success and ultimately changing their behavior to best benefit from the software.</p>
<p>&nbsp;</p>
<h2>The Path of the Organization</h2>
<p>The path of an organization is a choice—a highly encouraged choice—that if taken, plays a powerful part in the journey’s successful ending.</p>
<p>For organizations that choose to take the path, the momentum begins as use cases are developed and training programs are implemented. Followed by communication programs to edify how, when, and why to use the software there is even more progression and leadership promotes usage through events and other communication platforms. The organization’s path has a profound impact on the many individual paths that are underway.</p>
<div id="attachment_27007" style="width: 810px" class="wp-caption aligncenter"><img aria-describedby="caption-attachment-27007" decoding="async" class="wp-image-27007" src="https://greenbook.org/mr/wp-content/uploads/2021/03/events-and-leadership-chart-e1614896521278.png" alt="bar chart, &quot;Questions Asked per Month.&quot;" width="800" height="474"   /><p id="caption-attachment-27007" class="wp-caption-text">Events and leadership announcements drive short-term spikes and long-term gains.</p></div>
<p>&nbsp;</p>
<p>In the graph above, we have seen this play out many times where initial usage is light when smaller populations of users get initial training, and these individuals start their individual paths. Then usage slowly grows as the population of users grows and some of the earlier users gain more proficiency.</p>
<p>We see spikes driven by promotional events, leadership communication, special projects that demand the tech, etc. Following these major events, we see some drops in usage, but usage levels still far exceed from before the organization.</p>
<p>The organizational path is not an overnight success. It isn&#8217;t flip the switch and everyone is crushing. It is a gradual, but constantly progressing evolution of adoption and usage. It is critical in encouraging individuals down their path of a permanent change in behavior toward the universal journey of beneficial adoption.</p>
<p>&nbsp;</p>
<h2>The Path of Technology</h2>
<p>Then there is the path of the technology. In the old days, this was a slow one, broken up into major software releases that might occur on a quarterly or semi-annual basis. Thankfully, in the world of AI, this has been transformed.</p>
<p>True AI systems are learning engines, improving every day based on their learnings from usage. The software will be smarter tomorrow than it is today. That remains true every day of this overall journey. While that is fundamentally a great, if not amazing thing, it also needs to be calibrated into the understanding of the other path’s that are going on simultaneously to lead everything</p>
<p>&nbsp;</p>
<h2>Three Paths, One Journey</h2>
<p>When all three paths are traveled, intersecting, and time is taken to clean up any unforeseen roadblocks along the way, companies start seeing the paths connect and come together to drive the journey to successful completion. The true benefits of the technology not only are realized but continue to grow and strengthen.</p>
<p>If you are interested in tips on driving adoption or challenges to watch out for, I recently  shared some additional lessons learned during a recent webinar <a title="The Promises and Pitfalls of Market Insights Platforms" href="https://www.lucy.ai/the-promises-pitfalls-of-market-insights-engines-watch-now" target="_blank" rel="noopener">&#8220;The Promises and Pitfalls of Market Insights Platforms.&#8221;</a>  Though focused on insights platforms, many strategies shared can be easily translated to other technology platforms.</p>
<p><em>Photo by Mentatdgt on Pexels</em></p>


Technology adoption is a journey and the vendor selection is simply the prologue. How to make sure your organization's adoption of new technology is a success.

One Journey, Three Paths: A Tale of Technology Adoption

<p><span style="font-weight: 400;">Insights and marketing research teams in every industry are faced with the issue of holding a bounty of information without an effective way to search or share the unstructured data or knowledge assets owned by the company. </span></p>
<p><span style="font-weight: 400;">In a world of expansive research and data, there is simply too much to learn. Nobody can find </span><span style="font-weight: 400;">the information. Or worse, nobody knows that it exists. </span></p>
<p><span style="font-weight: 400;">This is the reason why so many data-driven brands are planning, budgeting, piloting, or fully deploying AI-powered knowledge management systems right now. </span></p>
<p><span style="font-weight: 400;">Just as every business has been affected by digital and mobile technology, every business will be impacted by artificial intelligence (AI). That being said, there needs to be a rigorous test to determine if the AI is ready for your particular needs when it comes to data, research, and insights.</span></p>
<p><span style="font-weight: 400;">Businesses that take the time to evaluate different AI-powered knowledge management systems with realistic expectations and make strategic plans for the implementation and adoption of a system, will have a competitive advantage.</span></p>
<h3><span style="font-weight: 400;">Walk Before You Run With AI</span></h3>
<p><span style="font-weight: 400;">The first step to evaluate solutions is to find any low-hanging fruit in terms of the problems you’d like to solve, the employees you’d like to support, and the data you’d like to draw insights and research from.</span></p>
<p><span style="font-weight: 400;">Some common challenges that enterprises can solve for include:</span></p>
<ul>
<li style="font-weight: 400;"><b>Redundant Research:</b><span style="font-weight: 400;"> Millions of dollars are spent recommissioning research that has already been completed.</span></li>
<li style="font-weight: 400;"><b>Dark Data:</b><span style="font-weight: 400;"> After 90 days, documents and data that are not actively in use are likely never to be opened or used again.</span></li>
<li style="font-weight: 400;"><b>Employee Turnover:</b><span style="font-weight: 400;"> When someone starts, leaves, or changes teams, previous knowledge is lost. Finding answers means deep exploration or duplication of the predecessors’ work.</span></li>
<li style="font-weight: 400;"><b>Acquisition Chaos:</b><span style="font-weight: 400;"> Times of rapid change during mergers or acquisitions require teams to get up to speed on a new influx of data.</span></li>
<li style="font-weight: 400;"><b>The Hunt:</b><span style="font-weight: 400;"> Storage systems require users to recall document titles, dates, or tags to search. This limits how people search for content and doesn’t point to answers or specific data within documents. Furthermore, the categorization of documents is rarely consistent between people and teams, making it even harder to find information.</span></li>
</ul>
<p><span style="font-weight: 400;">From our perspective, we tend to look at the biggest information challenge. Specifically, what is the data that you wish was being used better? Or, what is the data that you struggle to find? </span></p>
<p><span style="font-weight: 400;">Imagine being able to leverage video assets such as focus groups, presentations created by teams in different locations, audio recordings of meetings, and consumer research tucked away in PDFs simply by asking a natural language question and then you are delivered your answer, wherever it lives, in seconds. </span></p>
<p><span style="font-weight: 400;">These are just a few examples from our lens; however, there are all sorts of opportunities in information technology and human resources where AI-powered knowledge management can also have a profound impact across the organization.</span></p>
<p><span style="font-weight: 400;">When you understand the capabilities of the vendor, that’s great. But it’s fundamentally better to focus on the problems you and your team are experiencing, determine if they can be solved for, proved with a big gain, and to start there with a pilot program.</span></p>
<h3><span style="font-weight: 400;">Example Pilot Programs for AI Knowledge Management Systems</span></h3>
<p><span style="font-weight: 400;">Rather than jumping in headfirst at the enterprise level, it is best to identify specific use cases f</span><span style="font-weight: 400;">or a pilot program. This involves identifying certain data and groups to solve for, executing on a solution, then rolling out that success to other users and use cases. One way to identify a starting use case is to ask your teams about their pain points or challenges while gathering information. The following are examples of places to start.</span></p>
<h3><span style="font-weight: 400;">Organizational Efficiency</span></h3>
<p><span style="font-weight: 400;">Smart companies are looking for efficiencies where they can, in every corner of the company.</span></p>
<p><span style="font-weight: 400;">It is a fact of everyday life that insights departments have to continuously try to do more with </span><span style="font-weight: 400;">the same or less. One strategy is to help other employees extract all the possible value they can from the insights and research that has already been created. </span><span style="font-weight: 400;">Knowledge created for one reason can be repurposed for another.</span></p>
<p><span style="font-weight: 400;">While processes have definitely improved over the years, frustrations remain when employees are shown shared drives with endless amounts of assets they will never find, read, or obtain knowledge from.</span></p>
<p><span style="font-weight: 400;">Imagine instead a system where everyone is empowered with the company’s legacy knowledge. They can ask specific questions regarding the task at hand, instantly find answers within internal assets and resources, and move forward with their work without wasting time searching or repeating work already done. Data-driven decisions can now be made at the drop of a hat.</span></p>
<h3><span style="font-weight: 400;">Reduce Demand on Subject Matter Experts</span></h3>
<p><span style="font-weight: 400;">When a company relies heavily on subject matter experts, those individuals are often overloaded with one-off requests beyond their regular responsibilities. </span></p>
<p><span style="font-weight: 400;">Regardless of processes put into place to document and train other employees, individuals still reach out to the experts for information on a regular basis. This inhibits their productivity and value to the company. And if they come to a breaking point, some SMEs choose to leave, resulting in turnover issues and lost knowledge. In this use case, the goal is to empower teams to rely less on subject matter experts for one-off requests.</span></p>
<p><span style="font-weight: 400;">Does this type of scenario circumvent the importance of subject matter experts? No. But, </span><span style="font-weight: 400;">it is critical to communicate the benefit of gaining back time to focus on their high-priority </span><span style="font-weight: 400;">responsibilities. </span></p>
<h3><span style="font-weight: 400;">Bring More Research In-House</span></h3>
<p><span style="font-weight: 400;">Large companies tend to rely on outside vendors to conduct research. These projects tend to be expensive and are often repeated as different brand teams target the same market and are unaware that outside research was already shopped out and delivered.</span></p>
<p><span style="font-weight: 400;">It’s easy to see how redundant research happens. A brand gets a new manager who identifies the need for a new marketing plan to reach the brand’s target consumer. The brand manager has no efficient way of investigating whether the previous team had valuable insights that could be reused, or whether other divisions of the company have run analysis on the same sort of consumer.</span></p>
<p><span style="font-weight: 400;">Without an effective way to quickly find answers, they need to start from scratch. The process </span><span style="font-weight: 400;">takes a month and has a sizable budget. A few years later, they move on to another position.</span></p>
<p><span style="font-weight: 400;">Their replacement starts in the same position – with questions that need to be answered, and with no easy way of searching existing company assets for relevant data. So, the cycle of redundant research begins anew.</span></p>
<p><span style="font-weight: 400;">To choose a team or brand for a pilot implementation, look for early adopters of other technologies who innovate and lead naturally.</span></p>
<h3><span style="font-weight: 400;">Getting Started</span></h3>
<p><span style="font-weight: 400;">Defining a starting point for a successful implementation is a critical step in the process. </span></p>
<p><span style="font-weight: 400;">Remember to start small with a targeted pilot program and grow adoption through positive </span><span style="font-weight: 400;">use cases around small user groups, individuals with the biggest need, or highly sought after </span><span style="font-weight: 400;">content.</span></p>
<p><span style="font-weight: 400;">For more information on evaluating, implementing, and adopting AI-powered knowledge management systems, </span><a href="https://www.lucy.ai/how-to-evaluate-implement-ai-powered-knowledge-management-greenbook"><span style="font-weight: 400;">check out our full guide here</span></a><span style="font-weight: 400;">.</span></p>


From Insights to Action: Walk Before You Run With AI

<p><strong><em>Editor&#8217;s Note:</em></strong> <i>Smart companies are looking for cost savings where they can, in every corner of the company. It is a fact of everyday life that Insights departments have to continuously try to do more with the same or less. One strategy is to extract all the possible value they can from the data they already own. Data collected for one reason can be repurposed for another. In a really interesting piece, Scott Litman describes AI-based means by which companies can repurpose large amounts of data, saving money, time and effort in the process. A valuable read.</i></p>
<hr />
<h3>Data, Data Everywhere</h3>
<p><span style="font-weight: 400;">The amount of data available to brands as they track performance and strategize next moves is </span><i><span style="font-weight: 400;">astronomical</span></i><span style="font-weight: 400;">. The digital world has created an evolutionary leap, and we’re still inventing new tools to help us survive in it. </span></p>
<p><span style="font-weight: 400;">One of the latest tools is the advanced class of knowledge management systems tagged with labels like “cognitive,” “AI-enabled,” or “machine-learning.” As with any new technology, the cost must be justified by value and insight professionals who clearly see the benefit often need to persuade those higher up the food chain. </span></p>
<p><span style="font-weight: 400;">One unexpected area AI-powered knowledge management tools are showing their value is in eliminating redundant research. Redundant research is the source of millions of wasted dollars. We know this because global brands themselves have researched the issue. (And extrapolating on the answers they found, we might even guess they have researched the issue several times, come to the same conclusion, then shelved the knowledge away somewhere inaccessible.) </span></p>
<h3>Improve Efficiency</h3>
<p><span style="font-weight: 400;">It’s easy to see how redundant research happens — but it has been hard to see a way to solve it, until now. </span></p>
<p><span style="font-weight: 400;">A brand gets a new manager, who identifies the need for a new marketing plan to reach the brand’s target consumer. The brand manager has no efficient way of investigating whether the previous team had valuable insights that could be re-used, or whether other divisions of the company have run analysis on the same sort of consumer. </span></p>
<p><span style="font-weight: 400;">They could try asking the Insights Team. Though the Insights professionals are already carrying a heavy workload and have too many demands on their time. They have much more important things to do for the organization than to answer all of the one-off questions across the company.</span></p>
<p><span style="font-weight: 400;">Plus, there is no humanly way possible to remember every nugget of information ever produced, much less know exactly where each brand team may have stored their assets across company silos. </span></p>
<p><span style="font-weight: 400;">With no effective way to quickly find answers, they need to start from scratch. The process takes a month and has a sizable budget. A few years later, they move on to another position. Their replacement starts in the same position—with questions that need to be answered, and with no easy way of searching existing company assets for relevant data. So, the cycle of redundant research begins anew. </span></p>
<p><span style="font-weight: 400;">It’s not just a waste of money. Brands that are quickest to react to an insight have a competitive edge. What company wants to take unnecessary time out every few years because their internal knowledge management system isn’t up to the task?</span></p>
<h3>AI Means Big Impact</h3>
<p><span style="font-weight: 400;">This is where companies will see a huge impact on the adoption of AI-enabled knowledge management systems. For a system to truly save companies from redundant research, that brand manager I mentioned needs to be able to open a single application, play around for ten minutes asking a handful of questions, and walk away with certainty that the assortment of a dozen charts, reports, and PowerPoint decks their search returned represents a thorough examination of hundreds of thousands of company-owned data assets. </span></p>
<p><span style="font-weight: 400;">And when any team creates a report, the knowledge management system needs to automatically ingest it and search it. These are marketers and insights professionals, not archivists, so it’s a waste of their skillsets to make them tag data assets with keywords in the hopes of leaving a trail for their successors. </span></p>
<p><span style="font-weight: 400;">Take the example of a manager who was ready to start a research project to gain insights about moms who buy healthy kid’s yogurt. They start by asking the company’s AI-powered knowledge management system, “What do you know about moms who buy healthy food for kids?” Instantly, they find quick facts from a report the past manager did, and even better an intensive research study commissioned six-months ago from a cereal brand team with the exact information needed. The time and money saved from recreating past work can now be used for strategic action.</span></p>
<h3>Solving the Problem</h3>
<p><span style="font-weight: 400;">Cloud computing and machine-learning have combined to solve massive storage and smart-search problems. For the first time, inadvertent duplication of research commissions need not be part of the “cost of doing business.” </span></p>
<p><span style="font-weight: 400;">A global CPG company who recently piloted an AI knowledge platform called it “game-changing.” Starting with 10,000 documents that the client did not have to tag or help with beyond providing access to file systems for ingestion, they were able to test the system in just a couple weeks. With little to no training, the AI-based system was already providing specific answers to questions. Not only did the marketers using the system report that they were rapidly finding the answers they were looking for, but they also were uncovering key data they didn’t even know existed. </span></p>
<p><span style="font-weight: 400;">When different teams operating at different points in time all feed their final insights into a central, shared state-of-the-art knowledge management system, their discoveries remain close at hand. This secure and specific sharing, coupled with the advancements in a search made possible by cognitive computing systems, will change the way resources are allocated. </span></p>


How AI is Ending Redundant Research Waste

<p>We live in an era when businesses are losing knowledge faster than they can absorb it. If you think about all the PowerPoints, PDFs and Word documents that a business possesses, there’s no way to effectively harness it all. It’s simply too much data and information to keep track of. We’ve even seen situations where trying to find information is so challenging that customers are actually commissioning projects for work that was already done because they aren’t even aware that the work already exists.</p>
<p>With the speed of modern business and high demand for quick, impactful results, businesses need all the help they can get in harnessing their data assets. We are starting to see AI systems evolving to solve issues related to enterprise search—maintaining a bounty of information and being able to pinpoint where specific documents and information are stored. While the idea of asking “What were the biggest drivers of customer acquisition in 2017,” and instantly getting the specific answer from page 23 of a 75-page PowerPoint, seems like a fantasy, AI is making this commonplace. Here are a few scenarios in particular:</p>
<p>&nbsp;</p>
<h2>Human Limitations</h2>
<p>We work with a company that has a specialized team of over 30 Insights and Research professionals dedicated to Knowledge Management. The company has invested significant capital into legacy knowledge management systems. Despite this, they still needed to know the specific date, titles or identifying tag of a particular document to find what they were looking for. You can imagine the limitations in that approach. Not only that, but they were also limited to finding the entire document, rather than the precise location of the information. Good luck finding your answer in a 150-page document that requires you to read and understand the contents within. Through the power of AI, this company can now cut down days or weeks-worth of research into mere minutes.</p>
<p>&nbsp;</p>
<h2>Changing Partners</h2>
<p>Another example involves a multinational conglomerate that recently changed agencies. The previous agency was asked to share information with the new agency, so they packaged thousands of PDFs and sent them over to the new team. Some of these PDFs were hundreds of pages long, so realistically, there was no way for the new agency to get up and running quickly without a team full of speed-reading insight experts. The reality is that this would be impractical, as even reading all of the content wouldn’t help with proper recall of the bits and pieces needed weeks and months into the future. With AI, they are able to ingest every PDF in a system that will remember everything it’s read and recall it when requested at any point in the future.</p>
<p>&nbsp;</p>
<h2>New Staff</h2>
<p><strong> </strong>Another example is a company where people flow from account to account regularly. While this keeps things fresh for staff, nobody knows where documents and information live. Titles and keywords are helpful to an extent, but they provide little access to the actual content that’s in the documents. They are forced to piece together findings based on a deep exploration process, sifting through hundreds of documents created by their predecessors. With AI, they retain the past knowledge which allows new teammates to perform at a high level more quickly.</p>
<p>The challenges outlined above aren’t new challenges for businesses. For years, businesses accepted these challenges as the “cost of doing business” because they didn’t know that this was something that could be solved. AI is changing that. For the first time, these challenges can be easily solved with the latest generation of AI-based knowledge management tools. Now, the game has changed.</p>
<p>&nbsp;</p>
<div><em><strong>Editor&#8217;s Note:</strong> It struck me almost from the very beginning of my market research career that we were dramatically underusing information that already existed.  The sad reality was that it was a lot in easier many cases to field a new survey than to try to dig up old </em>cross tabs<em> or raw data.  What a waste!  The budget crunch that started with the financial crisis of a decade ago gave new impetus for finding ways to repurpose old data.  As Scott Litman discusses, it is a perfect problem for AI systems to solve. </em></div>


How AI is Completely Transforming Knowledge Management for Businesses

<p>The world has changed—where workers work, how businesses run, what consumers want. All of this creates a critical need to connect an organization&#8217;s people and knowledge, regardless of where the information lives or employees are working.</p>
<p>Think about the vast repositories of data just one brand in an organization holds—millions of pages of PowerPoint presentations, PDFs, Word documents and more. Imagine the expense and effort to create these decks, reports, plans and insights. After 90 days 95% + become permanently lost. Often the same work must be repeated because no one can find where it is or knew that someone has already done the work.</p>
<p><strong>Common challenges include:</strong></p>
<ul>
<li>Time and information lost from inability to locate the existing insights</li>
<li>Inefficiency in how information and insights are shared across the company</li>
<li>No standardized tagging to organize unstructured data</li>
<li>3-4 times the cost and inefficiency of redundant research</li>
<li>Retention of institutional knowledge as people leave and knowledge is lost</li>
<li>Inability to digest and leverage video assets such as focus groups, town hall meetings, or training sessions</li>
<li>Dependence on subject matter experts</li>
</ul>
<p>&nbsp;</p>
<h2>We are increasingly seeing businesses prioritize the need for knowledge portals to provide immediate access to research and insights to solve these challenges.</h2>
<p>While knowledge management has been around for decades, the importance of having a user-friendly system drove innovation for a variety of digital platforms. However, many of those systems still have their shortcomings. That most require users to identify, upload and tag enormous amounts of content and data before, during and after the implementation process remains a huge obstacle and impedes adoption.</p>
<p>“The better KM solutions are designed to optimize delivery of knowledge in a manner that minimizes the user’s investment of time.” <a href="https://www.destinationcrm.com/Articles/Columns-Departments/Reality-Check/Is-It-Knowledge-Managements-Time-144802.aspx">wrote Donna Fluss, president, DMG Consulting</a>. “…given the volume and diversity of information these solutions hold, automation is the only viable way to keep them current. It’s also an area where AI and machine learning can make major contributions, and something companies should examine during the buying process.”</p>
<p>The latest breed of AI-powered knowledge management systems include automation to integrate with your existing assets where they live natively, eliminating the tedious task of curations, uploading, and tagging content.</p>
<p>&nbsp;</p>
<h2>Benefits of Automation vs Uploading</h2>
<h3>Data Ingestion</h3>
<p>Though vendors make uploading sound simple and demonstrate a friendly interface for doing so, their clients quickly find that it is an extremely labor-intensive task. It also <strong>creates the ongoing issue around how to ensure that new and /or updated information is continually added into the system</strong>. This is time-consuming and it forces clients to move data that is stored in existing secure environments into a new environment. All of which duplicative, and not trivial, costs to clients using their platforms.</p>
<p>In contrast, systems with automated integration operate with the data where it natively lives, ensuring users always have access to the most up-to-date information and eliminates the painful uploading process. It is critical in terms of ensuring compliance with your user access, privacy, and security standards. It helps with usage and adoption, as users do not have to go through a separate step to upload new documents into the system, nor do they have the stress of worrying about whether documents are in the system or not.</p>
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<h3>Categorization and Tagging</h3>
<p>Manually entering content tagging is unnecessary and obsolete. <strong>Insights professionals do not have the time to take on the laborious and meticulous effort of tagging and categorizing content</strong>, never mind doing it accurately and consistently across all sources, including third-party subscriptions.</p>
<p>The system should be your enterprise librarian—automatically tagging and categorizing your documents based on multilayer, filterable metadata. This is an immense time saver for users. Content gets tagged across various levels (e.g., themes, topics, concepts, keywords etc.) at the individual answer unit (i.e., every paragraph of each document; each moment of audio/video). While some systems have varying levels of auto-tagging, they do not go as far as tagging to the answer unit to serve up the best answers such as a given slide within a 92-page PowerPoint. This unique benefit of complete automation, allowing the system to find the proverbial needle in a haystack and is impossible to do manually.</p>
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<h2>Knowledge Automation = A one-stop Insights engine</h2>
<p>Automation also effortlessly brings internal and external systems together<strong>. </strong>By avoiding the pitfalls, <strong>your organization can truly harness the power of next-generation knowledge portals, capable of linking together all knowledge that can include answers from internal documents &amp; subscriptions, data visualizations from databases, finding experts within the organization, and linking together critical tools to create a one-stop-shop for insights</strong>. <a href="https://www.linkedin.com/pulse/introducing-my-new-hire-ada-stephan-gans/">PepsiCo’s Ada</a> is one such example that is setting the standards for the future.</p>
<p>As you are comparing knowledge management systems, keep in mind the importance of automation. By eliminating the common challenges of obsolete systems, you will gain speed of implementation, adoption, usability, and results.</p>
<p>&nbsp;</p>
<p><strong>For more information download the guide <a href="https://www.lucy.ai/Uploading_is_Obsolete">Uploading is Obsolete: Automated Knowledge Management is the New Standard</a>.</strong></p>
<p><em>Photo by <strong><a href="https://www.pexels.com/@malwina-zalewska-187025?utm_content=attributionCopyText&amp;utm_medium=referral&amp;utm_source=pexels">Malwina Zalewska</a></strong> from <strong><a href="https://www.pexels.com/photo/green-sod-677825/?utm_content=attributionCopyText&amp;utm_medium=referral&amp;utm_source=pexels">Pexels</a></strong></em></p>
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Automation vs. Uploading, How to Make an Insights Engine Effortless

<p>No matter what an organization specializes in, there are certain frustrations that show up across the board. One of the biggest is the amount of time that’s wasted just looking for information.</p>
<p>Between searching through internal repositories and interrupting coworkers over Teams, Slack, e-mail etc., that amount of time can stretch from minutes to hours, and even from hours to days.</p>
<p>Think about how often your team gets pulled off their strategic priorities to answer redundant questions, simply because people can’t find the answers. What does that add up to across your entire company, over months and years? That&#8217;s where a data warehouse could come in handy &#8212; something to make existing data accessible to team members.</p>
<p>Knowledge management platforms were created to put an end to this problem. They can radically reduce the amount of time, resources, expertise and spend that had been all but wasted previously. But not all platforms work the same.</p>
<p>Many knowledge management systems require you to curate and upload every docu­ment you want to include in the platform, making a file-by-file decision about what’s valuable for the platform and what’s not. That process con­tinues even after the platform has been deployed.</p>
<p>Being tasked to upload a file sounds simple, though in actuality it is amplifying a bigger problem. It creates the ongoing issue around how to ensure that new and/or updated information is continually added to the system. Decision-makers need to be trained in which future documents should be included and which should be ignored. It’s ultimately time-consuming, labor intensive, and often results in low adoptability as users don’t trust the latest information to be in the system. That actually gets in the way of business decisions.</p>

        <div class="post-related-post">
            <h3>Related</h3>
            <h4><a href="https://www.greenbook.org/insights/insights-industry-news/how-ai-is-completely-transforming-knowledge-management-for-businesses">How AI is Completely Transforming Knowledge Management for Businesses</a></h4>
        </div>
    
<p>You’ll be best served by a platform that automates the process by integrating into other systems, rather than requiring that everything be shifted onto it. For example, many organizations already have dozens or hun­dreds of SharePoint sites. An automated system continually indexes the information across all sites ensuring the platform stays up to date. When someone adds an asset or changes an existing asset, that file is automatically indexed. When someone deletes an asset, that asset is purged from the system. It also eliminates the issue of moving assets from any already-secured internal systems and firewalls which is especially critical in terms of ensuring compliance with user-level privacy and security standards.</p>
<p>Simply put, businesses do not have time to upload and curate data. It is too time-consuming. No system should put that time type of burden on its users. And while uploading (or dragging and dropping) a document is demonstrated as easy; it just does not scale across an enterprise.</p>
<p>As you scale users, teams or data repositories, platforms with automation capabilities flex with your company. Organizational users can quickly expand from the pilot team to more than tens of thousands of users—automatically <a href="https://www.greenbook.org/insights/knowledge-management/lead-by-example-how-insights-managers-can-improve-knowledge-engagement">democratizing insights</a> and knowledge across an organization.</p>


The Importance of Automation for Knowledge Management

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