Participants learned:

• Ways to build buy-in by identifying “almost taxonomies;”

• Ways to illustrate how taxonomies can ease specific pain points, benefit end users, and drive cost savings;

• How to develop a working knowledge of generative AI, and establish a realistic way to integrate taxonomy; and

• How to communicate tangible results and value at each taxonomy development milestone.

The post Consulting from Within: Best Practices for the Solo Taxonomist appeared first on Enterprise Knowledge.

Enterprise Knowledge (EK), the world’s largest dedicated knowledge and information management consulting firm, announced the launch of the Knowledge Graph Accelerator today, a mechanism to establish an organization’s first knowledge graph solution in a matter of weeks. In partnership with data.world, the knowledge graph-based enterprise data catalog, organizations will be able to rapidly unlock use cases such as Employee, Product, and Customer 360, Advanced Analytics, and Natural Language Search. 

“Knowledge Graphs are a critical component necessary to achieve Enterprise AI, but most organizations need a quick and scalable way to understand and experience the value,” said Lulit Tesfaye, Practice Lead of Data and Information Management at EK. “EK, in partnership with data.world, is creating a holistic solution to make building Enterprise AI intuitive using knowledge graphs, from data modeling and storage to enrichment and governance. Having this end-to-end consistency is critical for the success of knowledge graph products and setting the foundations for Enterprise AI.”

“EK has been at the leading edge of Knowledge Graph strategy, design, and implementation since our inception,” added Zach Wahl, CEO of EK. “Our thought leadership in this field, combined with data.world’s advanced capabilities, creates an exciting opportunity for organizations to feel the impact and realize the benefits quickly and meaningfully.”

Gartner predicts that graph technologies will be leveraged in over 80% of innovations in data and analytics by 2025, but many organizations find the business and technical complexities of graph design and implementation to be daunting. The Knowledge Graph Accelerator addresses the need to develop a practical, standards-based roadmap and prototype to quickly realize the potential of knowledge graphs. 

Through the Knowledge Graph Accelerator, organizations will get the following outcomes in less than 2 months:

• An understanding of the foundations of knowledge graphs, including graph data modeling, data mapping, and data management;
• A first implementable version (FIV) knowledge graph that can be scaled and enhanced;
• A pilot version of your graph solution leveraging the knowledge graph-based data management solution data.world and gra.fo; and
• A strategy for your organization to make Enterprise AI a reality. 

“Enterprises need to understand and trust the data powering their analytics while generating meaningful insights. But supporting different data sources and use cases, while analyzing and traversing changes to metadata and automating relationships can be challenging,” said Dr. Juan Sequeda, Principal Scientist at data.world. “Knowledge graphs are foundational for an effective and future proof data catalog, as well for next generation AI and analytics .”

To learn more, explore our approach and what your organization will get through the Knowledge Graph Accelerator. Also, reach out to Enterprise Knowledge to learn how to unlock the use cases that are most valuable to your enterprise. 

On September 29th, 2021,  Enterprise Knowledge will participate in the virtual data.world fall summit. Additional keynote speakers include Zhamak Dehghani, Barr Moses, Doug Laney, and Jon Loyens. 

About Enterprise Knowledge 

Enterprise Knowledge (EK) is a services firm that integrates Knowledge Management, Information and Data Management, Information Technology, and Agile Approaches to deliver comprehensive solutions. Our mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs. At the heart of these services, we always focus on working alongside our clients to understand their needs, ensuring we can provide practical and achievable solutions on an iterative, ongoing basis. Visit enterprise-knowledge.com to see how optimizing your knowledge and data management will impact your organization.  

About data.world

data.world is the enterprise data catalog for the modern data stack. Our cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful knowledge graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than 1.3 million members, including 2/3 of the Fortune 500. Our company has 40 patents and has been named one of Austin’s Best Places to Work six years in a row. Follow us on LinkedIn, Twitter, and Facebook, or join us.

The post Enterprise Knowledge and data.world Partner to Make Knowledge Graphs More Accessible to the Enterprise appeared first on Enterprise Knowledge.

Enterprise Knowledge (EK) has been listed on KMWorld’s 2021 list of leaders in Artificial Intelligence, the “AI 50: The Companies Empowering Intelligent Knowledge Management.” This is the second year in a row EK has been included. To help spotlight innovation in knowledge management, KMWorld developed the annual KMWorld AI 50, a list of vendors that are helping their customers excel in an increasingly competitive marketplace by imbuing products and services with intelligence and automation.

Unique to the list, EK is one of the few dedicated consultancies that made the list, offering end-to-end technology selection, strategy, design, implementation, and support services for the full range of Enterprise AI components, including knowledge graphs, natural language processing, ontologies, and machine learning tools.

“A spectrum of AI technologies, including machine learning, natural language processing, and workflow automation, is increasingly being deployed by sophisticated organizations,” stated KMWorld Group Publisher Tom Hogan, Jr.  “Their goal is simple. These organizations seek to excel in an increasingly competitive marketplace by improving decision making, enhancing customer interactions, supporting remote workers, and streamlining their processes. To showcase knowledge management solution providers that are imbuing their offerings with intelligence and automation, KMWorld created the ‘AI 50: The Companies Empowering Intelligent Knowledge Management.’ ”

Lulit Tesfaye, EK’s Practice Leader for Data and Information Management, shared, “Given our continued leadership in this space, and the growth of our team and its capabilities, I’m proud to be recognized in this way. We are increasingly seeing our work with customers grow from initial assessments and prototypes into enterprise engagements that are transforming the way they do business. I’m proud to be leading in this exciting space.”

EK CEO Zach Wahl added, “Thanks to KMWorld for this recognition. KM and AI are increasingly coming together, and we’re pleased to be leading organizations in their transformations to intelligent knowledge organizations.”

To read more about the recognition, visit Lulit’s AI Spotlight article on KMWorld and explore EK’s knowledge base for the latest thought leadership.

About Enterprise Knowledge

Enterprise Knowledge (EK) is a services firm that integrates Knowledge Management, Information Management, Information Technology, and Agile Approaches to deliver comprehensive solutions. Our mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs.

About KMWorld

KMWorld is the leading information provider serving the Knowledge Management systems market and covers the latest in Content, Document and Knowledge Management, informing more than 21,000 subscribers about the components and processes – and subsequent success stories – that together offer solutions for improving business performance.

KMWorld is a publishing unit of Information Today, Inc. 

The post EK Again Listed on KMWorld’s AI 50 Leading Companies appeared first on Enterprise Knowledge.

Before going “all in,” we often advise our clients to first understand and quickly validate the value proposition for adopting advanced Artificial Intelligence (AI) and Machine learning (ML) solutions within their organization by engaging in a beyond AI project prototype or pilot. Conducting such targeted experimentations not only provides the enterprise with a safe way to validate that AI and ML solutions will solve real problems, but also provides a design foundation for key AI elements required for their roadmap and supports long-term change management by showing immediate incremental benefits and developing interest.

Without the appropriate guidance and strategy, AI efforts may still get stalled right after a prototype or proof of concept, regardless of how successful these initial efforts may have been. 

Although 84% of executives see the value and agree that they need to integrate and scale AI within their business processes, only 16% of them say that they have actually moved beyond the experimentation phase.

Mainly informed by the diverse set of organizational realities and AI projects we have delivered, below I will explore the common themes I see when it comes to potential roadblocks in moving from prototype to enterprise, and provide a selection of approaches that I have found helpful in scaling enterprise AI efforts.  

1. Understand that AI projects have unique life cycles

In software delivery, Agile and DevOps continue to serve as successful frameworks for allowing iterative delivery, getting the product closer to the end user or customer and ultimately delivering immediate value. However, Enterprise AI efforts have surfaced the need to revisit Agile delivery within the context of AI and ML processes. What this means for the sponsoring organization and the teams involved is that any project management or delivery approach that is employed will need to balance the predictable nature of software programming with facilitation and ongoing education about expected machine outcomes for the end-user and subject matter expert (SME), while balancing the random number of experimental data ingestion and model refinement required for AI deliverables.

Enterprise AI projects typically have a number of workstreams or task areas that need to be at play, in parallel. These include use case definition, information architecture, data mapping and modeling, integration and pipeline development, the data science side of things where there are multiple Machine Learning (ML) processes running, and, of course, the software engineering aspect that is required to connect with downstream or upstream applications that will render the solution to end users. With all these variables at play, the following approaches help to build a more AI-centric delivery framework: 

• Sprints for data teams are different: While software programming or development is focused on predefined applications or features, the primary focuses for data science and machine learning tasks are analysis, modeling, cleaning, and exploration. Meaning, the data is the center of the universe and the exploration process is what determines the outcome or the features being delivered. The results from the machine and data exploration phase could result in the project having to loop back to the planning phase. As such, the data workstream doesn’t necessarily need to be within or work through the same sprint as the development team.

AI Project Delivery Iterations 

• Embed research or “spike” sprints to create room for understanding and data exploration: Unlike humans, machines need to go through diverse sets of data to understand the context within which it is being applied at your organization (a knowledge graph significantly helps in this process) and align it to your expected results. This process requires stages of understanding, analysis, and research to identify relevant data. Do your AI projects plan for this research? 
• Embrace testing and quality assurance (QA) from the start: Testing in AI/ML is not limited to the model itself. Ensuring the data quality stays sufficient to serve use cases and having the right entry point checks in place to detect potential data collection errors is a foundational step before starting the model. Additionally the QA process in AI and ML projects should take into account the ability to test integration points as well as any peripheral systems and processes that serve as inputs or outputs to the model itself. As time goes by, having a proven integration and automation process to continue updating and training the model is another area that will require automation itself. 
• Prepare for organizational impact: When it comes down to implementation, some projects are inherently too big. Imagine replacing legacy applications with AI technology and models, for instance. There needs to be supporting organization-wide processes in place to ensure your model and delivery is supported all the way throughout strategy, implementation, and adoption. There are more players that need to be involved in addition to the project team itself. 

2. Know what is really being delivered

For machine learning and AI, the product is the algorithm, or the model, not necessarily the accuracy of the results. Meaning, if the model is right, with the right data, it will deliver the intended results. Otherwise, garbage in, garbage out. Understanding this dynamic is key when defining acceptance criteria and your minimum viable product. Additionally, leveraging UI/UX resources and wireframing sessions facilitates the explanation of what the AI tool really is and sets expectations around what it can help stakeholders achieve before they test the tool.

• • AI scope is mostly driven by two factors, use cases and available data: Combining top-down discovery and ideation sessions with end-users and subject matter experts (SMEs) with bottom-up mapping and review of content, data, and systems is a technique we use to narrow down AI/ML opportunities to define the initial delivery requirements. As the project progresses, there will almost always be new developments, findings, and challenges that arise. The key to successful definition of what is really being delivered is building the required flexibility into iteration cycles and update loops for end-users and SMEs to review exploratory results from the data and ML workstream regularly and provide context and domain knowledge to refine the model based on available datasets. 
  • Plan for diverging stakeholder opinions: Machine learning models are better than a human at browsing through thousands of content items and finding recommendations that organizational SMEs may not have thought of. However, your current data may not necessarily capture the topics or the “aboutness” of how your data is used. Encouraging non-technical stakeholders to provide context by participating in the ideation and the acceptance criteria development process is key. You need SMEs to help create a rich semantic layer that captures key business facts and context. However, your stakeholders or SMEs may have their own tacit knowledge and memory of your organization’s content to say what’s good or bad when it comes to your project results. What if the machine uncovers better content for search results that everyone may have forgotten about? And remember, missing results are not necessarily bad because they can help identify the content or data your organization is currently missing.
  • Defining KPIs or ROI for AI projects is an iterative process: It is important to create the ability to ensure the right solution is being developed and is effective. The definition of the use case, acceptance criteria, and gold standard typically serve as introductory benchmarks to determine how to measure impact of the solution and overall success and return. However, as more training data is added, the model is continually updated and can change significantly over time. Thus, it is important to understand that the initial KPIs will usually have assumptions that are validated and updated as the solutions are incrementally developed and tested. It is also critical to have baseline data in order to successfully compare outcomes with ML/AI and without. Because setting KPIs is a journey, it really boils down to planning for and setting up the right governance and monitoring processes to support continuous re-training of the model and measure impact frequently. 

3. Plan for ancillary (potentially hidden) costs

This is one of the primary areas where AI projects encounter a reality check. If not planned for, these hidden costs can take many forms and cause significant delays or completely stall projects. The following items are some of the most common items to consider when planning to scale AI efforts:  

• Size and quality of the right data: AI and ML models learn from lots of training data. The larger the dataset, the better the AI model and results will perform. This required size of data introduces challenges including the need to aggregate and merge data from multiple sources with different security constraints, diverse formats (structured, unstructured, video files, text, images, etc.). This affects where and how your data and AI projects teams spend most of their time i.e., preparing data for analysis as opposed to building models and developing results. One of the most helpful ways to make such datasets easier to manage is to enhance them with rich, descriptive metadata (see next item) and a data knowledge graph. 
• Data preparation and labeling (taxonomies / metadata): Most organizations do not have labeled data readily available for effective execution of ML and AI projects. If not planned for or staffed properly, the majority of your resources will be spent in annotating or labeling training data. Because this step requires domain knowledge and the use of standards and best practices in knowledge organization systems, organizations will have to invest in formal and standardized semantic experts and hybrid automation in order to maintain quality and consistency data across sources.
• Licenses and tools: The most common misconceptions for Enterprise AI implementations and why many AI projects fail starts with the assumption that AI is a “Single Technology” solution. Organizations looking to “plug-and-play AI” or who want to experiment with a variety of open source tools need to reset their expectations and plan for the requirements and actual cost using these tools as costs can add up quickly. AI solutions range from data management and orchestration capabilities to employing a solution for metadata storage, and depending on the use case, the ability to push ML model results to upstream or downstream applications. 
• Project team expertise (or lack thereof): Experienced data scientists are required to effectively handle most of the machine learning and AI projects, especially when it comes to defining the success criteria, final delivery, scale, and continuous improvement of the model. Overlooking this foundational need could result in even more costly outcomes, or wasted efforts after producing misleading results or results that aren’t actionable or insightful.

Closing

The approach to enable rapid delivery of AI and its adoption continue to evolve. However, the challenges with scale still remain attributed to many factors including selecting the right project management and delivery framework, acquiring the right solutions, instituting the foundational data management and governance practices, and finding, hiring, and retaining people with the right skill sets. And ultimately, enterprise leaders need to understand how AI and Machine Learning work and what AI really delivers for the organization. The good news is that if built with the right foundations, a given AI solution can be reusable for multiple use cases, connect diverse data sources, cross organizational silos, and continue to deliver on the hype. 

How’s your organization tracking? Find out if your organization has the right foundations to take AI to production or email us to learn more about our experience and how we can help.

The post AI Beyond a Prototype appeared first on Enterprise Knowledge.

The webinar, moderated by Marydee Ojala, Conference Program Director at Information Today, will cover practical use cases for knowledge graphs, machine learning, and Enterprise Artificial Intelligence and will offer the critical steps to leverage these concepts and technologies in practice.

Participants in the webinar will learn:

• What knowledge graphs are, and what solutions they can provide for your organization.
• What the building blocks are, from both a design and technology perspective, that will allow your organization to succeed with knowledge graphs.
• How to get started with knowledge graphs and machine learning to achieve Enterprise AI.
• Real world examples and case studies of how organizations are obtaining fast insights using knowledge graphs and machine learning.

The post EK Presenting in KMWorld Webinar on Knowledge Graphs and Machine Learning appeared first on Enterprise Knowledge.

Inc. magazine today announced that Enterprise Knowledge, a global knowledge and information management consulting firm serving the public and private sectors, is ranked at number 2,574 on its annual Inc. 5000 list, the most prestigious ranking of the fastest-growing private companies in the United States. The list represents a unique look at the most successful companies within the American economy’s most dynamic segment—its independent businesses. Intuit, Zappos, Under Armour, Microsoft, Patagonia, and many other well-known names gained their first national exposure as honorees on the Inc. 5000.

This is the third year in a row that EK has achieved a place on the Inc. 5000. Of the tens of thousands of companies that have applied to the Inc. 5000 over the years, only a fraction have made the list more than once. Less than 10% of companies have ever made the list three times.

“EK’s inclusion on the Inc. 5000 for the third year in a row serves as a clear indicator of our company’s stability and strength. This incredible trend is thanks to the work of every member of the EK team, fueling not just our growth, but also our unique culture of collaboration and kindness.” said Zach Wahl, CEO of EK. “Our growth means more opportunity for the team and deeper capability to serve our clients at the leading edge of Knowledge Management, Advanced Search, and Enterprise Artificial Intelligence.” 

Joe Hilger, EK COO added, “There is little this team of exceptional people can’t accomplish. Their passion, collaboration, and hard work is the secret to our success.”

In addition to inclusion on the Inc. 5000, EK has received broad recognition not just for sustained growth, but also for being a great place to work, including by Inc. Magazine, Virginia Chamber of Commerce Fantastic 50, Washington Business Journal Best Places to Work, KMWorld AI50, and KMWorld 100 Companies that Matter in Knowledge Management.

Not only have the companies on the 2020 Inc. 5000 been very competitive within their markets, but the list as a whole shows staggering growth compared with prior lists as well. The 2020 Inc. 5000 achieved an incredible three-year average growth of over 500 percent, and a median rate of 165 percent. The Inc. 5000’s aggregate revenue was $209 billion in 2019, accounting for over 1 million jobs over the past three years.  

Complete results of the Inc. 5000, including company profiles and an interactive database that can be sorted by industry, region, and other criteria, can be found here. “The companies on this year’s Inc. 5000 come from nearly every realm of business,” says Inc. Editor-in-Chief Scott Omelianuk. “From health and software to media and hospitality, the 2020 list proves that no matter the sector, incredible growth is based on the foundations of tenacity and opportunism.”

About Enterprise Knowledge

Enterprise Knowledge (EK) is a services firm that integrates Knowledge Management, Information Management, Information Technology, and Agile Approaches to deliver comprehensive solutions. Our mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs.

Our core services include strategy, design, and development of Knowledge and Information Management systems, with proven approaches for Taxonomy and Ontology Design, Project Strategy and Road Mapping, Brand and Content Strategy, Change Management and Communication, and Agile Transformation and Facilitation. At the heart of these services, we always focus on working alongside our clients to understand their needs, ensuring we can provide practical and achievable solutions on an iterative, ongoing basis.

More about Inc. and the Inc. 5000

Methodology

The 2020 Inc. 5000 is ranked according to percentage revenue growth when comparing 2016 and 2019. To qualify, companies must have been founded and generating revenue by March 31, 2016. They had to be U.S.-based, privately held, for profit, and independent—not subsidiaries or divisions of other companies—as of December 31, 2019. (Since then, a number of companies on the list have gone public or been acquired.) The minimum revenue required for 2016 is $100,000; the minimum for 2019 is $2 million. As always, Inc. reserves the right to decline applicants for subjective reasons. Companies on the Inc. 5000 are featured in Inc.’s September issue. They represent the top tier of the Inc. 5000.

About Inc. Media

The world’s most trusted business-media brand, Inc. offers entrepreneurs the knowledge, tools, connections, and community to build great companies. Its award-winning multiplatform content reaches more than 50 million people each month across a variety of channels including websites, newsletters, social media, podcasts, and print. Its prestigious Inc. 5000 list, produced every year since 1982, analyzes company data to recognize the fastest-growing privately held businesses in the United States. The global recognition that comes with inclusion in the 5000 gives the founders of the best businesses an opportunity to engage with an exclusive community of their peers, and the credibility that helps them drive sales and recruit talent. The associated Inc. 5000 Conference is part of a highly acclaimed portfolio of bespoke events produced by Inc. For more information, visit their website.

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Enterprise Knowledge (EK) has been listed on KMWorld’s inaugural list of leaders in Artificial Intelligence, the AI 50: The Companies Empowering Intelligent Knowledge Management. KMWorld developed the list to help shine a light on innovative knowledge management vendors that are incorporating AI and cognitive computing technologies into their offerings.

As a services provider and thought leader in Enterprise AI, Knowledge Management, and Semantic Search, EK is one of the few dedicated services organizations included on the list. EK was uniquely recognized for our leadership in this area, including our AI Readiness Benchmark and range of functional demos that harness knowledge graphs, natural language processing, ontologies, and machine learning tools.

“As the drive for digital transformation becomes an imperative for companies seeking to compete and succeed in all industry sectors, intelligent tools and services are being leveraged to enable speed, insight, and accuracy,” said Tom Hogan, Group Publisher at KMWorld.  “To showcase organizations that are incorporating AI and an assortment of related technolo­gies—including natural language processing, machine learn­ing, and computer vision—into their offerings, KMWorld created the “AI 50: The Companies Empowering Intelligent Knowledge Management.”

Lulit Tesfaye, EK’s Practice Leader for Data and Information Management stated, “We are thrilled for this recognition and extremely proud of the cutting edge solutions we’re able to deliver for organizations looking to optimize their data and Knowledge AI initiatives. This recognition demonstrates EK’s ability to leverage our real-world experience and define the enterprise success factors for maturity and readiness for AI, bringing the focus back to business values, and the tangible applications of AI for the enterprise. Allowing organizations to go past the common AI limitations is what helps us show where we are leading.”

EK CEO Zach Wahl added, “Thanks to KMWorld for this recognition and congratulations to my amazing colleagues for their thought leadership. Alongside our recognition as one of the top 100 Companies That Matter in Knowledge Management for the sixth year in a row, this demonstrates EK’s leadership position at the nexus of KM and AI.”

About Enterprise Knowledge

Enterprise Knowledge (EK) is a services firm that integrates Knowledge Management, Information Management, Information Technology, and Agile Approaches to deliver comprehensive solutions. Our mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs.

About KMWorld

KMWorld is the leading information provider serving the Knowledge Management systems market and covers the latest in Content, Document and Knowledge Management, informing more than 21,000 subscribers about the components and processes – and subsequent success stories – that together offer solutions for improving business performance.

KMWorld is a publishing unit of Information Today, Inc. 

The post EK Listed on KMWorld’s AI 50 Leading Companies appeared first on Enterprise Knowledge.

With our focus and expertise in knowledge, data, and information management, Enterprise Knowledge (EK) developed this proprietary Enterprise Artificial Intelligence (AI) Readiness Assessment in order to enable organizations to understand where they are and where they need to be in order to begin leveraging today’s technologies and AI capabilities for knowledge and data management. 

Based on our experience conducting strategic assessments as well as designing and implementing Enterprise AI solutions, we have identified four key factors as the most common indicators and foundations for many organizations in order to evaluate their current capabilities and understand what it takes to invest in advanced capabilities. 

This assessment leverages over thirty measurements across these four Enterprise AI Maturity factors as categorized under the following aspects. 

1. Organizational Readiness

The foundational requirement for any organization to undergo an Enterprise AI transformation stems from alignment on vision and the business applications and justifications for launching successful initiatives. The Organizational Readiness Factor includes the assessment of appropriate organizational designs, leadership willingness, and mandates that are necessary for success. This factor evaluates topics including:

• The need for vision and strategy for AI and its clear application across the organization.
• If AI is a strategic priority with leadership support.
• If the scope of AI is clearly defined with measurable success criteria.
• If there is a sense of urgency to implement AI.

With a clear picture of what your organizational needs are, your Organizational Readiness assessment factor will allow you to determine if your organization meets the requirements to consider AI related initiatives while surfacing and preparing you for potential risks to better mitigate failure.

2. The State of Organizational Data and Content

The volume and dynamism of data and content (structured and/or unstructured) is growing exponentially, and organizations need to be able to securely manage and integrate that information. Enterprise AI requires quality of, and access to, this information. This assessment factor focuses on the extent to which existing structured and unstructured data is in a machine consumable format and the level to which it supports business operations within the enterprise. This factor consider topics including:

• The extent to which the organization’s information ecosystems allow for quick access to data from multiple sources.
• The scope of organizational content that is structured and in a machine-readable format.
• The state of standardized organization of content/data such as business taxonomy and metadata schemes and if it is accurately applied to content.
• The existence of metadata for unstructured content. 
• Access considerations including compliance or technical barriers.

AI needs to learn the human way of thinking and how an organization operates in order to provide the right solutions. Understanding the full state of your current data and content will enable you to focus on the right content/data with the highest business impact and help you develop a strategy to get your data in an organized and accessible format. Without high quality, well organized and tagged data, AI applications will not deliver high-value results for your organization.

3. Skills Sets and Technical Capabilities

With the increased focus on AI, the demand for individuals who have the technical skills to engineer advanced machine learning and intelligent solutions, as well as business knowledge experts who can transform data to a paradigm that aligns with how users and customers communicate knowledge, have both increased. Further, over the years, cloud computing capabilities, web standards, open source training models, and linked open data for a number of industries have emerged to help organizations craft customized Enterprise AI solutions for their business. This means an organization that is looking to start leveraging AI for their business no longer has to start from scratch. This assessment factor evaluates the organization’s existing capabilities to design, management, operate, and maintain an Enterprise AI Solution. Some of the factors we consider include:

• The state of existing enterprise ontology solutions and enterprise knowledge graph capabilities that optimize information aggregation and governance. 
• The existence of auto-classification and automation tools within the organization.
• Whether roles and skill sets for advanced data modeling or knowledge engineering are present within the organization.
• The availability and capacity to commit business and technical SMEs for AI efforts.

Understanding the current gaps and weaknesses in existing capabilities and defining your targets are crucial elements to developing a practical AI Roadmap. This factor also plays a foundational role in giving your organization the key considerations to ensure AI efforts kick off on the right track, such as leveraging web standards that enable interoperability, and starting with available existing/open-source semantic models and ecosystems to avoid short-term delays while establishing long-term governance and strategy. 

4. Change Threshold 

The success of Enterprise AI relies heavily on the adoption of new technologies and ways of doing business. Organizations who fail to succeed with AI often struggle to understand the full scope of the change that AI will bring to their business and organizational norms. This usually manifests itself in the form of fear (either of change in job roles or creating wrong or unethical AI results that expose the organization to higher risks). Most organizations also struggle with the understanding that AI requires a few iterations to get it “right”. As such, this assessment factor focuses on the organization’s appetite, willingness, and threshold to understand and tackle the cultural, technical, and business challenges in order to achieve the full benefits of AI. This factor evaluates topics including:

• Business and IT interest and desire for AI.
• Existence of resource planning for the individuals whose roles will be impacted. 
• Education and clear communication to facilitate adoption. 

The success of any technical solution is highly dependent on the human and culture factor in an organization and each organization has a threshold for dealing with change. Understanding and planning for this factor will enable your organization to integrate change management that addresses the negative implications, avoids unnecessary resistance or weak AI results, and provides the proper navigation through issues that arise.

How it Works

This Enterprise AI readiness assessment and benchmarking leverages the four factors that have over 30 different points upon which each organization can be evaluated and scored. We apply this proprietary maturity model to help assess your Enterprise AI readiness and clearly define success criteria for your target AI initiatives. Our steps include: 

• Knowledge Gathering and Current State Assessment: We leverage a hybrid model that includes interviews and focus groups, supported by content/data and technology analysis to understand where you are and where you need to be.This gives us a complete understanding of your current strengths and weaknesses across the four factors, allowing us to provide the right recommendations and guidance to drive success, business value, and long-term adoption.
• Strategy Development and Roadmapping: Building on the established focus on the assessment factors, we work with you to develop a strategy and roadmap that outlines the necessary work streams and activities needed to achieve your AI goals. It combines our understanding of your organization with proven best practices and methodologies into an iterative work plan that ensures you can achieve the target state while quickly and consistently showing interim business value.
• Business Case Development and Alignment Support: we further compile our assessment of potential project ROI based on increased revenues, cost avoidance, risk and compliance management. We then balance those against the perceived business needs and wants by determining the areas that would have the biggest business impact with lowest costs. We further focus our discussions and explorations on these areas with the greatest need and higher interest.

Keys to Our Assessment  

Over the past several years, we have worked with diverse organizations to enable them to strategize, design, pilot, and implement scaled Enterprise AI solutions. What makes our priority assessment unique is that it is developed based on years of real-world experience supporting organizations in their knowledge and data management. As such, our assessment offers the following key differentiators and values for the enterprise: 

• Recognition of Unique Organizational Factors: This assessment recognizes that no Enterprise AI initiative is exactly the same. It is designed in such a way that it recognizes the unique aspects of every organization, including priorities and challenges to then help develop a tailored strategy to address those unique needs.
• Emphasis on Business Outcomes: Successful AI efforts result in tangible business applications and outcomes. Every assessment factor is tied to specific business outcomes with corresponding steps on how the organization can use it to better achieve practical business impact.
• A Tangible Communication and Education Tool: Because this assessment provides measurable scores and over 30 tangible criteria for assessment and success factors, it serves as an effective tool to allow your organization to communicate up to leadership and quickly garner leadership buy-in, helping organizations understand the cost and the tangible value for AI efforts. 

Results

As a result of this effort, you will have a complete view of your AI readiness, gaps and required ecosystem and an accompanying understanding of the potential business value that could be realized once the target state is achieved. Taken as a whole, the assessment allows an organization to:

• Understand strengths and weaknesses, and overall readiness to move forward with Enterprise AI compared to other organizations and the industry as a whole;
• Judge where foundational gaps may exist in the organization in order to improve Enterprise AI readiness and likelihood of success; and
• Identify and prioritize next steps in order to make immediate progress based on the organization’s current state and defined goals for AI and Machine Learning.

The post Enterprise AI Readiness Assessment appeared first on Enterprise Knowledge.

1. The Need for Business Application and Enterprise Use Cases

AI is no longer an experience, it is a business requirement that increasingly requires clear justification and tangible business values with return on investment (ROI) in order to receive funding and organizational support. Clear understanding of how advanced AI capabilities can impact business or help address relevant use cases is becoming the cornerstone for the success of Enterprise AI implementation and organizational alignment.

2. A Narrowing Gap Between Structured and Unstructured Data/Information

In the past, the academic debate on how we manage data vs. information vs. knowledge has been a central driver in determining  how we practice these disciplines. Now, semantic solutions allow organizations to structure previously unstructured information by capturing meaningful relationships between data entities in a scalable and efficient manner. This makes it possible to train machines to interpret data in a human-centered way, powering knowledge and data discovery. 

3. Advanced Data Management and Governance Approaches

There are more resources being dedicated toward Enterprise AI efforts to derive value from existing content and data, providing a better alternative to purchasing nebulous “big data” storages to glean a comprehensive picture of the data within the enterprise. By employing enterprise knowledge graphs, automated categorization, or a recommendation system, organizations can now achieve optimized access, natural language search, and use and re-use their information assets without a costly and burdensome migration. 

4. Open Source Solutions and Computing Capabilities

Over the years, web standards, open source training models, and linked open data for a number of industries, such as open government data (OGD) or open science data, have emerged to help organizations craft customized Enterprise AI solutions for their business. This means an organization that is looking to start leveraging AI for their business no longer has to start from scratch. Further, Cloud computing capabilities have also progressed in a way that makes implementing these solutions a pragmatic and cost effective reality.

5. Expanded Roles and Skill Sets

Recently, there has been an increasing demand for individuals who have the technical skills to engineer advanced machine learning and intelligent solutions, as well as business knowledge experts who can transform data to a paradigm that aligns with how users and customers communicate knowledge. This is becoming achievable with growing skill sets in data science, knowledge engineering, and semantic architecture that facilitate unprecedented collaboration between knowledge and data across an organization.

6. The Ability to Start Small and  Scale Incrementally

The most successful Enterprise AI initiatives are not attempting to incorporate every data set, every business unit, and every enterprise system. Organizations are more often building a rapid prototype or pilot, prioritizing a backlog of enhancements, and scaling iteratively. 

These trends are key indicators that AI for the enterprise is no longer a hype, rather, it is becoming a necessity with feasible environmental and infrastructure support – now more than ever. If you are interested in how to benefit from your knowledge and data to accelerate enterprise AI capabilities, email us.  

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