The Critical Role of Enterprise Data in Generative AI
A flood of Gen AI-based tools and applications, often acting as wrappers for LLMs like ChatGPT, has hit the market. While they offer clever and creative solutions, LLMs alone can't solve all organizational information problems. Machine learning, integral to AI applications, is now embedded in conventional enterprise tools like ERP, data warehouses, eCommerce, and content/knowledge management systems, enhancing their core functionalities. This integration promises new efficiencies and productivity across various scenarios.
Accelerating Data and Analytics Capabilities Age of Generative AI: How Governance is a Key Enabler
The underlying principles of Artificial Intelligence have been evolving over decades. Recent advances have created nothing short of a revolutionary breakthrough in information management. Generative AI is in the public consciousness and corporate applications are promising but require certain guardrails and decision-making policies and processes. While “governance“ is a term that brings to mind bureaucratic structures with little practical on-the-ground application, a correctly designed decision-making framework driven by business process/outcome measures and KPIs provides a critical component of data analytics and AI programs.
How to Successfully Test and Deploy a ChatGPT-Type of Application
Organizations are taking a cautious approach to Generative AI — the Large Language Model (LLM) powered ChatGPT-like applications that have burst onto the technology and consumer scene. Increasingly, the C-suite is trying to factor in how LLMs and Generative AI will be part of their digital transformation roadmaps.
What ChatGPT and Generative AI Mean for Digital Transformation
An arms race is raging in the AI industry — every organization is frantically investing in or investigating how the large language models (LLMs) that powers Generative AI can be applied to the enterprise. Generative AI will be on every organization's digital transformation roadmap. The astounding capabilities of ChatGPT have been grabbing all of the headlines, but the question now is, how can these powerful tools be used to optimize the customer or employee experience?
ChatGPT: Insightful, Articulate, Inconsistent, and Wrong. A Game Changer?
Digital assistants are taking a larger role in digital transformations. They can improve customer service, providing more convenient and efficient ways for customers to interact with the organization. They are available 24/7 and can personalize recommendations and content by taking into consideration role, preferences, interests, and behaviors.
All of these contribute to improved productivity and efficiency. Right now, bots are only valuable in very narrow use cases and are unable to handle complex tasks. However, the field is rapidly changing and advances in algorithms are having a very significant impact.
How to Navigate the 5 Stages of Organizational Maturity in Digital Transformation
In contemplating digital transformation program investments, executives ask two things: What is the current state costing us, and does it make economic sense to fix it?
One challenge is deciding where to begin tactically. A good starting point is to assess current capabilities in the context of organizational maturity and the desired future state. That exercise will help decision-makers determine whether their organization can get from where is to where it wants to be.
Removing Friction from Information Flows: Vital for a Successful Digital Transformation
A critical step in digital transformation is to enable the free flow of information throughout the enterprise. But various forms of friction can obstruct this flow. Friction is anything that slows down information access, information retrieval or information manipulation.
These sources of friction point directly to the conclusion that a solid information architecture and well-designed information management system, along with an overall vision for the digital transformation, are prerequisites for success.
Harvard Business Review: Is Your Data Infrastructure Ready for AI
Creating an ontology is an essential investment to prepare your enterprise to realize the benefits of AI and machine learning. Gone are the days when businesses should simply allow a number of small AI projects to blossom independently: for these projects to be competitive they need to draw on data from across the company, data stored in many different forms in many different systems. Businesses will be best positioned to build ontologies if they identify and research pain points first–areas where the data connections are most needed–before beginning to set the organizing principles for the ontology itself.
Knowledge Graphs, a Tool to Support Successful Digital Transformation Programs
Knowledge graphs are pretty hot these days. While this class of technology is getting a lot of market and vendor attention these days, it is not necessarily a new construct or approach. The core principles have been around for decades. Organizations are becoming more aware of the potential of knowledge graphs, but many digital leaders are puzzled as to how to take the next step and build business capabilities that leverage this technology.
The Coming Tsunami of Need — Knowledge Management for Artificial Intelligence
Knowledge management has had a bad rap. For the past few decades, it has gone through cycles of popularity after being introduced in the early 90s, and in some of those cycles, it has been significantly devalued. That is the online incarnation of KM.
Now knowledge management is experiencing something of a revival, as its value in enabling AI is being increasingly recognized.
Leveraging Data to Improve the Customer Experience
When you consider how customers interact with organizations these days, it quickly becomes apparent that much of that interaction is through digital channels. “CX” suggests a customer experience via laptops or mobile devices, and that digital experience is driven entirely by data. The question is, how do we make it the most relevant and seamless experience possible, given the needs and objectives of the user, and what data can we leverage to do so?
5 core principles for successful AI/human partnerships
AI works best when humans are in the loop. Knowledge communities can provide a robust flow of information that supports and continuously refreshes the content on which AI relies. When organizational processes are identified and documented, AI can take over routine tasks, leaving the creative and more challenging problem-solving tasks to be handled by humans. Behind the scenes, content and product models need to be developed and aligned with data capture processes to make AI components work, but humans must create the knowledge flow and take charge of the content.
There's No AI Without IA
Artificial intelligence (AI) is increasingly hyped by vendors of all shapes and sizes—from well-funded startups to the well-known software brands. Financial organizations are building AI-driven investment advisors. Chat bots provide everything from customer service to sales assistance. Although AI is receiving a lot of visibility, the fact that these technologies all require some element of knowledge engineering, information architecture, and high-quality data sources is not well known...
“Just Make it Work” – Dealing with Executive Disengagement During Large Scale Digital Transformations
Executives cannot make informed decisions without getting into the weeds about not just the nature and severity of the challenges but the business decisions that need to be made as part of any technology effort. The most important program parameters include expected outcomes, proof points to support the investment, a realistic plan, ongoing measures of success, and long-term program ownership and governance.
