What is AI Enablement?

AI enablement is the process of equipping organizations with the right technology, infrastructure, and connectivity to leverage AI effectively across their business processes. This concept goes beyond simply implementing a plug-and-play AI solution. Instead, it focuses on creating an ecosystem where AI deployments can seamlessly connect with native applications, knowledge sources, and workflows to deliver a holistic experience that drives tangible AI outcomes.

What is an AI enablement platform?

An AI enablement platform allows an organization to fully realize the benefits of the latest technology by taking an “AI-first mindset,” which means organizations choose their own AI model and search engine, and the AI enablement platform acts as a keystone to span gaps between enterprise knowledge and AI technology. Ultimately this means that organizations can scale their AI investment faster while ensuring that AI solutions are deployed, connective, scalable, secure, and significant to the organization’s top and bottom lines.

The shift: Addressing AI project failures

There’s a growing understanding that many AI projects are failing to deliver—in fact, up to 90% of AI projects reportedly fail to make it out of the planning stage. Research from industry leaders, including Gartner, Everest Research Group, Forrester, and Harvard Business School highlights that most enterprises simply lack the infrastructure and sometimes the internal expertise to deploy AI projects effectively. Yet many organizations are attempting to do so anyway.

These overarching AI project failures mostly arise due to underestimating the effort required to properly prepare, access, and expose knowledge between the AI engine and existing business workflows. Today’s AI enterprise search solutions focus heavily on the first step in AI enablement: connectivity and access to knowledge. However, most current AI enterprise search solutions fail to address mission-critical functionality, such as preparing knowledge for ingestion or the complexities of exposing knowledge to an individual user.

Let’s break down these three steps further:

1. Prepare

Preparing your knowledge is an important first step that is often overlooked. To make the most of your information, you need more advanced data preparation methods such as data chunking, vectorization, and semantic analysis to connect relationships between terms. Data enrichment and augmentation powered by machine learning (ML) and natural language processing (NLP) allow for classification, so data can be fed into any AI model.

2.  Access

There are many factors to consider when configuring access to knowledge. If only a fraction of organizational knowledge is accessible for the AI to use, the effectiveness of any AI solution will drop. Organizations must ensure that knowledge can be pulled from various sources and applications, while simultaneously providing secure, role-specific access to that knowledge at an end-user level.

3. Expose

Finally, your knowledge needs to be properly exposed. Who can access what data and for what purposes? Are there customer contracts in place that detail how knowledge is to be handled? With permissions and accessibility still top of mind, the right technology employs APIs and embedded applications to seamlessly connect systems and users with the knowledge they need to get work done.

For organizations looking to address these challenges, now is the time to seek out an AI e. By integrating features such as graph connectors, robust security protocols, and granular data enrichment, an AI enablement platform serves as a foundational solution for deploying Generative AI projects throughout an enterprise.

The need for AI enablement: Three reasons AI projects fail

1. Poor data quality

Data quality is a significant challenge for enterprises aiming to deploy valuable AI projects. If knowledge is the lifeblood of AI, mediocre inputs dramatically hinder your organization’s AI performance. Through augmentation, enrichment, and metadata tagging strategies, enterprises can standardize taxonomy structures to vastly improve the quality of their AI solutions’ inputs and outputs.

To avoid hallucinations and inaccurate answers, you need to effectively classify and prepare your knowledge to be fed into the model of your choice. Enriching organizational knowledge adds critical information and relevant context to the raw data. Augmentation strategies include modifying the data for even further context for AI, which prevents the model from overfitting your information and helps understand the data more comprehensively.

An AI enablement platform should employ augmentation, enrichment, and tagging strategies to adequately prepare your knowledge for AI readiness. This mitigates the risk of your AI project failing from poor-quality information being fed into the model.

2. Incomplete connectivity

One of the most common reasons for AI project failures is that organizations struggle to connect their AI projects to various business applications. The unfortunate result is knowledge silos, which prevent AI from achieving its full potential. If AI is only integrated into a few applications, it is running on only partial knowledge and lacks the contextual information needed to generate impactful insights or perform tasks effectively.

Graph connectors designed specifically to tackle this issue. By facilitating connectivity across enterprise systems such as JIRA, Confluence, Google Drive, iManage, NetDocuments, OpenText, and many more, AI enablement platforms deliver a comprehensive view of the business to the AI engine.

3. Missing guardrails and security

Another critical factor contributing to AI project failure is the absence of security measures and frameworks. Because this emerging technology is evolving so quickly, many organizations may feel rushed to jump into AI projects without establishing proper guidelines for data security and accessibility. Yet security is a top concern for executives and is frequently cited in surveys as a robust roadblock to successful AI implementation. AI systems often handle sensitive data, and without proper security protocols, sensitive data could be inadvertently exposed through your AI engine, creating a loss of confidence from employees, prospects, or customers.

An AI enablement platform anticipates these challenges by introducing enhanced security measures when integrating enterprise data directly with the AI engine. This includes security trimming, retaining data integrity, and maintaining accessibility controls throughout deployment. Seek out an AI enablement platform supported by knowledge experts to help your team identify core risks and limitations from the outset. The platform embeds enterprise-grade security protocols to protect your data while ensuring the right people have access to the knowledge they need.

Why should I invest in an AI enablement platform?

The transition to an AI platform signifies a broader industry shift from basic AI capabilities to more sophisticated enterprise-wide initiatives. By leveraging an AI enablement platform, organizations can take advantage of core use cases such as search, augmentation, discovery, generation, and delivery. This empowers enterprises to:

Maximize the value of AI investments

Rather than deploying isolated, disconnected, and low-quality AI projects, organizations can create an interconnected ecosystem where AI integrates into existing business processes. This holistic approach unlocks greater value and drives productivity throughout the organization.

Deploy AI efficiently

As organizations transition into a landscape increasingly dominated by Generative AI, the importance of robust connectivity and support cannot be overstated. Partnering with experts experienced in navigating AI challenges can help organizations identify core risk points, offer flexible technology implementations, and scale effectively with business growth.

Supercharge productivity

Equipping AI with a 360-degree view of enterprise knowledge allows employees to complete their daily tasks with the power of full context, eliminating information silos. This not only enhances productivity but also increases the overall effectiveness and value of AI initiatives.

By preparing knowledge through intelligent metadata tagging, classification, and additional powerful data augmentation methods and then providing universal access to that knowledge using graph connectors, enterprises can transform disjointed AI projects into cohesive, competitive solutions that enhance productivity and innovation. This is the promise of effectively enabling AI.

Additional insights on AI enablement

1. Real-world applications of AI enablement

Leveraging an AI enablement platform has far-reaching implications across various industries, and the applications and benefits continue to grow alongside our collective imaginations. Here are just a few use cases to bring this concept of an AI enablement platform to light:

• Legal: AI can enhance contract analysis, document review, and legal research by automatically extracting the most relevant content and identifying key clauses. AI enablement allows legal professionals to access matter-centric content and quickly analyze vast databases of legal precedents and case law, improving efficiency, reducing manual errors, and accelerating the decision-making process.
• Life Sciences / Pharmaceuticals: AI can accelerate time-consuming processes for employees, such as clinical trial optimization, literature review and research, or access to the appropriate training and documentation. Improving the efficiency of internal resources allows for everything from happier employees to more satisfied end customers.
• Manufacturing: AI can automatically generate required documentation for regulatory compliance and track changes in regulations to ensure that production operations adhere to safety standards. This helps workers reduce the time spent on compliance, minimize regulatory risk, and improve documentation quality.

2. Overcoming implementation challenges

While the benefits of an AI enablement platform are clear, organizations may still face challenges during implementation. The right technology partner will work with your team to ensure that all details are considered prior to integration, but your organization will still need to infuse some key internal strategies to support your staff, as well as your AI investment. Some strategies to consider include:

• Get your team on board: For your AI implementation strategy to take hold, your team needs to understand the current and future potential value of the technology. Get stakeholders excited by communicating the value of an AI enablement platform – and be specific. Giving your team clear examples of how AI can solve specific problems, save them time, accelerate project timelines, and help them work smarter can foster support for the initiative.
• Invest in ongoing training opportunities: To maximize the full potential of any AI investment, organizations need to provide ongoing training programs that equip employees with the skills needed to work alongside AI technologies. This reduces resistance, supports best practices, and ultimately builds confidence in AI capabilities.
• Keep tabs on your technology: Once AI solutions are deployed, the job isn’t done. This technology is changing and growing rapidly, which means that organizations should establish metrics to monitor performance continuously. This enables timely adjustments and ensures that AI remains aligned with business goals and can even offer technology swap outs if your team desires to use a different model or AI technology.

Wrap up

Investing in an AI enablement platform is not merely a technical decision; it is a strategic move toward future-proofing your organization in an increasingly AI-driven landscape. By addressing challenges related to connectivity, security, and data quality, businesses can harness the true power of AI, ensuring that their investments yield meaningful results.

As organizations work toward a future driven by AI, the focus must be on creating an ecosystem where AI not only integrates with existing systems but also drives transformation across all facets of the organization. The journey toward effective AI enablement begins with understanding and addressing the missing pieces of the AI puzzle, leading to a more connected, empowered, and productive workplace.