Artificial Intelligence is upgrading the traditional IT world and the race between vendors to harness the power of AI-embedded technologies is at its peak. Every 2.5 days, a new generative AI (GenAI) frontier model is released. Gartner further predicts that most enterprise apps will witness a 40% cost boom due to GenAI by 2027. While the technology offers great promise and unparalleled opportunities to businesses, fragmented systems, decentralized data, and a general lack of sturdy governance add hiccups along the enterprise AI implementation journey.

Enter the “AI Tech Sandwich” approach—a conceptual framework resembling a sandwich, with each layer designed to integrate AI across organizations in a structured, scalable, and secure manner.

The essence of this model lies in its layered implementation. From embedding AI across departments to addressing trust and security risks, while establishing a centralized foundation, this approach provides a structured pathway to success. What follows is a deep dive into this three-layered approach to enterprise AI, detailing how to design your AI sandwich for maximum impact.

Furthermore, we will break down ideas shared in Gartner’s 2024 IT Symposium Keynote on “Pacing Yourself in the AI Races”, for building secure, scalable enterprise AI systems, regardless of the implementation pace or organizational size.

Top layer: AI from everywhere

The top layer is characterized by AI that’s embedded, built, or brought in from external sources. It ensures agility and adaptability in AI deployment by allowing teams to customize and integrate AI solutions. However, decentralized AI requires careful governance to address issues like data security, consistency, and alignment with business goals.

Key components

• Embedded AI
  Over 80% of independent software vendors are expected to embed AI into their applications by 2026. It directly translates into multiplied operational efficiency with zero hindrance to the established workflows. By embedding AI into its customer relationship management (CRM) platform, any company can enable its agents to access real-time insights, enhancing their decision-making capabilities.
• Department-Specific Bring Your Own AI (BYOAI)
  The tailored solutions enable departments and teams to address specific pain points, such as lead scoring based on behavioral data for sales or employee engagement for HR, while contributing to the overall business objectives.
   
  However, the adoption of such tools can introduce behavioral challenges stemming from jealousy to emotional attachments. For example,
• Trust Issues: Only 39% of employees believe AI produces fair outcomes, underscoring the need for transparency in AI processes.
• Overdependence: Teams may rely too heavily on AI tools, potentially undermining critical thinking and problem-solving skills.
   
  By fostering open communication and providing training on AI’s role and limitations, organizations can build trust and ensure that these tools augment, rather than replace, human expertise.
• Managing decentralized AI capabilities
  Decentralization ensures organizational flexibility, but it also requires oversight to maintain uniformity, trust, and compliance. Implementing clear guidelines and providing governance mechanisms can ensure that AI initiatives align with enterprise-wide objectives.
   
  Real-world example:
  Amazon Web Services (AWS) expanded its AI offerings by investing in AI startups and developing in-house AI chips like Trainium, providing diverse models and cost-effective solutions. These initiatives showcase AWS’s strategy of integrating AI capabilities at multiple organizational levels.

The critical middle layer: TRiSM

The TRiSM (Trust, Risk, and Security Management) layer is the governance engine of the AI Tech Sandwich. It ensures that AI systems operate securely, ethically, and reliably, addressing concerns like bias, compliance, and data security.

From committees to guardian agents

In organizations managing fewer than 10 AI initiatives, manual governance committees can oversee policies effectively. However, as AI initiatives scale beyond this threshold, manual oversight becomes insufficient.

This is why we need guardian agents—AI-based tools to ensure governance. These agents act as enforcers of policies with real-time monitoring and automation, ensuring AI systems adhere to predefined rules and ethical standards.

To illustrate, at the Pacific Northwest National Laboratory, an AI guardian agent reviews research outputs, checking footnotes for accuracy, compliance, and ethical appropriateness, enabling the organization to maintain high standards in AI-driven research at scale​.

Frameworks vs. technologies

While governance committees are essential for establishing policies, TRiSM technologies operationalize these principles, making them indispensable for enterprises navigating the complexities of modern AI deployment. Guardian agents, bias detection systems, and ethical AI monitors provide the scalability and precision required for large-scale AI governance.

Take the case of a healthcare provider using AI for diagnostics. By integrating TRiSM technologies to maintain compliance with HIPAA, protect patient data, and detect biases in medical recommendations, these tools will ensure their AI outputs remain reliable, ethical, and aligned with organizational goals.

The foundation layer: Centralized AI infrastructure

Beneath the visible layers lies the foundation, centralized AI infrastructure. This layer serves as the backbone of enterprise AI, providing the essential infrastructure required for seamless operations and scalable growth.

Elements of centralized AI infrastructure

• Core data and AI capabilities
  Centralized infrastructures unify structured and unstructured data, enabling enterprises to extract insights from diverse sources efficiently. Notably, Gartner in its IT Symposium reports that 70–90% of enterprise data is unstructured, encompassing emails, images, customer feedback, and social media interactions. Managing this vast data pool presents both opportunities and challenges.
   
  While structured data fits neatly into databases, unstructured data demands advanced AI techniques like natural language processing (NLP) and computer vision for interpretation. Organizations must balance the scalability of managing structured data with the sophistication required to handle unstructured sources effectively.

   
  Modern organizations are transitioning from traditional data aggregation and cleaning methods to deploying AI tools that analyze data in its native format and location. This approach reduces processing time and costs while improving the accuracy of insights. AI models now interpret behavioral data—tracking customer interactions, preferences, and purchasing patterns across multiple channels—without requiring extensive preprocessing.

   
  For example, McKinsey highlights the role of behavioral data in driving hyper-personalization. By analyzing real-time customer journeys, businesses can segment users into micro-groups based on habits and preferences. This level of granularity powers predictive models that anticipate needs, recommend products, and optimize user experiences.

• Role of IT and Data Analytics teams
  The success of this layer relies heavily on IT and Data Analytics teams. Their responsibilities extend beyond managing data pipelines to implementing privacy-first frameworks and ensuring ethical AI practices. For instance, in e-commerce, these teams design infrastructures capable of processing millions of data points in real time to deliver recommendations, dynamic pricing, and fraud detection. Their expertise ensures interoperability across systems while safeguarding data integrity and compliance with regulations.
• Building vs. buying AI capabilities
  Enterprises often face a build-or-buy dilemma. While building ensures customization, buying offers faster deployment and lower upfront costs. Balancing these aspects based on organizational needs is critical.
• Infrastructure considerations
  Factors like data storage, computing power, and cloud scalability are critical. A robust foundation minimizes redundancies and simplifies the deployment of department-specific AI tools.

Real-world example:

A major automotive OEM centralized its AI and data infrastructure, enabling rapid deployment of AI algorithms in a decentralized manner. Similarly, Uber, Netflix, and Google use AI factories to streamline data pipelines, algorithm development, and experimentation platforms.

Choosing your sandwich type

The AI Tech Sandwich is not a one-size-fits-all model. Enterprises can select the sandwich type that best aligns with their objectives, resources, and risk tolerance. Tailoring the sandwich type ensures that AI deployments are efficient, secure, and scalable.

The three sandwich types

1. Vendor-packaged approach
Suitable for small to mid-sized enterprises, this type leverages vendor-provided AI capabilities with minimal customization. It includes embedded AI and a thin TRiSM layer for basic governance. This approach is best for organizations with limited in-house AI expertise or resources.

2. TRiSM-rich model
This sandwich prioritizes a robust TRiSM layer for governance and compliance, with centralized data and limited BYOAI. It is ideal for industries and public-sector organizations like healthcare or finance that require strict compliance with regulations such as GDPR or HIPAA.

3. Deluxe implementation
Designed for large enterprises with significant AI ambitions, this sandwich incorporates extensive BYOAI, in-house development, and comprehensive TRiSM technologies. It’s best for organizations that view AI as a competitive differentiator and have the resources to invest in complex systems.

Implementation strategy and best practices

Deploying an AI Tech Sandwich requires a strategic approach that aligns with technology, governance, and business goals. Enterprises must carefully plan and execute their AI strategies while addressing potential challenges.

Organizations can adopt one of two implementation paces depending on their needs.

• AI-steady pace enables careful adoption through pilot projects, ideal for regulated or transformation-cautious industries.
• AI-accelerated pace prioritizes swift scaling for agile sectors like e-commerce and healthcare, addressing competitive demands.

When choosing an implementation pace, organizations should consider resource availability, industry context, and strategic goals. Moreover, by adhering to a 24-hour Service Level Agreement (SLA), AI enterprises can respond within a single business day, maintaining operational efficiency and customer satisfaction.

In addition, effective AI adoption demands strategic budgeting, robust change management through employee engagement and training, and clear KPIs like productivity, cost savings, and customer satisfaction to achieve success.

Key outcomes

Gartner identifies three critical outcome areas of enterprise AI initiatives:

1. Business: Improved productivity and cost reductions

2. Technology: Robust governance and scalable and secure infrastructure

3. Behavioral: Positive employee and customer interactions with AI

Accelerate your AI journey with HTC

As organizations navigate their AI transformation journey, HTC offers specialized expertise in delivering Enterprise AI solutions that drive measurable outcomes. Through its comprehensive suite of services in data engineering, analytics, AI/ML solutions, and industry-specific solutions across retail, healthcare, insurance, travel, and other sectors, HTC helps organizations implement their chosen AI sandwich strategy. Our three-staged approach – Learn, Scale, and Transform – aligns perfectly with organizations’ AI implementation pace, whether starting with a vendor-packaged approach or advancing to a deluxe implementation. We combine deep domain expertise with disruptive technologies to help enterprises test AI viability, scale critical functions with purpose-built applications, and ultimately transition to an intelligent ecosystem that prepares them for an AI-powered future.