With approximately 77% (AuthorityHacker, 2024) of companies already integrating AI in some capacity, the rapid adoption signals both confidence in its potential and the competitive pressure to keep up. But does widespread adoption necessarily equate to meaningful value?

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AI-driven virtual assistants (VAs) are among the most transformative applications, though their impact varies greatly depending on implementation. While Forbes (2024) reports that nearly 47% of AI usage is in personal assistants, the question remains: are businesses leveraging VAs as true strategic assets, or are they merely deploying them as cost-cutting measures?

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Beyond simple automation, AI-driven chatbots promise seamless, 24/7 customer engagement and operational efficiency. However, not all implementations deliver on this promise - poorly designed VAs can frustrate users, create bottlenecks and even erode trust (PwC, 2023). As businesses increasingly rely on AI for front-line interactions, understanding how to build, evaluate and mitigate the risks of virtual assistants becomes critical.

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What is the difference between virtual assistants and chatbots?

While the terms “virtual assistants” and “chatbots” are often used interchangeably, they serve distinct functions and operate at different levels of sophistication.

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Virtual assistants leverage advanced AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), as well as Large Language Models (LLMs) to interpret, contextualise and respond to user inputs dynamically. These assistants can handle open-ended conversations, learn from interactions and personalise responses over time. Examples include AI-powered systems like Siri, Google Assistant and enterprise-grade assistants that integrate with business applications (IBM, 2023) to provide real-time support, process automation and even decision-making assistance like Ntegra’s Optimised Response Assistant (Nora). Read the full case study on LinkedIn.

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On the other hand, chatbots are generally more rigid and rule-based. They rely on pre-programmed scripts and decision trees to guide conversations, meaning they can only operate within a predefined set of responses and limit the paths a user can travel. While useful for simple tasks such as answering FAQs, routing users to appropriate resources, or handling structured customer service inquiries, traditional chatbots struggle with nuanced language, unexpected user inputs or complex problem-solving (Gartner, 2023).

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The key differentiator is adaptability. Virtual assistants can evolve and refine their interactions over time, while chatbots are typically static unless manually updated. As businesses seek more intelligent automation, the shift from basic chatbots to AI-powered virtual assistants represents a critical evolution in customer engagement and operational efficiency.

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How are virtual assistants built?

Building a VA specific to your business needs requires several mechanisms:

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INTEGRATE > TRAIN > PROCESS > UNDERSTAND > RESPOND > LEARN

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1. AI Models: (e.g., Open AI’s ChatGPT, DeepSeek, Google’s Gemini) to process the user inputs and generate human-like responses.

2. Natural Language Processing (NLP) Engines: to interpret and understand user inputs.

3. Continuous Learning Mechanisms (CLMs): to improve responses over time.

4. Integrations: with Business Systems to fetch data and perform actions.

5. Training: on Business-Specific Data to customise interactions.

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The first three are the core of the VA and the latter to make it bespoke to your operations.

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How can virtual assistants support my organisation?

The core benefit of a VA is to help save time, which drives productivity and efficiency, allowing teams to focus on more value-added work. It is expected that AI will increase productivity by over 40% (PwC, 2025). On a basic level, VAs can become the first point of contact for customers. On an advanced level, they can help develop market research by aggregating the organisation’s own data and competitors' data and augmenting industry trends.

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Some significant benefits include:

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• Customer Support Automation:  Providing instant responses to customer inquiries, reducing wait times, increasing satisfaction and reducing the need for human contact, decreasing cost.

• Operational Efficiency: Automating repetitive tasks, for example, scheduling, data entry and order tracking.

• Sales & Marketing Assistance: Engaging prospects, answering FAQs and providing recommendations.

• Employee Support: Providing general support, supporting research, assisting with HR inquiries and providing first-line tech support.

• Business Development: Conducting market analysis, competitor research and production of bid materials.

• Data Insights: Analysing customer interactions to identify trends and improve services, for example, in customer satisfaction and FAQ’s.

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Risks associated with AI technologies and virtual assistants

Despite their advantages, AI assistants also come with many risks if not thoroughly understood, designed and implemented, such as:

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• Data Privacy Concerns: Through access to internal systems, AI models may process sensitive information, raising privacy and compliance risks.

• Bias in AI Responses: Biases can be introduced from both the technology (I.e. geopolitical motivations) and training from biased datasets. The risks may come in the form of incorrect information, discriminatory or misleading information.

• Security Vulnerabilities: Like all connected technology, VAs and their underpinning technologies can be exploited through hacking and brute force for phishing or data extraction.

• Lack of Human Oversight: Automated responses may lead to miscommunication or unsatisfactory resolutions, adding frustration to users.

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Managing tomorrow’s AI risks

While AI-driven technology presents various risks, these can be effectively mitigated by following expert guidelines. Here are key strategies:

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• Ongoing Compliance: Ensure adherence to data protection laws in the regions where your organisation operates (e.g., GDPR in the EU).

• AI Ethics Implementation: Many organisations have developed ethical guidelines to address risks related to misuse, questionable design and unintended consequences (Gov.uk, 2019). Virtual assistants rely on AI as underpinning technology and considering these ethical factors helps mitigate potential risks.

• Secure Implementations: Connected systems are always at risk from “bad actors”.  Protect connected systems from cyber threats by implementing encryption, strong authentication, continuous monitoring, and regular updates to prevent security breaches.

• Apply Human Oversight: AI requires ongoing monitoring to detect risks, whether from malicious activities or everyday user interactions. Establish clear processes for continuous oversight, escalation and development.

• Regularly Update Models: Regularly updating AI models is essential to prevent outdated and misleading information, which could lead to legal or financial risks. Continuous investment in updating, refining and training AI models ensures they remain accurate, relevant and responsive to feedback.

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Impact of different AI Models on virtual assistants

By far, the most well-known AI Model has been OpenAI’s GPT variants (GPT-3.5, GPT-4o underpinning ChatGPT). Many organisations have developed or are developing their own capabilities, such as the recent competitor DeepSeek. Different AI models have varying capabilities, influencing chatbot performance. Most of the models fall into four key categories:

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• Rule-Based Models: Simple decision-tree bots with predefined responses (limited flexibility), commonly used in a majority of chatbots.

• LLMs (Large Language Models): (e.g., ChatGPT, DeepSeek, Claude) Advanced language understanding with contextual awareness. Even these come with varying capabilities; for example, ChatGPT has strong conversational skills, is excellent at coding and general knowledge and demonstrates context-awareness. On the other hand, DeepSeek appears to focus on coding and technical tasks, highly customisable for industries.

• Custom AI Models: Tailored solutions trained on specific industry data. These are popular for specialist markets, for example, analysing stocks and shares.

• Hybrid Models: Combining AI-driven interactions with rule-based control for accuracy.

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Other Factors:

• Knowledge Base: The model’s training data significantly impacts its performance. For example, GPT-3.5 is built on 175 billion parameters, whereas GPT-4 leverages are estimated to be over 1 trillion, offering a more advanced understanding. The benefits of the additional training are vast, however, to mention a few: increased functionality, memory and ‘safer’ more accurate responses.

• Usage/Resource Costs: Most AI models are priced based on query volume. Higher usage may necessitate upgrading to a larger plan.

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Selecting the right model will depend on your unique use case, budget and need for customisation. It is important to have understood these considerations before setting out on your journey as the solution may greatly differ.

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How to select the right virtual assistant for your organisation

We have already discussed the general benefits, risks, considerations and technology to support your decision to introduce a virtual assistant. However, to ensure its successful introduction to your organisation, you also need to consider:

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• Business Needs: Well-defined primary use cases (i.e. customer service, sales, internal support, HR, etc.).

• AI Capabilities: Evaluate the many models and providers for AI tools on their NLP proficiency, response accuracy and adaptability.

• Integration Requirements: Ensure compatibility with existing systems (CRM, ERP, etc.).

• Cost & Scalability: From the outset, there is the cost of the initial implementation (dependent on the AI solution/variant selected), however, there are longer-term costs, from the ability to handle queries to ensuring resourcing is met for the human oversight.

• Security & Compliance: Keeping your organisation, its information and more importantly, its reputation safe should be the highest priority. This starts with adherence to data protection and security standards.

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By strategically deploying well-designed AI-powered virtual assistants, your organisation can boost efficiency, enhance customer satisfaction and gain a competitive edge, ultimately driving profitability. However, successful implementation requires careful planning and consideration. Ntegra's in-house expertise in developing and integrating advanced virtual assistant solutions ensures a smooth and effective adoption, guiding you through the complexities of AI technology.

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Connect with Ben Parish, Head of Innovation, to explore opportunities and collaborate.

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Authored by Saffwaan Bham, Senior Product Manager.