Navigating the Future with AI Trust, Risk and Security Management (AI TRiSM)

As we stride towards an AI-driven future, AI Trust, Risk and Security Management (AI TRiSM) emerges as a key technology trend poised to revolutionize businesses.

This innovative AI TRiSM framework allows organisations to identify, monitor and mitigate potential risks associated with the application of AI technology, including the rapidly evolving Generative and Adaptive AI. Adherence to this framework ensures compliance with pertinent regulations and data privacy laws.

In this article, we will unpack the concept of AI TRiSM, its operational dynamics and its strategic leverage for organisations.

Building Robust AI Systems: The Importance of AI Trust, Risk, and Security Management (AI TRiSM)

Companies implementing robust Artificial Intelligence Trust, Risk and Security Management (AI TRiSM) frameworks successfully deploy more valuable AI models. So, what does it take to make AI systems both secure and effective?

AI TRiSM and Cybersecurity: A Crucial Intersection 

AI models are susceptible to cyber threats, implying that cybercriminals can manipulate these models to optimise malicious processes, such as:

  • Malware Attacks
  • Data Breaches
  • Phishing Scams

In the first half of 2022, about 236 million ransomware attacks were reported globally, signifying a sharp rise from previous years. This surge can be attributed to the widespread adoption of novel technologies and adequate security measures.

The Imperative for an AI Bill of Rights 

The recently proposed U.S. blueprint for an AI Bill of Rights underscores the need for strict protective measures against potential AI perils, urging AI developers and users to integrate safety precautions within their AI models and strategies. This highlights the crucial need for rigorous AI TRiSM implementation.

Demystifying AI TRiSM

AI TRiSM is a comprehensive framework advocating for AI model governance, fairness, reliability, robustness, efficacy and privacy. It encompasses solutions, techniques and processes to enhance model interpretability, explainability, privacy, model operations and resistance against adversarial attacks, which is vital for both the enterprise and its customers.

IT leaders who invest time and resources into AI TRiSM can expect improved AI outcomes in terms of adoption, business goals and user acceptance. Given the relentless evolution of AI threats and compromises, AI TRiSM must be an ongoing effort.

The Rising Tide of AI TRiSM

By embracing AI transparency, trust and security, organisations are likely to witness an improvement in their AI model performance concerning adoption, business goals and user acceptance. By 2028, it’s predicted that AI-driven machines will comprise 20% of the global workforce, contributing to 40% of all economic productivity.

However, it’s important to note that several organisations have deployed countless AI models that even IT leaders find difficult to explain or interpret. Organisations failing to manage AI risk are more susceptible to negative AI outcomes, including security and privacy breaches, financial and reputational losses, and harm to individuals. Poorly managed AI could also lead to detrimental business decisions.

Strategising and Operationalising AI TRiSM

With increasing AI regulations on the horizon, it’s crucial to adopt practices promoting trust, transparency and consumer protection before such protections become mandatory. IT leaders need to adopt innovative AI TRiSM capabilities to ensure model reliability, trustworthiness, privacy and security.

Applying AI TRiSM shouldn’t wait until models are in production, as it may expose the process to potential risks. It’s advisable for IT leaders to familiarise themselves with potential compromises and utilise the AI TRiSM solution set to adequately safeguard AI.

Successful implementation of AI TRiSM requires a cross-functional team, involving legal, compliance, security, IT and data analytics staff. Establishing a dedicated team or task force is recommended to derive optimal results, with appropriate business representation for each AI project.

Benefits of AI TRiSM extend beyond mere regulatory compliance, enabling organisations to enhance the business outcomes derived from their use of AI.


AI TRiSM capabilities ensure model reliability, trustworthiness, security and privacy. Organisations need to manage AI trust, risk and security for better AI adoption, achieving business goals, and user acceptance. Consider AI TRiSM as a comprehensive solution set to adequately protect AI. 

In the digital era, ensuring the safety and effectiveness of AI is a necessity, not a luxury. AI TRiSM plays a pivotal role in meeting these demands, heralding a secure and promising AI future.

Apply now to the 2023 AI Awards!

Applications for the 2023 AI Awards are still open until August 25th so if you or someone you know is working on exciting projects, products, services and leaders in AI, Data Science and Machine Learning that are making a real impact in the industry, we want to hear from you!It’s free to enter and there are 12 categories you can apply for across industry, academia and leadership. 

Head over to to submit an application or please feel free to contact with any queries about the submission process.



1. What is ChatGPT? 

Chat Generative Pretrained Transformer, commonly known as ChatGPT, is an advanced chatbot and generative language tool inaugurated by OpenAI in November 2022. 

Given an initial phrase or “prompt,” the ChatGPT models calculate the most likely sequence of letters or words. Built upon OpenAI’s GPT-3 family of vast language models, ChatGPT allows interaction with a model via a conversational user interface. The system was trained using 300 billion words sourced from books, internet texts, Wikipedia articles, code libraries and then refined with human feedback. 

In January 2023, Microsoft introduced Azure OpenAI Services, encompassing ChatGPT, additional language models, and supplementary enterprise services. It’s crucial for enterprise planners to differentiate between OpenAI’s ChatGPT and Azure OpenAI Service which, while still evolving, offers promising enterprise operational features.

2. How will ChatGPT be utilised in the Enterprise?

ChatGPT and similar foundation models will serve as tools in conjunction with numerous other AI and hyperautomation innovations. They will be part of architected solutions that automate and augment humans or machines, and autonomously carry out business and IT processes. 

As generative AI finds its place alongside existing work approaches, ChatGPT and other competitors will be deployed to replace, recalibrate and redefine various activities and tasks that are part of many job roles.

3.  What are the primary applications of ChatGPT?

ChatGPT can generate and enhance prose and code development, summarize long-form texts, classify content, answer questions, and translate and convert languages (including programming languages).

4. ROI of ChatGPT: What is its value?

The ROI of ChatGPT hinges on the use case. For augmented scenarios, these tools can save time for writers and programmers, but such time savings may not necessarily benefit employers. Users should maintain realistic expectations about the use cases and the value they aim to derive, especially given that the service as-is has significant limitations, such as reliability issues. 

The generated text or code might be inaccurate or biased, necessitating human validation, which could offset the initial time savings. It is essential to link ChatGPT use cases to KPIs and ensure the project enhances operational efficiency, generates new revenue or improves experiences.

5. How much is ChatGPT?

The 3.5 version of ChatGPT, is free of charge. OpenAI recently announced the launch of the ChatGpt 4.0 subscription plan for at $20 a month. ChatGPT will also be part of the Microsoft Azure OpenAI Service soon, but the pricing is currently being introduced. It’s plausible that substantial elements will be bundled with different Microsoft 365 software subscriptions.

6. Direct Customer Interaction with ChatGPT: Should it be done?

Generally, no – providing ChatGPT-powered experiences directly to customers is considered too high risk for most use cases at the present time, except in rare cases perhaps related to gaming or entertainment, where the correctness or impartiality of the content may not be under rigorous scrutiny.

7. Will ChatGPT replace jobs?

Initially, ChatGPT will primarily enhance specific activities or tasks rather than replace whole jobs. Future iterations of ChatGPT, along with other tools and their combinations, will likely progress beyond augmentation and start executing targeted activities or tasks independently. This process will necessitate testing, quality control, guardrails and governance.

8. Impact of ChatGPT on the Enterprise Workforce: What can we expect?

As stated in response to question 2, ChatGPT will be one among various tools, including other hyperautomation and AI innovations, incorporated in architected solutions that automate, augment humans or machines, or autonomously conduct business or IT processes. It will replace, recalibrate and redefine the activities and tasks that constitute many job roles.

9. What will be the Magnitude of Workforce Impact?

There will be creation of new jobs and the redefinition of others. The net change in the workforce will considerably fluctuate based on factors such as industry, location and the size and offerings (products or services) of the enterprise. 

However, it’s evident that tools like ChatGPT (or competitors), hyper-automation and AI innovations will target repetitive, high-volume tasks with an emphasis on efficiency, such as reducing cycle time, boosting productivity and enhancing quality control (reducing error rates), among others.

10. Future Prediction for the Enterprise: What’s the forecast?

By 2026, over 100 million humans will collaborate with robot colleagues (synthetic virtual colleagues) for enterprise work. This will not solely be powered by ChatGPT (or competitors); it will involve several other technologies and solutions.

11. Is ChatGPT Artificial General Intelligence?

No. Despite the impressive capabilities of ChatGPT and related large language models (LLMs) or foundation models, they cannot comprehend, learn or undertake any intellectual task that humans can. ChatGPT is a type of reinforcement learning approach. While enhanced with human feedback, it fundamentally remains a machine learning construct and lacks the generalization attributes provided by symbolic techniques.

12. Is ChatGPT a New AI Paradigm? 

ChatGPT is more of an evolution of ongoing trends than a new paradigm. Its underlying model is based on transformer neural networks, which have been foundational for over five years, including in vendor applications.

Nevertheless, ChatGPT introduces some new elements to those foundation models, such as conversational and short-term memory layers and massive human-in-the-loop feedback (reinforcement learning) for training. The engineering employed to make the model available for mass consumption is also novel, requiring extensive computational resources and model-serving architecture.

13. What are the multilingual Capabilities of ChatGPT?

ChatGPT was trained on a multilingual corpus, enabling it to respond to inputs and generate outputs in several languages. Gartner has informally observed that ChatGPT performs comparably to the leading commercial machine translation model for English to Spanish, but is not as proficient for other official UN languages (Arabic, Chinese, French, and Russian). ChatGPT’s translation is slower than commercial engines. The use of GPT-3 for translation should be evaluated on a case-by-case basis.

14. What are the different Uses of ChatGPT?

ChatGPT can be utilized in four different ways:

As-is: By entering prompts and receiving results via the web-based interface. This is the most popular usage approach currently.

Prompt Engineering without APIs: This involves using a service like ChatGPT alongside other technologies as part of a workflow, which can be carried out manually or by using screen scrape and robotic process automation (RPA) technologies.

Prompt Engineering using APIs: Although there are solutions on Github enabling an API wrapper around ChatGPT, these are not recommended for production builds or scale, and they are not supported by OpenAI.

Custom Build: Creating a custom build of the core GPT2/GPT3 model for a bespoke implementation is possible, but it wouldn’t have the conversational interaction.

15. What are the current Limitations of ChatGPT?

ChatGPT’s training only covers data up until 2021, which limits its recency. It cannot provide the sources of the information used to generate its answers and lacks explainability. The reliability of the model is dependent on its (unknown) underlying sources, which can sometimes be erroneous or inconsistent. 

While it can generate language and code, it cannot create images. Currently, there is no supported API available. You cannot train ChatGPT on your own knowledge bases. Despite seeming to perform complex tasks, ChatGPT only makes predictions without understanding the underlying concepts. It does not provide data privacy assurances. Although recent updates have improved its ability to handle mathematical queries, it still cannot be relied on for computation.

16. Can I use my own Data with ChatGPT?

At present, you can use your own data only for providing prompts to ChatGPT, but not for training or fine-tuning it. If you’re using ChatGPT as-is, you can include your own data and content with your questions, like pasting in software code for ChatGPT to debug, or inputting text for it to summarize. 

However, presently you can’t add your own industry or domain knowledge data to train or fine-tune ChatGPT, although this functionality is expected to be available in the Azure service in 2023. As an alternative, you can use the GPT2/3 engines without the ChatGPT conversational interface or additions and use transfer learning to train your own version of the model, but it would not result in the same type of model as ChatGPT.

17. Can I personalise content with ChatGPT?

While you cannot personalise the user experience of ChatGPT, users can influence the generative output via their prompts, such as by requesting that the generated content adhere to a certain writing style or educational level. The Azure OpenAI ChatGPT service is likely to add APIs in the future, which will likely make it possible to intercept the input and output and handle the user experience with a different interface.

18. Can I build or Integrate ChatGPT into Other Systems?

Yes, it is possible to use ChatGPT in the building of or integration into other systems. Currently, it is more suitable to construct augmented approaches that support various roles.

19. What are the new features of ChatGPT?

ChatGPT is not a static service. For instance, it was recently improved to handle mathematical prompts more effectively. Microsoft might use the Azure OpenAI ChatGPT service to complement Bing search in 2023. Furthermore, it is expected that more formal API offerings will be added to the service. Recently, updates have been rolled out to Microsoft Teams Premium, utilizing the Azure OpenAI ChatGPT core model of GPT3.5.

20. Is ChatGPT Replacing or Threatening Search?

No, ChatGPT is not a threat to search but rather, it complements it. While ChatGPT generates answers, search is more focused on artifact discovery like finding a particular document or sentence. Many search and insight engine vendors have been using base GPT technology as part of their AI techniques for some time. It is predicted that over time, discovery methods like search will evolve to use foundation models in conjunction with existing approaches.

21. Who are the competitors of ChatGPT?

Yes, ChatGPT does have competitors. Several smaller vendors have utilized large language models, similar to ChatGPT, to deliver specific task usage. However, many of the larger technology vendors have not yet commercialized their offerings. Google announced its own offering, Bard, on February 6, 2023, while it is expected that competitors such as Baidu and IBM will enter the market later in 2023.

22. What are the markets around ChatGPT?

The biggest evolution will be in creating bespoke variants of models like GPT, where system integrators and vendors support end users to input their own knowledge bases via transfer learning. More corpora management and prompt engineering services and tools are expected to emerge in 2023, as well as tools for fact-checking and generated text detection. 

Vendors are likely to differentiate their products through task-specific fine-tuning of their models and by introducing tools to mitigate risks related to the explainability, reliability, fairness, security, and transparency of generated content.

23. What is the effect of ChatGPT on Current Natural Language Technologies? Does it make them obsolete?

No, ChatGPT does not render current natural language technologies obsolete. It intersects two markets in the NLT space: conversational AI and natural language generation. If your chatbot conducts transactional conversations and relies on your own knowledge body, then ChatGPT won’t replace it. At present, ChatGPT serves as a broadly useful, general-purpose conversational tool, not a single-API solution for NLT. 

Within a workflow, ChatGPT and GPT technologies have a role. It might be possible to use the technology within NLT systems, like generating synonyms, utterances, and responses. It’s advisable to check with your current vendors to understand how they are utilizing generative technologies like ChatGPT.

24. Security of ChatGPT for Staff Usage ChatGPT. Should be used with the same caution as public online platforms?

Employees should avoid sharing sensitive personal, company or client information. While there aren’t currently clear assurances of privacy or confidentiality, Microsoft plans to introduce privacy assurances for its Azure OpenAI ChatGPT service, just like its other software services.

25. Does ChatGPT has a toxic content filter for both inputs and outputs?

Due to the nuanced and contextually dependent nature of this task, users should not completely rely on the model’s output for compliance or risk management, and should ensure human oversight of inputs and outputs.

26. Is there a Risk of Misuse of ChatGPT by Bad Actors?

There’s a valid concern that bad actors may misuse ChatGPT to generate false information, create convincing phishing emails, or even generate malicious code. The simplicity and widespread availability of ChatGPT heighten this risk. Users may be required to sign ethical usage agreements, but these could be hard to enforce.

27. Who Can View Conversations with ChatGPT?

ChatGPT service providers, currently OpenAI and Microsoft, can review conversations to improve their systems and ensure compliance with their policies. There are no assurances regarding other parties who might access the posted information. The Azure version of the service is expected to follow existing Azure OpenAI services in this respect.

28. Can conversations with ChatGPT be used for Training?

Yes, conversations may be used for training and could be reviewed by trainers. It is not currently possible to delete specific prompts, so users need to be careful about what they share. While it is possible to delete an account, this action won’t erase the training data.

29. Is there biases in ChatGPT?

ChatGPT’s fine-tuning is aligned to the trainers’ preferences rather than verified facts, leading to plausible but potentially unreliable outputs. Bias may be present in the large datasets used to train the GPT-3 model. Despite OpenAI’s efforts to minimize bias, there have been known instances of it surfacing.

30. Is there regulatory Risks Regarding Training Data Content Ownership?

There are concerns about the ownership of data and intellectual property rights with respect to content used to train GPT-3 and ChatGPT. As of now, there is no clarity on this matter, which poses a risk to OpenAI and the further usage of ChatGPT which needs to be resolved.

31. Can you detect ChatGPT-Generated Content?

There’s currently no reliable method to detect whether content was generated by ChatGPT or a human. Some tools have attempted to do this, but results so far have been mixed.

32. Should I implement a Company Policy on ChatGPT?

It is advisable to establish a policy as knowledge workers might already be using ChatGPT for various tasks. An outright block might result in covert “shadow” ChatGPT usage, giving organizations a false sense of compliance.

Employees should treat information posted through ChatGPT as if it were public. Organizations should monitor usage, encourage innovation, but ensure ChatGPT is used responsibly and never unfiltered with customers and partners.



The provided list of resources highlights the various sources from which information about ChatGPT and related AI technologies has been drawn for the purpose of the FAQ. These resources, provided by the creators and hosts of the technology, offer insights into the development, deployment and implications of using AI models like ChatGPT. Here’s a brief summary of what these resources entail:

ChatGPT and GPT Board Reference Presentation: A deep dive into the workings, application, and impact of GPT and ChatGPT models.

Innovation Insight for ML-Powered Coding Assistants: A resource providing information on how machine learning is being used to facilitate coding, discussing AI models like GPT-3’s code-generation capabilities.

ChatGPT: Optimizing Language Models for Dialogue, OpenAI: A detailed explanation of the development and optimization process of the ChatGPT model, published by OpenAI.

Azure OpenAI Service, Microsoft: Information on Microsoft’s Azure OpenAI service, including its features, capabilities, and use cases.

Introducing ChatGPT Plus, OpenAI: An announcement by OpenAI introducing the enhanced features and capabilities of the ChatGPT Plus model.

General Availability of Azure OpenAI Service Expands Access to Large, Advanced AI Models With Added Enterprise Benefits, Microsoft: A release by Microsoft announcing the broader availability of their Azure OpenAI service, with an emphasis on its benefits for enterprises.

Microsoft Teams Premium: Cut Costs and Add AI-Powered Productivity, Microsoft: A resource detailing the integration of AI technologies into Microsoft Teams for enhanced productivity and efficiency.

Data, Privacy, and Security for Azure OpenAI Service, Microsoft: Information provided by Microsoft regarding data handling, privacy measures, and security features within their Azure OpenAI service

Apply now to the 2023 AI Awards!

Applications for the 2023 AI Awards are still open until August 25th so if you or someone you know is working on exciting projects, products, services and leaders in AI, Data Science and Machine Learning that are making a real impact in the industry, we want to hear from you!It’s free to enter and there are 12 categories you can apply for across industry, academia and leadership. 

Head over to to submit an application or please feel free to contact with any queries about the submission process.


Understanding the Capabilities and Limitations of AI in Creative Domains

AI has made monumental strides in recent years, now capable of generating ideas and content in novel ways. These tools can rehash and reshape existing ideas to generate new ones or create novel ideas reminiscent of familiar ones. 

However, AI’s capacity to produce something genuinely unique is limited due to its inability to comprehend and synthesize concepts like humans. Nevertheless, AI can play a crucial role in spurring human creativity by generating fresh ideas and tweaking existing ones.


The Intersection of AI and Creativity: A Contemporary Dialogue

The astonishing developments in Generative AI tools such as ChatGPT, Midjourney, Dall-E and others, have sparked debates about whether creativity is a uniquely human trait. 

Noteworthy milestones of Generative AI include:
  • The AI-created artwork “The Portrait of Edmond de Belamy,” sold for an unexpected $432,500 by Christie’s in 2018.
  • Successful collaborations between AI and music producers, such as Grammy-nominee Alex Da Kid and IBM’s Watson.
In these instances, humans remain in control by filtering the AI’s output according to their vision and retaining the piece’s authorship. However, AI image generator Dall-E can quickly produce unique content, blurring the lines of authorship. The algorithm, the numerous artists whose work was utilized or the prompter who detailed the style, reference, subject matter, and emotion could all arguably hold authorship.

Dissecting Creativity: The Three Forms


Margaret Boden identifies three types of creativity: Combinational, Exploratory and Transformational. 

Combinational creativity merges familiar ideas, while Exploratory creativity yields new ideas by delving into ‘structured conceptual spaces.’ The synthetic creativity exhibited by AI is comparable to these forms. However, Transformational creativity, which involves generating completely original ideas, remains a challenging feat for AI and a major topic in the discourse of AI’s potential role in copyright infringement.


The Systematic Nature of AI Creativity

AI’s creative processes are predictable and systematic, unlike the often spontaneous creativity displayed by humans. The key difference lies in the motivations. While artists are driven by self-expression and product creation, AI is driven by consumer demand. Therefore, AI art only reflects what we request, not necessarily what we need. So far, Generative AI appears to thrive best when combined with human creativity, augmenting rather than replacing it.

Implications of Synthetic Creativity for Businesses

Synthetic creativity, as currently exhibited by AI, is a significant asset to business and marketing. Potential uses include AI-enhanced advertising, AI-designed furniture and AI-assisted fashion styling. While these applications are vast, they also necessitate another form of creativity: Curation. AI may ‘hallucinate,’ or generate nonsensical output, requiring the human skill of sense-making to create a unified and compelling vision.

Apply now to the 2023 AI Awards!

Applications for the 2023 AI Awards are still open until August 25th so if you or someone you know is working on exciting projects, products, services and leaders in AI, Data Science and Machine Learning that are making a real impact in the industry, we want to hear from you!It’s free to enter and there are 12 categories you can apply for across industry, academia and leadership. 

Head over to to submit an application or please feel free to contact with any queries about the submission process.


AI and Machine Learning: A Promising Future for All

Boosting the benefits of AI and Machine Learning, AI Ireland champions safety and accountability as crucial pillars in AI system development.

AI and the Workforce: Echoes of Past Technological Fears

Just as calculators once stirred unease among mathematicians, today’s rapid advancements in AI and ML are triggering debates and concerns about the future of jobs worldwide. Public perceptions towards AI development vary greatly, influenced by factors such as education levels, wages, technical expertise, and gender. Amid these anxieties, we at AI Ireland remain not fearful but excited for the future.

Technological Advancement: A Catalyst, Not a Threat

Historically, similar concerns have emerged with every industrial revolution, intensifying with the rapid technological progress in the 20th century. Yet time and again, technology has proven to be a catalyst for efficiency and achievement, rather than a hindrance.

The Power of AI: Amplifying Human Potential

Just as few would choose to perform complex calculations by hand today, AI’s power lies in its ability to automate low-level, repetitive tasks. This not only improves efficiency but also frees up workers to concentrate on higher-level tasks.

Use Cases: The Wine Industry and Cybersecurity

Examples of AI’s potential can be found in the California wine industry, where automation has increased and allowed workers to shift to more complex roles. Similarly, in the cybersecurity sector, automation of routine tasks allows professionals to concentrate on more critical activities, like analyzing and responding to attacks.

The Collective Learning of AI

Just as our proficiency with technology increases over time, AI systems learn and evolve with each new piece of data. This continual learning can greatly benefit established disciplines like the advertising industry, where AI and ML can help compile useful data, enhancing precision and delivering significant benefits.

The Human Factor in AI: Unleashing Exponential Power

Despite the efficiency of machines, human collaboration remains paramount to success. AI platforms can significantly enhance human capabilities by connecting companies with the best service providers for their needs, thereby fostering opportunities and enabling individuals to benefit from the platform’s efforts.

Enhancing Teamwork and Communication with AI

AI’s potential extends to improving collaboration and communication, particularly in buyer-seller marketplaces. AI-based systems can learn from continuous feedback, becoming smarter and more capable of predicting customer preferences. This increased understanding allows AI to match intangible factors for successful business relationships, such as communication preferences and company culture, leading to beneficial results for all parties.

The Increasing Value of Human Talent in an AI-Driven World

The progression from paper and pencil to calculators and spreadsheets didn’t replace mathematicians – it made them more valuable. This value will continue to grow with the advancement of sophisticated analytics engines, which require the human touch for interpretation and application.

A Look at the Future of AI in the Workplace

The last few decades have seen an exponential increase in speed and the democratization of computing capabilities, leading to a more effective workforce. The evolution of calculators alone highlights our rapid rate of advancement. 

As AI continues to develop, it is set to transform the workforce and the workplace in ways we can only imagine today.


How is AI being applied to business?

Artificial Intelligence is still an unfolding technology, and its complete influence and advantages remain untapped. AI breakthroughs are among various elements causing disruption in current markets and facilitating fresh digital business projects. Moreover, AI finds applications in diverse sectors, companies and roles in many ways.

Here are a few examples of AI application in business operations:

1. AI in Human-like Communications

Machine learning is paving the way for AI applications such as chatbots, autonomous vehicles, and smart robots that replicate human communications.

2. AI in Biometrics

Through deep learning techniques, AI provides solutions like facial recognition and voice recognition. Neural networks are used to hyper-personalize content through data mining and pattern recognition.

3. AI in IT Operations/Service Desk

AI facilitates IT support with Virtual Support Agents, ticket routing, information extraction from knowledge management sources, and providing answers to common questions.

4. AI in Supply Chain Management

AI assists with predictive maintenance, risk management, procurement, order fulfilment, supply chain planning, promotion management, and decision-making automation.

5. AI in Sales Enablement

AI can help identify new leads, nurture prospects through intelligent tracking and messaging, as well as improve sales execution and revenue through guided selling.

6. AI in Marketing

AI enables real-time personalization, content and media optimization, campaign orchestration, and uncovers new customer insights for effective marketing deployment.

7. AI in Customer Service

AI predicts customer needs and proactively deflects inquiries. Virtual customer assistants equipped with speech recognition, sentiment analysis, and automated quality assurance provide round-the-clock customer service.

8. AI in Human Resources

AI facilitates recruitment processes, skills matching, and leverages recommendation engines for learning content, mentors, career paths and adaptive learning.

9. AI in Finance

AI helps in dynamic processes requiring judgment and handling unstructured, volatile, high-velocity data. Examples include new accounting standards compliance, expense reports review, and vendor invoice processing.

10. AI in Sourcing, Procurement, and Vendor Management (SPVM)

AI assists in spend classification, contract analytics, risk management, candidate matching, sourcing automation, virtual purchasing assistance, and voice recognition.

11. AI in Legal

AI finds use in contract assembly, negotiation, due diligence, risk scoring, life cycle management, e-discovery, invoice classification, and more.

As enterprises adopt AI more widely, it’s inevitable that accompanying threats will arise, potentially posing significant risks to the organization. It’s crucial that these threats are assessed proactively to bolster stakeholder confidence in AI.

By 2025, it’s anticipated that regulations will demand greater emphasis on AI ethics, transparency and privacy. Far from inhibiting AI, these requirements will likely foster trust, stimulate growth and enhance the global performance of AI.


What is an Enterprise AI Strategy?

An enterprise AI strategy is a comprehensive plan that business leaders create to integrate Artificial Intelligence into their operations.

This strategy outlines AI use cases, weighs potential benefits and risks, aligns tech and business teams, and shifts organizational competencies to support AI implementation. Such a plan focuses on choosing AI initiatives that align with the company’s objectives and solves specific business problems.

As a company matures in its AI journey, the strategy evolves to address broader applications and greater impacts. Key components of this strategy include defining the AI vision, assessing and mitigating AI risks, crafting an AI strategic action plan, planning for AI adoption, and securing buy-in for the AI program.

Leveraging AI

To fully leverage the benefits of AI, business leaders must devise a holistic AI strategy. This strategy should pinpoint specific use cases, measure potential advantages and risks, synchronize tech and business teams, and shift organizational skills to bolster AI adoption.

Value extraction from AI requires strategic initiative selection, centered on your organization’s goals and the business challenges you aim to address. Integrating AI into your existing software suite is crucial for its success, and it is essential to harness data from all business segments to enhance its capabilities.

Companies in the early stages of AI maturity often focus on cost control use cases. As they advance in their AI journey, they start to explore other significant aspects such as improving customer experience. The application of AI becomes more widespread and impactful as AI maturity escalates.

1. Defining an Enterprise AI Strategy

For a company to seize AI advantages, executive leaders must craft a comprehensive AI strategy. This strategy should outline use cases, quantify benefits and risks, align business and IT teams, and modify organizational skills to promote AI adoption.

2. Selecting Strategic AI Initiatives

To extract value from AI, you should strategically select initiatives. These should align with your organization’s objectives and aim to solve specific business challenges. Successful AI implementation involves integrating AI into your existing software ecosystem and leveraging data from all business areas to power its features.

3. AI Use Cases

Typically, organizations in the early stages of AI maturity focus on cost control before moving on to critical elements such as customer experience. Research suggests that as AI maturity increases, its application broadens, and its impact becomes more significant.

4. Key Components of an Enterprise AI Strategy

AI Vision: Connect AI goals with enterprise aspirations. Clearly communicate how AI will support digital transformation objectives, encourage organization-wide AI fluency, and define success metrics.

AI Risks: Evaluate your potential risk exposure areas, including regulatory (privacy laws), reputational (AI bias), and organizational (lack of skills or infrastructure), and create mitigation plans.

AI Strategic Action Plan: Identify the effect on business models, processes, personnel, and skills. Adopt a portfolio approach to AI opportunities and assign responsibility for AI strategy development and execution. Interdisciplinary teams and data literacy are crucial for success.

AI Adoption: Define the use cases (such as human-like engagement, process optimization, insight generation) and use value maps and decision frameworks to prioritize adoption.

AI Program Buy-in: Promote the initiatives launch and celebrate its successes. Equip other C-suite leaders with the ability to share the AI team’s achievements.