How Can Banks Implement AI Ethically?

Artificial general intelligence is generally defined as AI capable of performing any intellectual task a human being can do, including the ability to reason about and think up complex problems it was not programmed to solve. Unexplainable results are a significant challenge in AI systems due to their inherent black box nature. Explainability — understanding how an algorithm reaches its conclusion — is not always possible with AI systems, ChatGPT given the way they are configured with many hidden layers that self-organize the weights used as parameters to create a response. The advent of generative AI dramatically expands the type of jobs AI can automate and augment. Businesses and consumers have quickly adopted GenAI technology, using applications such as ChatGPT, Gemini and Copilot to conduct searches, create art, compose essays, write code and make conversation.

Another important factor is the structure of a company’s technology stack—AI must be able to flexibly integrate with current and future systems to draw and feed data into different areas of the business. Generative AI use cases in the customer support industry includes AI-enhanced customer interactions, sentiment analysis, and AI-driven information access. GenAI technologies enable more intelligent, personalized, and faster services, resulting in remarkable refinements in how businesses engage and assist their customers. Some of the more popular generative AI tools for customer interaction and support include HubSpot, Dialpad Ai, and RingCX. GenAI streamlines processes, elevates product design, and boosts operational efficiency for organizations in the manufacturing industry.

Another common mistake I have seen is companies getting mesmerized by transformative and grandiose AI projects which often leads them to overlook the value of quick wins and iterative improvements. I want to highlight some ChatGPT App instances – let’s call them cautionary tales – that have emerged from implementing AI without preparation. However, if you are truly ready to implement AI into your operations, you can reach out to us for further guidance.

Other analysts suggested a layered or phased adoption of the technology may be the most best path forward. “A big gap exists between current LLM-based assistants and full-fledged AI agents,” Gartner analyst Tom Coshow wrote in a blog post in early October, noting that to close this gap enterprises will have to learn to build, govern, and trust them. “Think of RPA as a train on tracks — it can only go where the tracks are laid. Agentic AI is more like a self-driving car — it can navigate different routes and situations adaptively,” said Paul Chada, co-founder of agentic AI-based software providing startup Doozer AI. The AI market is growing rapidly and the percentage of companies using AI continues to grow with it.

Benefits of Using AI in Business

Generative AI is expected to remarkably impact more industries, but ethical considerations and human oversight will remain indispensable in guiding its development and use. Notion AI is an add-on feature integrated into the Notion project management platform, with generative capabilities for summarizing notes, brainstorming ideas, and drafting content. It is best suited for businesses that rely heavily on documentation and project management, such as tech startups and educational institutions. The tool’s seamless integration into the Notion platform eliminates the need to switch between different applications, improving efficiency.

The way I see it, software agencies can, and have, played a pivotal role in helping companies navigate the complexities of AI implementation and bypass common mistakes. Last, but not least, a successful AI implementation requires collaboration and validation across diverse teams and stakeholders. It is common to see organizations failing to do so which led them to overgeneralization, brand dilution, and consumer skepticism, ultimately impacting the organization’s credibility and reputation in the market. When companies fail to do so it can result in innovation tunnel vision, missed collaboration opportunities, and regulatory and ethical oversights. Another important point I think we should be keeping in mind is that user-centric design lies at the core of successful AI implementation.

Without that trust, an AI implementation will be unproductive, according to experts. However, executives are finding that AI in the enterprise also comes with unique risks that need to be acknowledged and addressed head-on. If you are interested in implementing AI in your business, feel free to reach out to me or one of our experts to get some more information.

Generative AI Companies to Watch

These chatbots can handle a wide range of inquiries, from answering frequently asked questions to assisting with product recommendations. For example, a small online retailer can implement a chatbot to help customers with product searches, order tracking, and returns, significantly improving customer satisfaction and reducing the burden on customer service representatives. Lack of expertise isn’t the only obstacle that businesses are experiencing with AI. Many companies have found that the cost of implementing AI is much higher than initially expected. According to S&P Global’s “2023 Global Trends in AI Report,” commissioned by WEKA, more than half of AI decision makers report that cost barriers have led to struggles in deploying the latest in AI tools. Even if these cost barriers are short term, they can make AI seem inaccessible or leave AI projects lingering in the pilot phase of development.

Teams comprising business stakeholders who have technology and data expertise should use metrics to measure the effect of an AI implementation on the organization and its people. The true power of AI in the enterprise extends far beyond a few expensive GenAI-driven “co-pilots” assisting knowledge workers with administrative tasks and content generation. The future of AI lies in its seamless embedding within business processes and systems, ensuring that AI capabilities are integrated, not standalone. The excitement surrounding GenAI – known for its ability to create text, images, and other media from simple prompts – is well-founded. It promises to revolutionize content creation, customer service, and numerous other domains. In fact, according to Gartner’s research, global spending on AI is expected to reach £229 billion by 2027, with enterprise applications embedding of GenAI comprising a significant portion of this investment.

In this incident, liquid nitrogen leaked from the plant’s refrigeration system. Liquid nitrogen is commonly used in the food industry for freezing products, but it becomes dangerous when it vaporizes and displaces oxygen in the air. The leak caused the oxygen levels to drop, leading to a hazardous environment.

Younger generations’ optimism about AI also dovetails with the importance of DEX. Last year’s Riverbed survey on DEX found that Gen-Z and Millennials have the highest expectations of DEX, with 68% of decision-makers saying that poor user experiences would drive employees to leave the company. It is hard to overstate the scope of development being done on artificial intelligence by vendors, governments and research institutions — and how quickly the field is changing. The rapid evolution of algorithms accounts for many recent advancements, notably the new — and disruptive — AI large language models that are redefining the modern search engine.

According to the Small Business and Entrepreneurship Council’s 2023 Small Business AI Adoption Survey, 83% of small business owners plan to invest in AI in the next year. “We see this spectrum of users from skeptics and novices on one end to power users on the other,” says Stallbaumer. Generative AI adoption at work feels like the corporate equivalent of the space race. Everyone is rushing to adopt it, B2B partnerships are forming rapidly (such as NVIDIA and McKinsey and PwC and OpenAI), and employees are scrambling to learn what it means for their roles. Imagery provided by advanced aerospace technologies – such as high-resolution satellites and drones within an AI environment – can generate customised analyses for applications relating to catastrophe response and damage assessment.

Every organization will need to assess whether and when to implement generative AI tools. Ultimately, organizations that fail to adopt new technologies will fail to compete on a quality and cost basis with their competitors, while those that implement it carelessly can experience detrimental effects. While we firmly believe the rewards will outweigh the risks, the assessment must be done, and the potential liabilities must be identified and ultimately mitigated. Working with experts, including legal counsel, developing a roadmap to implementation, adopting governance policies, and training your base of users and employees will all accelerate the quality and speed of adoption. These success stories demonstrate the variety of ways in which AI is used to improve operations in the workplace, from perfecting search algorithms through machine learning and revolutionizing healthcare practices, all while optimizing logistics. In sales and marketing, AI can deliver personalization at scale, automatically generating product recommendations and consumer communications based on purchase history and other data.

“This will leave many gaps in competitiveness between organisations who know how to implement AI and the ones who don’t.” AI looks at past deals, market trends, and customer actions to make these predictions. You can foun additiona information about ai customer service and artificial intelligence and NLP. Businesses need to assess which AI technologies align with their goals and capabilities.

This trend reflects employee enthusiasm but underscores a significant leadership gap. The potential for unintended data breaches is a serious concern, given that employees can upload sensitive company documents to AI platforms for analysis, unknowingly compromising proprietary information. This scenario not only raises data privacy concerns but also increases legal and competitive risks. Without clear guidelines, businesses face the dual challenge of realizing AI’s benefits while protecting their valuable information assets.

Once a baseline is established, it’s easier to see how the actual AI deployment proves or disproves the initial hypothesis. In addition, consider who should become champions of the project, identify external data sources, determine how you might monetize your data externally and create a backlog to ensure the project’s momentum is maintained. Early implementation of AI isn’t necessarily a perfect science and might need to be experimental at first — beginning with a hypothesis, followed by testing and measuring results.

• First, creating an environment of transparency and education is important so that the employees understand all its advantages along with removing misconceptions.
• Our community is about connecting people through open and thoughtful conversations.
• For example, deep learning, a subset of machine learning, uses neural networks to process large data sets and identify subtle patterns and correlations that can give companies a competitive edge.
• One key aspect that I have witnessed is the expertise in facilitating cross-functional collaboration.
• Build an AI strategy for your business on one collaborative AI and data platform—IBM watsonx.
• Generative AI models can be trained to detect subtle patterns of equipment failures, which is valuable in predictive maintenance.

The introduction of AI to business applications raises urgent concerns around the ethics, privacy, and security of the technology. Governance of AI technology must consider how to develop and expand current legislation around privacy and data protection, including purpose specification, data collection and use limitations, accountability and security of data storage. “These systems often present integration challenges, making it difficult to implement drastic changes to the existing technology stack. The successes and failures of early AI projects can help increase understanding across the entire company. “Ensure you keep the humans in the loop to build trust and engage your business and process experts with your data scientists,” Wand said.

The latest in artificial intelligence

Eighty-nine percent of organizations believe AI and machine learning will help them grow revenue, boost operational efficiency and improve customer experiences, according to research firm Frost & Sullivan’s “Global State of AI, 2024” report. Although 82% of respondents say they believe their own organization is ahead of industry peers in AI adoption, only 37% are prepared right now to implement AI. The survey found several impediments to AI adoption, including security issues, the quality and suitability of data used to train AI models, and companies’ ability to effectively implement solutions. IT organizations apply machine learning to ITSM data to gain a better understanding of their infrastructure and processes. They use the named entity recognition component of NLP for text mining, information retrieval and document classification. AI techniques are applied to multiple aspects of cybersecurity, including anomaly detection, solving the false-positive problem and conducting behavioral threat analytics.

AI can have a huge impact on operations, whether as a forecasting or inventory management tool or as a source of automation for manual tasks like picking and sorting in warehouses. It can prove useful in allocating resources or people, like drivers, scheduling processes, and solving or planning around operational disruptions. Sales and marketing departments can use AI for a wide range of possibilities, including incorporating it into CRM, email marketing, social media, and advertising software.

The introduction of AI in the workplace marks a significant turning point for business and industry, as it has transformational potential across all sectors. The case studies highlight the benefits companies have achieved by integrating AI strategically into their operations, which include better search engine features as well as fundamental changes in healthcare and logistics. The success stories shed light on the flexibility and competitive advantage AI brings to companies that are willing to innovate. Good data governance also helps ensure that the model outputs are observable and explainable. Organizations that are involved in a successful AI transformation typically monitor data activity and continuously audit their cybersecurity practices. This phase might involve multiple processes to increase data security on-premises, in the cloud, and in software as a service (SaaS) apps.

Leading Examples of Generative AI in Top Companies

Some leaders might baulk at the cost without being able to visualize data, results, and potential reward or may simply not feel comfortable selling the costs to internal stakeholders. An additional worry for businesses is the changing capabilities of the tools themselves. AI is evolving quickly, and the best model for the task at hand one week may not be the best the following week. Having an orchestration layer that can move applications between providers without impacting the business, is therefore critical for building agility into AI business offerings and processes. However, with AI being a newly prevalent technology with an abundance of information about it published daily, not all businesses will be aware that offerings for this exist yet. Every AI system introduces certain risks, whether related to cybersecurity, operational disruptions or legal liabilities.

A strong, responsible AI project with a carefully crafted methodology behind it can improve performance and give businesses a significant competitive advantage. But as in all digital transformations, successful adoption and tangible business impact are far from guaranteed. A product team might use AI to test and optimize a product through its lifecycle. The technology can also be applied to threat management and decision support. These functions reduce incident response times and helping business leaders proactively plan for and manage future risk.

In the entertainment industry, the technology can compose music or scripts, develop animations, and generate short films. With GenAI, marketing teams can quickly write blog posts, social media updates, and product descriptions implementing ai in business in bulk. These tools can also translate content into multiple languages, ensuring message consistency across different markets. Beyond text, GenAI can also create visuals, such as vivid images or infographics for ads.

(4) Ethical and legal considerationsAI deployments must also be reviewed thoroughly to identify any ethical or legal implications, especially when it comes to data usage and privacy. Ensuring compliance with regulations like GDPR is crucial in maintaining trust and integrity in the use of AI. A key starting point in the AI journey is to pinpoint where AI truly adds value. The truth is that there are some areas where AI truly excels – and can outperform a human – and there are others where human intervention is definitely still required. To see value quickly, it’s wise to focus on AI’s strengths and consider how they can be applied within an organisation.

At Netguru, we have a proven track record of assisting clients with AI solutions. We also host AI Primer Workshops specifically designed for decision-makers looking to understand the potential of AI implementation in their companies. AI consulting experts can provide valuable insights, best practices, and hands-on support throughout the process. They can help you assess your needs, develop a tailored AI strategy, select the right tools and technologies, and implement solutions that deliver measurable results within your budget constraints.