Nicole Adriaans, sales manager: data, analytics and AI, iOCO.
For decades, business leaders were told that the latest technologies available would revolutionize their operations, and that those who lagged behind in technology adoption would suffer. While there is a lot of truth to these sentiments, the reality is much more nuanced. Despite the lessons learned by early adopters of the “must-have” technologies of recent years, it has become standard operating procedure for most businesses to immediately consider investing in the newest tools available, thereby perpetuating the cycle of familiar hype.
AI, for example, has become the latest buzzword in the business world. Organizations of all sizes and across industries are adding AI to their technology portfolio, seeking immediate results. Unfortunately, not all AI solutions are equal, and many companies struggle to ensure their investments deliver the promised results.
While there is no doubt that delaying AI adoption can put a business at a competitive disadvantage, success requires more than just investing in an AI solution. Success also relies on the integration of old and modern technologies, risk management and access to appropriate skills. Another critical factor to consider when implementing AI is considering ethics that fit the industry or ecosystem.
In other words, businesses need tailor-made, fit-for-purpose AI solutions to achieve results.
The right tool for the job
Generative AI (GenAI) like ChatGPT has helped AI become a mainstream technology, but businesses are starting to realize that GenAI solutions are limited on their own. Generative AI in action would be an HR service chatbot that can answer common questions and provide information to employees/users based on their feedback. A compound AI combines the capabilities of multiple agents to provide more comprehensive assistance to the user.
As an example, consider an employee named Nicole who wants to go on leave and needs to know the weather conditions for her planned trip, as well as the amount of leave she has available.
In this scenario, the compound agent seamlessly integrates two separate agents – a weather information agent and a human resources agent – to provide Nicole with the information she needs.
First, Nicole interacts with the compound agent and informs him of her intention to take a vacation. The Compound Agent then uses the Weather Information Agent to obtain the forecast for her vacation location, providing Nicole with the information needed to plan her trip accordingly.
Then, the compound agent accesses the HR agent to retrieve information about Nicole’s current leave balance. He informs her how much leave she has and helps Nicole plan the vacation accordingly.
By combining the capabilities of the Weather Information Agent and the Human Resource Management Agent, the Compound Agent provides Nicole with more comprehensive support, helping her plan her leave more effectively and efficiently. Overall, a compound agent improves the user experience by seamlessly integrating the capabilities of multiple agents to provide more comprehensive assistance to the user.
Agentic AI is a compound agent that essentially integrates with external databases and tools to improve problem-solving capabilities and adaptability to scenarios that would typically present themselves in these use cases. Built to handle incredibly complex and repetitive tasks across various business functions, agentic AI solutions such as iOCO AI Agents can analyze huge volumes of data, understand relationships, provide visibility into operations and support better decision-making. Use cases include fraud detection, customer service, supply chain management, compliance and risk management, and logistics optimization, to name a few .
Data is at the heart of a successful AI solution. Data improves decision-making, can be used to automate tasks and deliver hyper-personalized customer experiences, but many companies continue to struggle under the weight of traditional business models and analog business processes, hampering the potential of data analysis and AI. Some have started to modernize, but are failing to make the required cultural change – or commit to the information management, advanced skills and technology investments needed.
Since each AI use case has its own data requirements, businesses must invest in their data practices in order to reap the benefits of any AI investment. Business and technology leaders should analyze industry-specific examples of how data and analytics can deliver economic benefits to the organization, and measure the value of the organization’s data assets to help evolve the business mindset towards data as a business asset. Decision makers in charge of the data practice should emphasize their involvement in business and strategic planning so that they can ensure that data is at the forefront of how they execute those plans. This should be communicated internally and publicly through annual reports or reviews and investor conferences.
Ideally, data and analytics strategies should be a regular and common topic of discussion on boards across all sectors, so that organizations can use data analytics as competitive weapons, operational accelerators and catalysts for innovation. This will become even more important as AI continues to permeate every aspect of business operations.
No conversation about AI is complete without a discussion about ethics. As organizations begin to collect and use more data, they need to be aware of ethical considerations, security, appropriate use, and AI hallucinations or biases in the algorithm. AI hallucinations occur when an AI system generates incorrect, misleading, or absurd information in response to a user prompt.
Data, AI and digital technologies must therefore be used responsibly and ethically – not only to avoid embarrassment, but also to build trust with stakeholders and customers. A good way to do this is to publish an ethical framework that includes confidentiality, discrimination, bias and accountability. As AI tools become smarter, it will be increasingly easy to forget that we are talking to an AI agent. Companies must therefore be transparent and explicit about how technology is used.
Organizations that have embarked on the AI journey have already discovered that there are a multitude of factors to consider to ensure they get the most out of the technology. The keys to success, however, lie in the data used by the system and the type of AI solution implemented. Once businesses master these two essential aspects, AI can transform the business by providing actionable insights from vast data sets, automating decision-making processes, personalizing customer experiences, generating creative content and enabling innovative solutions that drive growth, efficiency and competitive advantage.
