Voices | How to effectively implement AI in your organization

Mike Evans, Chief Innovation Officer at Amplifi explores how businesses are leveraging AI to overcome data challenges and drive innovation. Gain insights into the application of generative AI and large language models in transforming business practices.

Unless you’ve been living under a rock, you’ve probably heard “AI” being talked about, or at least ChatGPT. 2023 marked a watershed moment in the AI landscape, driven by the release of Chat GPT in late 2022 which sparked enormous interest in Generative AI and Large Language Models (LLMs). The term Artificial Intelligence has been around for decades, but is often used carelessly within data and analytics, with every technology under the sun claiming to be AI-enabled, but not always being as clear about what that really means. Yet, it's the recent advancements in large language models that have sparked widespread excitement.

Historically, AI has been prevalent in various forms – from facial recognition to virtual opponents in video games, emulating human-like behavior. The current buzz, however, centers around generative AI's ability to create new, human-like content. This includes generating images, writing articles (not this one, though!), or simulating various celebrities in “deep fakes” - essentially, producing outputs that closely mimic human creativity. It’s important to consider the nuances of this transformation and its implications for data management, ethics, and broader societal impacts.

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AI is everywhere

AI has been around and widely adopted for some time. Whether it's the digital assistants, such as Siri and Alexa, or Google’s auto-complete answers when you’re typing in the search bar. Even shopping online, if you’re shown product recommendations, or used a chatbot for help – that's probably powered by AI too.

Used a maps app? AI. Any facial recognition or detection to unlock your devices? AI. Even tools such as spell check that activate when you're writing a message is AI – especially those auto-complete sentences to finish your emails. On the receiving end, spam filters use artificial intelligence to either block emails that are suspected as spam or identify an email as something your recipient would like to receive in their inbox. Anti-virus software uses machine learning as well to protect your email account.

The form of AI that has really exploded in popularity though, is Generative AI, which we’ll be focusing on here. Generative AI refers to artificial intelligence systems that can generate new content, such as text, images, and music, based on patterns learned from existing data.

Separating AI hype from reality

Nowadays, AI is often bandied around as a buzzword, often used more for its marketing appeal than its actual application. The challenge lies in separating the hype from genuinely transformative AI-powered solutions. With all the fuss around AI, but very little specificity, how do you know if you really need it as an organization?

It can be challenging to identify what you really need from Artificial Intelligence – I'd always recommend taking a practical use case that requires improvement today and ask how (and if) applying AI techniques can help solve it. It’s important to also consider whether an AI-powered solution is better for you than any of the alternatives. Just because AI has been embedded in a platform doesn’t mean it’s going to answer your use cases!

I recently spoke to a guest at a popular data and analytics conference who’d been given a brief to ‘buy AI’ for their organization. This is almost certainly the wrong way to approach Artificial Intelligence. AI should be considered an enabling toolset to achieve the broader goals of your business. If you’re seeking to implement ‘artificial intelligence’ without any outcomes in mind, then you shouldn’t be surprised if the initiative fails to deliver.

Generative AI in data use cases

Generative AI (GenAI) can truly shine, provided its strengths limitations are acknowledged. Some of the best ways we’ve seen it be implemented in the industry are using it as a tool to support humans, not to replace them. These include:

Code Generation: A time-saving tool augmented by human oversight, offering efficient solutions without entirely replacing the coder.

AI in PIM: Utilizing AI for generating compelling product descriptions in the Product Information Management realm, though still necessitating human review.

Product Categorization: Using artificial intelligence to efficiently map products to various industry standards, removing hours of time-consuming manual process.

Data Discovery and Data model generation: understanding an organization’s data landscape by interrogating data and metadata, applying semantic labels and rules to tables and fields and using this information to propose data model designs.

Data Fabric: Leveraging AI to derive insights from data & metadata repositories and using this data to improve the overall data & analytics ecosystem.

AI-Powered Data Quality: Employing AI to enhance data quality, reducing the manual effort involved in data management processes.

However, these applications come with caveats. AI's output can sometimes be based on incorrect assumptions or flawed data. Its unpredictable nature means that it may not always deliver the desired or accurate answer. The sophistication of AI necessitates a re-evaluation of how technical skills are utilized and integrated into business processes.

How to use Generative AI for your business

Understanding how to leverage Generative AI for your business starts by pinpointing specific business challenges or opportunities that are candidates for AI to address, and selecting those which balance potential ROI with feasibility of achievement. It's important to remember that success with AI isn't guaranteed; thorough testing and evaluation of AI's effectiveness within your unique scenarios are essential. For this reason, you should start small, prototype, experiment and fine tune; and be prepared to throw away prototypes if you can’t achieve the results you need. Exercise caution with products boasting 'AI-driven' capabilities and focus instead on practical applications that genuinely benefit your business operations.

Generative AI should be used to augment—not replace—human capabilities, ensuring it works in harmony with your workforce. For optimal performance, your AI systems need high-quality data, supported by detailed metadata. Lastly, as AI becomes more embedded in business processes, prioritizing ethical considerations and robust governance is crucial. To learn more, read our top 6 expert tips to understand how to use AI for your business needs.

Risks of AI

While AI brings transformative opportunities, it is often accompanied by significant risks. These include shifts in job roles, complex ethical dilemmas, challenges to intellectual property, and heightened privacy concerns, each requiring careful consideration and management to harness AI's potential responsibly.

Job Displacement

While AI can automate certain data-intensive tasks, this creates opportunities for employees to move into roles that require more complex data analysis and strategic decision-making. Effective transition management, including retraining and upskilling, is crucial to leverage AI to complement human skills, focusing on enhancing - rather than replacing - human capabilities.

Ethical Dilemmas and Bias

AI systems heavily depend on the integrity of the data they process. Ensuring the accuracy and unbiased nature of data is paramount to avoid ethical pitfalls like perpetuating stereotypes or producing misleading outcomes. Proactively addressing biases in data is vital for maintaining fairness in AI applications, particularly in data-driven decisions in hiring, lending, and other critical business operations.

Intellectual Property and Creative Rights

In the realm of data-generated content, AI challenges existing frameworks of intellectual property. Establishing clear data usage policies and creative rights guidelines is essential to protect the contributions of human creators while fostering AI-driven innovation.

Privacy and Surveillance

The extensive data processing capabilities of AI pose risks to privacy and data protection. Adherence to stringent data protection standards, such as GDPR, is necessary to ensure that AI applications respect individual privacy and maintain trust among users and stakeholders.

From Prototype to Production

When implementing AI solutions, it’s important to remember that the distance between prototype and production can be vast. It’s relatively straightforward to produce some very impressive results very quickly in a prototype, but ironing out all potential issues to ensure production readiness can be highly complex and time consuming. One popular application of generative AI is to deliver a ‘chatbot’ interface which provides expert responses based on an organization’s knowledge base. Retrieval Augmented Generation (RAG) makes developing a rapid prototype relatively simple, but we must shift the focus towards the practical aspects of deployment if we are to go into production:

  1. Data Privacy and Security: Consider the sensitivity of the information that the chatbot will access – the knowledge base, the user prompts, its responses. Implement appropriate security measures to safeguard sensitive information. Consider whether it is acceptable to use a publicly available AI service (like ChatGPT) or whether a private instance of an LLM is more suitable for your use case.
  2. Accuracy and Reliability: Ensuring that the chatbot consistently provides correct, unbiased and dependable information can be crucial, especially if the chatbot is customer-facing, or if the impact of an incorrect response could be high. Consider how ‘creative’ the chatbot should be with its answers, and how it should respond when it doesn’t know the answer to a question. Ensure the knowledge base upon which it operates is kept up to date and that the model is appropriately fine-tuned over time.
  3. Integration with your Data Ecosystem: Remember that the AI chatbot (or any other AI solution) is, in essence, just another application interacting with your data ecosystem, like an analytics dashboard or operational system. If your data ecosystem is fragmented, ungoverned, badly integrated, then you will struggle to get good results from the chatbot (or results will degrade over time), as would be the case with any other application. Invest in establishing a data ecosystem that will enable both current and future AI initiatives and pay attention to the principles of Data Fabric.
  4. Cost of Operation: Navigating the cost implication of cloud-based AI applications can be a complex area to master. Fine tuning one part of your solution can have cost implications elsewhere and you may find yourself considering the trade-off between cost and performance. Make use of the estimating tools from your cloud service provider where possible, and where not, monitor and review costs constantly and adapt where necessary. Above all, we should continuously validate that the benefit we get from our AI solution outweighs the cost of operating it.
  5. Performance: Consider the performance requirements of the solution. In our chatbot example, how essential is a fast response versus an accurate one? How will you monitor that performance falls within acceptable thresholds and take action if and when it fails to?
  6. Scalability: Planning for scalability from the outset is important as user demand and data volume may grow over time. The infrastructure and design should support easy scaling to accommodate increased loads.

Amplifi’s Three-Pronged Approach to AI Implementation

At Amplifi, we are committed to transforming promising AI prototypes into robust, real-world applications that deliver substantial value to organizations. We employ a strategic three-pronged approach to transform your innovative ideas into practical AI solutions that deliver tangible results:

  • Accelerate - We utilise AI technologies to ensure the efficient delivery of data solutions, using techniques such as data model discovery and business rule discovery. High quality, governed data is the bedrock upon which AI success is built, and AI can help us establish that bedrock too! Our approach is designed to accelerate your journey to AI readiness, ensuring swift, efficient delivery of data solutions.
  • Augment - We aim to enhance your existing data solutions (e.g. Master Data Management, Data Integration, Analytics) with AI, improving their efficiency and effectiveness on targeted use cases. Our goal is to deliver solutions which enhance productivity for our clients and help ensure an ongoing high quality data foundation. This, in turn, helps unlock future value from AI.
  • Architect - We design and implement tailored AI solutions to address specific client use cases (e.g. ‘expert ’chatbots) and, in doing so, embed scalable architectures that are both ready for the present and geared to unlock value from future AI initiatives. Each solution is crafted with a focus on driving ROI.

Our AI, data and analytics capabilities mean that we can address a vast array of use cases, many of which may not yet be on your radar. Whether you're looking to optimize existing processes or explore uncharted opportunities, our team is equipped to guide you through the complexities of AI delivery.

We've discussed various AI use cases above, but the dynamic nature of AI means there are countless other possibilities waiting to be explored. If you have an idea you want to turn into reality, read our guide here for 6 expert tips for adding business value with AI. Not sure where to begin? Our data experts can help you determine your needs and devise a strategic approach, tailored to your organization’s unique goals - get in touch.

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About the author

Mike Evans is Chief Innovation Officer at Amplifi.

He is responsible for ensuring the evolution of Amplifi’s service offering to continuously achieve excellence in delivering modern data ecosystems to our clients.

If you would like to speak with Mike about implementing AI within your organization, please fill out our contact form here.

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