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In a fast-paced global marketplace where the quality of data-driven decision-making regularly separates the ‘haves’ from the ‘have nots’, the ability to keep up with technological advancements is an essential aspect of managing a successful business. With the Artificial Intelligence (AI) boom of the last few years, business leaders are also being faced with a new set of challenges and opportunities. To make the most efficient use of generative AI (gen AI) tooling while mitigating the potential risk of their use, more and more organizations are formulating organization-wide gen AI strategies to better align the technology they use with the business OKRs they ostensibly serve.
At its most basic, a gen AI strategy refers to a strategic approach that incorporates AI technologies and tools to improve operations, enhance customer experience, and drive growth. With a terrain informed by myriad forces ranging from potential budget cuts to new rulings by the Federal Trade Commission (FTC) granting them the use of compulsory measures in investigations related to the use of AI, the imperative that CXOs and other C-Suite executives take ownership of AI-driven business outcomes provides another important justification for the development of coherent AI strategies. AI must be thought of not just as a tool for automating certain manual processes, but as a market-wide disruption that will reshape businesses down to their underlying operational models.
In this blog post, we’ll explore the reasons why having a gen AI strategy is crucial for your business and how it can give you a competitive edge in the market. We will also walk you through how to build your own AI strategy to make the most out of your AI investments. Finally, we will walk you through how Hakkoda has helped one Fortune 500 Consumer Packaged Goods (CPG) company strategically leverage artificial intelligence to streamline its reporting ecosystem and improve operational efficiencies in a modern data stack centered on Snowflake.
Step 1 – understand the risks of gen AI (and how to mitigate them)
The integration of artificial intelligence into your organization’s data practices comes with numerous benefits, but it also presents unique risks that need to be addressed.
One such risk is the potential for AI systems to make errors or provide inaccurate information. Since gen AI and LLM content is the by-product of vast amounts of training material, it is also worth being proactive in addressing discriminatory output, as AI will share any biases commonly found in its source data. To combat this, businesses must have robust quality control measures in place to validate the accuracy and integrity of AI-generated outputs. This is a prime example of Garbage In, Garbage Out: if the data going into your new AI application isn’t prepped and clean, you run the risk of relying on hallucinated results. Additionally, regular monitoring and auditing of AI systems can help identify and address any errors or inconsistencies.
Data privacy and security is another critical risk associated with gen AI. For businesses that rely on sensitive customer data, ensuring its privacy and protection is paramount. Hackers can also deploy gen AI to steal sensitive information with methods like prompt injection, which allows them to remotely input code into the gen AI app as a way of tricking other users. This is especially dangerous in highly regulated industries like healthcare or financial services, where datasets contain large volumes of private information and where failure to comply with privacy and security standards can have far-reaching consequences for companies and their clients.
To combat these security risks, businesses should implement stringent security measures, including encryption, access controls, and regular security audits. They should also implement strict policies about the kinds of information that employees should and should not feed to gen AI tools so as not to violate privacy laws or otherwise share customer data without their consent. One of the main reasons for data leaks falls upon employees not being educated on which AI tool to use and what is appropriate to share. For example, you will not want your employees uploading sensitive data into ChatGPT, which trains on user input and may repeat information to other users.
Lastly, from a security perspective, choosing the right LLM is vitally important to your company’s data and required business outcomes. You need to determine if you would like to use an open-source, privately tuned LLM like a Llama2 or if you would like to leverage an expansively trained LLM like OpenAI’s GPT-4. You cannot simply ‘plug and play’ any LLM and expect it to understand your customer data and business logic. Additionally, LLM tuning and agent prompt engineering is required to privately host and manage LLM applications when dealing with sensitive data.
Developing a robust risk management approach that aligns with industry best practices is the absolute foundation of any AI strategy. By proactively addressing the validity, privacy, and security challenges attached to emerging technologies, businesses can equip themselves to mitigate potential harm and maximize the benefits of their AI integration.
Step 2 – link your gen AI strategy to business objectives
Now that you know the risks attached to AI and the importance of training models on quality data, you’re ready to talk about the operational efficiencies gen AI tooling can unlock and how you can build a strategy to unlock them.
With gen AI’s ability to quickly and accurately synthesize massive volumes of information, you can easily unlock new insights from your data, identifying trends and patterns that were previously hidden. This can enable you to make smarter, evidence-driven decisions, identify new revenue streams, and improve customer experiences. By harnessing the power of AI, you can also react to volatility in the marketplace faster than your competitors, giving you a competitive advantage. AI can be embedded within your organization’s data teams to drive innovative and cutting-edge solutions, but the success of this integration depends on initializing the correct LLM to understand your business logic.
For AI to capture valuable insights hidden in your data, it is also imperative to make strategic choices about the data it is fed. To get accurate and trusted performance while minimizing problematic inherited bias and AI hallucinations, it is essential that the data in question is clean, of high quality, and well-governed. In other words, the bedrock of a strong gen AI strategy is a strong data strategy.
Step 3 – identify how and where gen AI can help you cut costs
Business leaders already know the importance of keeping costs under control while still providing high-quality services. This is another arena in which a gen AI strategy can truly shine.
One of the main ways gen AI can help you cut costs is through process automation. AI algorithms can streamline repetitive tasks, such as data entry and analysis, freeing up valuable time and resources. By automating these manual processes, you can reduce the risk of human error and increase productivity – freeing you, your teams, and your resources to focus on more turnkey strategic initiatives.
In addition, gen AI can help you identify areas of your business that are not generating enough value. Through AI-driven analytics, you can uncover inefficiencies, redundant processes, and unnecessary expenditures. Armed with this knowledge, you can make informed decisions on where to make adjustments and optimize your operations. For an automation initiative to be successful, however, you must evaluate which existing processes have the greatest associated costs and which can be automated most effectively, then build your AI strategy accordingly.
Gen AI strategy in motion – a case study
Consider the case of a leading Fortune 500 Consumer Packaged Goods (CPG) company grappling with around 650,000 reports and the resulting data sprawl in their Oracle Discoverer system. Thousands of overlapping and underutilized reports quickly turned into inefficiencies across the various departments – bringing commensurate costs to its bottom line.
The company came to Hakkoda intending to migrate to a modern reporting system based on Snowflake and Sigma. However, the overwhelming number of reports and the manual effort needed to analyze them made this transition seem too daunting and time-consuming to even consider.
Our AI team needed a revolutionary solution to help the company out of this predicament and set out on a journey of meticulous data preparation and AI agent training on the internal business logic to develop one. These efforts culminated in the creation of an easy-to-use application based on Streamlit, a low-code API that provides the UI for Snowflake-native apps. This application went on to become the cornerstone of our consulting strategy for the client – enabling deep insights into reporting trends, key metrics, and consolidation opportunities.
This AI-driven process, which would have taken months or years if done manually, could also now be completed in mere minutes – representing an astounding improvement in efficiency of up to 3,000 times. This drastic reduction in time equated to substantial cost savings for the company, streamlining its operations, laying the groundwork for a more efficient reporting ecosystem, and freeing up resources for other strategic initiatives.
Key takeaways – future-proofing with your gen AI strategy
As the example above so powerfully illustrates, gen AI can be a tool for increasing productivity, cutting costs, and automating time-consuming tasks. But it is also important that organizations are deliberate about its use to mitigate risk and deliver real value on their investments. This means approaching AI integration strategically and accounting for each of the core tenets touched on in the article above:
- Understanding the risks of gen AI and how you plan to address them at the organizational level. This includes taking precautionary measures to protect sensitive data and audit AI outputs for accuracy and completeness.
- Ensuring the quality, completeness, and governance of your data before training your AI model, which will allow you to optimize your business more effectively. Garbage In, Garbage Out, or GIGO, is a truism of all data practices, but this is especially true of AI implementations.
- Determining which existing data processes can be automated most easily, and which currently have the greatest time and human resources burdens attached. By taking a full inventory of existing processes, you can steer your AI efforts toward greater operational efficiency.
With these three strategic pillars in place, you are ready to begin your AI business integration while also ensuring that it is responsible, efficient, scalable — and yes, profitable. For businesses that don’t quite know where to begin with the principles above, you may also consider working with a trusted AI consultancy, who can walk you through development of your enterprise AI strategy in depth. Hakkoda’s gen AI consulting team, for example, has developed a quick-start roadmap to help our clients through the early phases of their respective AI projects.
Ultimately, the path to a successful AI integration looks different for every business. The possibilities for innovation, however, are virtually limitless once the right strategy is in place.
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