Harnessing Generative AI: A Roadmap for Strategic Integration

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The Critical First Step:

What Are You Doing With It?

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Before diving into the possibilities, establish whether you aim to use AI primarily for:

1. efficiency and cost reduction or;
2. explore entirely new business opportunities.

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Often the most fruitful path. Analyse existing processes to see if AI could replace or streamline tasks, yielding tangible cost savings or productivity boosts. At this relatively early stage, generative AI systems (and LLMs in particular) can be thought of as willing-but-limited junior colleagues - bearing this in mind, how could an army of such colleagues help improve efficiency within your business?

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Of course, one of the great promises of generative AI is its theoretical ability to drive innovation through large-scale data and pattern analysis, to identify new markets or product opportunities. But tread carefully! Generating innovative concepts is tempting but this requires deep understanding of both Generative AI capabilities and your own business model.

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Mapping AI Opportunities:

Processes, Data, and Human Roles

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Let’s assume you aim for efficiency gains - because that’s the most likely area for AI-integration at the time of writing (Q1 2024).  Consider your core business functions like marketing, customer service, operations, content creation, etc. Within each area, take a process-oriented view:

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Matching AI Tools to Your Needs (and Budget)

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If you’ve pinpointed potential use cases, it's time to explore solutions. Understand the differences between various Generative AI models (or third-party products built on them), their associated costs, and how they could align with your goals. Consider factors like:

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The main tools currently available have broadly-comparable functionalities which can be tailored to specific tasks; third party venders will often offer products which have been pre-specialised for specific, discrete tasks (for example, the automation of the creation and placement of banner advertising). Does a very focused solution make sense for you, or should you explore building on something more flexible?

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Beyond the Buzz:

Protecting Your Competitive Edge

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As Generative AI tools become more accessible, the risk of homogenization increases. It’s a matter of fact that, while each of the major models has its own particular strengths or idiosyncrasies, they are in many respects very similar in the quality of their outputs and the tone / style of their outputs - which means, as more businesses leverage these technologies, these similarities are likely to manifest in more homogenous outputs for business. Ask yourself these tough questions:

Does the AI unlock truly unique capabilities, or does it simply level the playing field? Will what is produced help you stand out and deliver competitive advantage, or will it just make you look, sound or behave like every other business currently jumping on the AI bandwagon?

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How seamlessly does your AI strategy support your overall business objectives and workflows?

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Managing Implementation:

Checkpoints and Responsible AI

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Generative AI is powerful, but it's not magic. Plan carefully:

Humans must remain in the loop! Gatekeeping processes are essential to catch errors or unexpected bias. It’s important to have a clear idea of how much verification will likely be required, and how long that will take - and what the cost implications of this burden will be. AI is not a ‘magic bullet’ if you need to hire three additional humans to check it’s outputs.

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Depending on the industry in which you operate, this is a potentially huge caveat - the legislative landscape around ‘fair use’ of data, about data safety and security, about the known instances of inherent bias resulting from polluted datasets…each of these is in itself a huge risk to companies building using generative AI.

While solutions are increasingly available that indemnify businesses from potential liabilities - for example, Adobe’s recent work in the space - it’s important that a full and clear assessment of the risk and regulatory landscape is conducted to avoid potential unpleasant surprises down the line.

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Before deploying a solution, define clear success metrics and track them diligently.

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While it’s relatively early days for the technology, public reaction to corporate use of generative AI has been, at best, mixed. Depending on the use case you have alighted upon, it’s important to undertake a rigorous, and honest, assessment of any potential negative reputational impact on your business should it choose to embrace these technologies to a meaningful extent.

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Even after completing your due diligence, embedding generative AI at a transformative level will likely require an agile, iterative approach over months or years before full deployment. Starting with a controlled proof-of-concept and closely measuring outputs compared to human processes is advisable before climbing up the scale.

Ultimately, generative AI holds immense potential for enterprises able to navigate the steep learning curve and unique risks.

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Methodical evaluations, calculated investments, and responsible deployment are critical for corporate leaders looking to innovate with, rather than fall victim to, the AI bubble.