Jason Foster is CEO of Cynozure and author of Data Means Business.
With the hype and innovation around artificial intelligence (AI) at an all-time high, you’d be forgiven for getting swept up in the excitement. The potential applications for individuals, businesses and governments are almost difficult to comprehend, especially when experimenting with cutting-edge co-pilot technologies like ChatGPT, Midjourney and other similar applications flooding the market.
However, the unexpected answer to this AI frenzy might actually be a counterintuitive one: not rushing headlong into the whirlwind of AI. While it might appear contrary to the prevailing sentiment, taking a step back to carefully consider your strategy for AI could be your best approach in the long run.
Understanding The Backdrop
Although Sam Altman and his team at OpenAI didn’t pioneer the field of AI, their creation, ChatGPT, has undoubtedly introduced a revolutionary tool to the masses.
AI has gone mainstream. We’re now composing music using AI, drafting complaint letters to banks and devising content plans. Our children are using it to assist with homework. The AI “genie” is out of the bottle, compelling us to contemplate the immediate, future and lasting implications of this rapidly evolving landscape.
The Essentials Of A Successful Approach To AI
As with any innovation and step change in technology, balancing exploration and a considered mid- to long-term approach allows you to test and learn as you go whilst building a plan that you can get behind. No matter what you point AI at, the essentials will put you in good stead.
1. Data Strategy
A robust data strategy is fundamental for delivering on AI’s potential. Data fuels AI algorithms, making it essential to gather, process and manage data effectively. A well-designed data strategy, tailored specifically to the objectives of the business, allows you to ensure that you have the required skills to collaborate effectively as well as the technology, culture and approach to data management necessary for success.
2. Ethical Considerations
Ethics in AI cannot be overstated. The emergence of ‘ethics by design’ is a testament to that, where processes to assure ethical considerations in the design of AI solutions are baked into its development. As AI technologies become increasingly intertwined with daily life, ensuring that these systems operate ethically and without bias has never been higher.
3. Innovation
Innovation is the heartbeat of successful AI application. Continually exploring new avenues, experimenting with novel approaches and encouraging a culture of creativity within your organization will drive AI to deliver true value.
Likewise, the conventional models of software development do not necessarily apply to AI. The dynamic nature of AI models requires a willingness to embrace new approaches, such as rapid iteration and continuous monitoring, to prevent models from veering off track.
4. Broad Education And Training
Progress can only be made if there is a proper understanding of AI—what it is and, importantly, what it isn’t. When is it appropriate to use AI and when would other approaches work? Where can it add the most value?
Equally, knowing the risks is important to ensure that as an organisation, you are aware of how to manage those risks and the potential impact those risks can have. Therefore, robust education and training should be considered from executive leadership to those building and being impacted by decisions from AI solutions.
5. Policies And Governance
AI has the potential to impact individuals and society in significant ways. Without proper guardrails and governance, there is a risk of AI being used in unethical or harmful ways.
For example, there is inherent bias in data that is used for training AI models, since it’s based on real-world data, which can lead to unfair or discriminatory outcomes. Therefore, guardrails and governance mechanisms help ensure that AI is developed and deployed responsibly, legally, transparently and ethically.
Doing things right and doing the right things are a business decision either positively or negatively impacting its stakeholders and brand.
Starting Without Overwhelming
Given these essentials, your strategy for AI may be to not dive in. But the opportunity is such that embarking on the AI journey doesn’t have to be an all-or-nothing in this way. There are pragmatic ways to get started without feeling overwhelmed:
1. Prototype and learn.
Begin with minimal viable products (MVPs) that focus on high-value, achievable use cases aligned to valuable business outcomes. Starting small allows you to experiment, iterate and learn without overextending resources. Prototyping enables you to test hypotheses, identify challenges and fine-tune your approach. Learning from these early experiences will inform the evolution of your AI strategy.
2. Collaborate with co-pilot tools.
Leverage co-pilot tools like ChatGPT as internal collaborators that allow your organization to test how AI can accelerate and improve job functions. To do this safely, set clear boundaries to prevent excessive reliance or sharing confidential data that shouldn’t be shared, and ensure that the technology enhances human capabilities.
3. Choose problems that need AI.
Choose a problem that actually needs AI. Avoid having a solution (AI) and trying to find a problem to solve. Identify problems or opportunities that your organization has and assess whether AI is the right tool for the job. This way you focus investment and prevent over-engineering costly solutions.
A Thoughtful Approach
In a landscape littered with AI promises, adopting a thoughtful and strategic approach is certainly a wise move. But, by thoughtfully integrating AI, aligning with ethical principles, fostering innovation and embracing a new model of development, you can drive meaningful transformation through the application of AI.
If you do, start small, grow steadily, lay strong foundations, test and iterate, and be open to finding new ways of building technology solutions.
In the end, you may decide that there are huge advantages to be gained from simply improving the way you manage and use data day to day and holding off on AI. But if the opportunity and value are there, then your strategy to engage with AI should be on your terms and not driven by the hype and market frenzy.
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