Before we break into the potential of AI and how companies can harvest it, we have a story to tell.
Once, a mid-sized SaaS company was known for its innovative solutions and drive. Still, they were eager to stay ahead of the competition. As a result, they hastily decided to integrate AI into their marketing strategies to automate customer targeting and personalized content creation.
However, chaos resulted in uncleaned and disorganized data, leading to unreliable predictions and ineffective customer targeting. Ethical concerns were brushed aside, causing unintended bias and ethical violations in their AI algorithms.
Rushed AI model deployment resulted in numerous technical glitches, and their AI-driven marketing campaigns triggered a severe customer backlash. Legal actions and penalties followed, damaging the brand’s reputation and causing severe financial strain. What was supposed to be an AI-assisted boost cost a lot — in time, money, and effort.
The fairytale turned into a nightmare very quickly.
This serves as a cautionary tale in the business world about the perils of adopting AI without setting the stage for its success.
But how does one ensure the successful implementation of AI technology?
Here are four crucial steps your company should consider before embracing AI technology.
1. Nail the business case
Before diving into the world of AI, identify your objectives. It may sound basic, yet many companies plunge into AI endeavors without a clear understanding of the problems they’re trying to solve or the questions they are trying to answer.
Sometimes, the solution you seek may already be there in your existing framework. If so, don’t jump on the AI bandwagon just for the sake of it. Conversely, the problem you are trying to solve must be simplified for any AI solution.
The key to success here is to target AI applications with achievable outcomes that align with the current state of technology.
2. Build a strong and lasting foundation
Before diving headfirst into AI implementation, ensure a solid foundation is in place. This entails three key points:
- Improve data quality: Assessing your organization’s data quality and making adjustments to ensure it’s high-quality and clean.
- Evaluate and shift the organizational mindset: Educating leaders at all levels and functional teams about AI’s significance and implementation.
- Build your AI team: Upskilling and training existing employees about AI, hiring new talent, or partnering with AI service providers to build a diverse and skilled AI team.
Once you have laid the groundwork for AI in your business, the next step is to nurture AI models over time to ensure the foundation lasts. It requires you to feed AI models with accurate production data continuously, retrain them regularly, and meticulously monitor for bias to ensure model quality.
3. Recognize that AI projects are IT ventures
Companies often make the mistake of treating AI as distinct from other IT ventures. The reality is that AI should be subject to the same management processes employed in IT.
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Like other IT projects, assess AI’s potential return on investment. Conduct financial modeling to understand the operating costs of deploying an AI model.
4. Bridge the data science-business divide
The goal is to integrate machine learning into your production environment to achieve business objectives. Therefore, it’s essential to understand your business needs and align data scientists with them. Establish trust between data science and business units by transitioning from experiments to reliable, repeatable model deployments.
Lastly, AI doesn’t deploy itself. It’s still fundamentally an IT project, necessitating adherence to best practices for managing projects, as outlined below:
- Define scope, goals, and KPIs.
- Conduct regular meetings to coordinate and monitor project progress.
- Cultivate organizational understanding and alignment.
- Regularly communicate progress to broader audiences and corporate management.
The final word
Organizations that embrace and internalize these principles can unlock the full potential of their AI models and avoid wasting resources in finding the right solution. Those at the onset of their AI journey may require strategic planning, preparation, and commitment to ensure their AI strategies are built for long-term success.
Want to know more about data-driven tech? Since 2006, Escalon has helped thousands of startups get off the ground with our back-office solutions for accounting, bookkeeping, taxes, HR, payroll, insurance, and recruiting — and we can help yours, too. Talk to an expert today.
This material has been prepared for informational purposes only. Escalon and its affiliates are not providing tax, legal or accounting advice in this article. If you would like to engage with Escalon, please contact us here.
