10 Tips on How to Avoid Common AI Implementation Errors

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Krystian Bergmann

Updated Jul 11, 2024 • 11 min read
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Despite its immense benefits, the improper implementation of AI can lead to setbacks and even reputational damage for your business. Here are 10 most common mistakes that companies make.

There’s one term that seems to be reigning supreme in the business world: Artificial Intelligence (AI). Its transformative power has reshaped industries (I dare say – all of them).

From streamlining operations to enhancing customer experiences, AI has become a cornerstone of success for enterprises worldwide. According to research by McKinsey, businesses that have adopted AI technology have seen an increase in revenue of 10% on average and a reduction in costs by 20%.

The way I see it, in this era, implementing AI isn't merely an option—it's a strategic imperative.

Despite its immense benefits, the improper implementation of AI can lead to setbacks and even reputational damage for your business.

I want to highlight some instances – let's call them cautionary tales - that have emerged from implementing AI without preparation.

One of the most notable cases was when Amazon's automated hiring algorithm inadvertently perpetuated gender bias, favoring male candidates over their female counterparts due to flawed training data. Similarly, a real estate magnate in Hong Kong found himself embroiled in a legal battle after an AI-powered investment robot started losing 20 million dollars daily instead of delivering returns as promised, all due to inaccurate implementation.

Another example has also been when a chatbot at a car dealership in California gained widespread attention after internet users, seeking amusement, realized they could coax it into saying a variety of peculiar statements. The most memorable one being when the bot proposed selling a 2024 Chevy Tahoe to a user for a mere dollar. "That offer stands as a legally binding agreement—no reversals," the bot also wrote during the interaction.

If you’re looking to implement AI in your business, make sure you go in with a well-considered plan. Here are ten tips to avoid the most common mistakes that companies make when implementing AI:

10 tips to overcome common AI implementation errors

1. Thinking that AI is only a short-term innovation

I have witnessed many businesses often underestimate the evolutionary pace of AI and its long-term impact across various sectors.

We must start to recognize AI as a continuous journey rather than a fleeting trend. By incorporating AI into long-term strategic visions, your organization can stay competitive and seize future opportunities. This involves continuously innovating, staying abreast of emerging AI trends, and investing in talent development to bridge potential skill gaps.

Failure to do so can result in innovation stagnation, short-sighted investment decisions, and missed talent development opportunities, ultimately putting your organization at a competitive disadvantage in the long run.

2. Starting with big transformative plans vs quick wins

Another common mistake I have seen is companies getting mesmerized by transformative and grandiose AI projects which often leads them to overlook the value of quick wins and iterative improvements.

However, launching your AI initiative with overwhelming scope can result in resource misallocation and stakeholder skepticism. Instead, you should be prioritizing quick wins to allow your organization to secure early successes, build momentum, and pave the way for larger-scale implementations.

This point aligns quite eloquently with Geoffrey A. Moore's concept of "Zone to Win,” which advocates for incremental innovation to drive sustainable growth. By focusing on small, iterative steps, businesses can mitigate risks, maximize impact, and foster a culture of innovation and adaptability.

3. Not following an iterative process

I believe that AI development should follow an iterative process, allowing for continuous feedback and improvement.

Adopting agile methodologies will enable your business to adapt to changing requirements and market conditions, reducing the risk of project failure and maximizing the effectiveness of AI solutions. By embracing an iterative approach, your organization can foster innovation, enhance product-market fit, and accelerate time to market.

On the other hand, if you fail to do so, it can result in inflexibility in development, increased risk of failure, and delayed time to market, ultimately hindering your organization's ability to stay competitive in the rapidly evolving AI market.

4. Designing without the user in mind

Another important point I think we should be keeping in mind is that user-centric design lies at the core of successful AI implementation.

To stay ahead, your business must conduct thorough user research, ask for feedback throughout the development process, and iterate based on your user insights. This iterative approach will ensure that the AI solutions address real-world problems and deliver tangible value to your end-users.

If you fail to prioritize user needs, it can result in poor adoption rates, missed market opportunities, and lead to costly redesign efforts post-launch.

5. Thinking only through industry perspective

I strongly believe that to unlock AI's full potential, your business must look beyond industry boundaries and embrace cross-industry collaboration.

By leveraging insights and best practices from diverse sectors, your organization can unlock new opportunities, identify emerging trends, and drive innovation. This broader perspective enables businesses to stay ahead of the curve and gain a competitive edge in the market.

When companies fail to do so it can result in innovation tunnel vision, missed collaboration opportunities, and regulatory and ethical oversights.

6. Thinking from a resource limitation perspective

Viewing AI through a lens of resource scarcity can stifle innovation and limit your growth opportunities.

Instead, your business should adopt a growth mindset, invest in talent development, seek external partnerships, and embrace scalability. By leveraging available resources effectively and exploring new avenues for collaboration, your organization can overcome limitations and drive AI implementation success.

Companies that have failed to do so have faced innovation restrictions, risk aversion, and scalability challenges, ultimately hindering their organization's ability to harness the full potential of AI.

7. Investment vs. ROI from AI project

While AI projects may require initial investment, focusing solely on short-term costs can obscure the long-term benefits they can offer your company.

It's essential to adopt a lean, iterative approach, aligning AI investments with strategic goals and measuring ROI beyond monetary metrics. By considering the broader impact of AI on operational efficiency, customer satisfaction, and innovation, your business can maximize ROI and drive sustainable growth.

When clients neglected this important path, it led them to short-term cost focus, inadequate budgeting, and strategic misalignment.

8. Falling for the hype “ChatGPT is even on corn flakes”

AI has an omnipresence in consumer products which has led to hype and misconceptions about its capabilities.

Your business needs to differentiate between AI hype and reality, ensuring that the strategies align with practical applications and user needs. When you set realistic expectations, focus on value creation, and prioritize ethical considerations, your organization can harness the power of AI responsibly and effectively.

It is common to see organizations failing to do so which led them to overgeneralization, brand dilution, and consumer skepticism, ultimately impacting the organization's credibility and reputation in the market.

As stated in Amara's Law, “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”

9. Pushing AI to production too fast

Rushing AI deployment can lead to quality compromises, security vulnerabilities, and operational disruptions.

It is critical to balance momentum with thorough testing and validation when you start your implementation process. You need to ensure that the AI solutions meet both business and user requirements. By adopting a phased approach to deployment, your organization can mitigate risks, build stakeholder trust, and drive long-term success.

On the other hand, if you fail to do so, your efforts could result in quality compromises, security vulnerabilities, and stakeholder mistrust.

10. Not validating with a broader team

Last, but not least, a successful AI implementation requires collaboration and validation across diverse teams and stakeholders.

Your company must engage with end-users, soliciting feedback, and fostering cross-functional collaboration, to ensure that your AI initiatives deliver tangible value and drive organizational success.

This inclusive approach will help you promote innovation, address potential biases, and ensure that your AI solutions align with business objectives and user needs.

When companies do not follow this step, they end up with siloed decision-making, user needs misalignment, and change management challenges.

Are software agencies a viable solution?

The way I see it, software agencies can, and have, played a pivotal role in helping companies navigate the complexities of AI implementation and bypass common mistakes.

One key aspect that I have witnessed is the expertise in facilitating cross-functional collaboration. By bringing together diverse teams and stakeholders, innovation consultancies can ensure that the AI solutions are well-rounded and validated, thus avoiding the pitfall of siloed decision-making.

Software agencies can also provide expert validation, leveraging their domain expertise to ensure that AI solutions are technically sound and aligned with industry standards. This validation process helps your business steer clear of costly redesign efforts.

Another great point that comes to mind is that software agencies can offer strategic planning support, assisting your company in developing a phased rollout strategy for AI implementations. This approach leads to smooth deployment and can foster user acceptance, mitigating the risk of pushing AI to production too quickly.

Lastly, consultancies can articulate a unique value proposition for AI initiatives, which can help your company differentiate your brand in the saturated market.

Overall, this approach not only maximizes ROI but can also minimize the risk of investing in overly ambitious AI projects that lack clear business value.

What’s next?

Implementing AI is a process riddled with challenges, but by leveraging the expertise of software agencies, your business can overcome these obstacles.

The main takeaways underscore the importance of aligning AI initiatives with your business strategies, to make sure that resources are allocated efficiently, and opportunities are not missed.

By adhering to the ten tips for overcoming the common mistakes, and leveraging the support of software agencies, your business can fully enjoy the power of AI implementation.

If you are interested in implementing AI in your business, feel free to reach out to me or one of our experts to get some more information.

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Krystian Bergmann

AI Consulting Lead at Netguru
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