49 3. Find the Right Methods and Technologies
The world of artifi cial intelligence is complex and deeply technical. Successfully leveraging AI requires navigating this complex landscape and understanding the best technologies and techniques for your use cases.
We recommend collaborating with a knowledgeable partner who can guide you through the intricacies of relevant technologies, models, methodologies, and languages. Look for a partner with technical expertise in AI and analytics, complemented by a comprehensive understanding of your specifi c industry and workfl ows. A partner familiar with the distinctive instruments, processes, use cases, and - most importantly - business objectives in your industry can make all the difference to your lab’s success with AI. The right partner can also ensure your data quality, privacy, and security protocols meet necessary standards.
4. Incorporate AI into Operational Processes
Identify the hotspots within your lab’s workfl ow that would see the most benefi t from the addition of AI. Explore avenues to seamlessly integrate AI models into your workfl ow by embedding them into existing software and tools or creating new, complementary interfaces. Additionally, prioritise optimising the human-machine interface by selecting platforms with user-friendly interfaces and dashboards.
5. Foster a Culture that Embraces AI
The real challenge is often not one of technology but of the humans operating it. There is signifi cant cultural resistance to AI, even among scientifi c circles. This resistance looks a lot like the resistance we’ve seen with other technologies, including the computers, smartphones, and internet that we can seemingly no longer live without.
Concerns about AI potentially replacing human tasks in laboratories undoubtedly lead to apprehension in the workplace. Encourage an open, collaborative culture that educates employees about the opportunities presented by this technology and reassures them of their ongoing value in the workplace.
Integrating AI also requires a mindset shift on behalf of management and employees. Labs will reap the true benefi ts of AI only when they focus on upskilling teams and fostering a complementary relationship between software workers and human workers. And with AI taking care of routine tasks, team members can shift their focus to more strategic work.
Ultimately, True Success
Begins with Commitment The companies that see real success with AI are committed from day one. While many laboratories are enticed by the promises of new technology, they often underestimate the dedication required to realise long-term benefi ts.
Labs that approach AI with a ‘testing’ mindset often end up not just abandoning AI, but in a worse position than when they started, having invested signifi cant time and resources into an ‘experiment’ that didn’t go as planned.
Rather than ‘testing’ out the technology with language like ‘playing around with it’ and ‘building a prototype’, we recommend fully committing, initially with small achievable use cases and building from there. Why? Approaching AI through an explorative lens rather than a commitment lens sets the stage for potential failure. Profi ciency with AI demands a development mentality. Instead of merely ‘playing around’ with the technology, ask and answer:
• What does success look like? • What’s my plan for achieving this success? • What resources will I need? • How will I deploy this into production? • How will this technology fi t into my business plans and processes? • How do I deploy this technology into production effectively? • Can I fi nd a partner with the right resources?
With the right mindset, a commitment to seeing it through, and an expert scientifi c data partner by your side, any lab can reap the enormous benefi ts of our century’s defi ning technology.
References
1.
https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-survey-the- state-of-ai-in-2021
2.
https://www.gartner.com/en/newsroom/press-releases/2018-02-13-gartner-says- nearly-half-of-cios-are-planning-to-deploy-artifi cial-intelligence
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