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The promise of AI is real. However, as much as it is a delight for any AI practitioner to witness the transformative role of AI across industries, they also understand the challenges of AI implementation.
Yes, implementing AI is no small feat. Various factors, such as technical feasibility and data limitations, lack of skilled talent, and compliance with ethical and responsible development, can hinder success.
That’s when following the valuable lessons from early AI adopters becomes crucial. Those ahead in the AI transformation journey can offer their experiences navigating these challenges and achieving meaningful outcomes.
This is the focus of our article which highlights the common roadblocks faced during AI implementation, and how timely insights from early adopters can save you time and money leading to successful AI adoption.
Challenges in AI Implementation And Lessons from Early Adopters
Let’s start with discussing some of the most common challenges organizations face, along with the best practices of industry leaders:
Data Quality and Accessibility
We all have heard the profound statement, “Data is the lifeblood of AI.” But truth be told, several organizations still struggle with data quality and accessibility.
That raises the need for us to move beyond the theoretical understanding of “data is the engine of AI”. AI algorithms require large volumes of high-quality, diverse, and well-labeled data to result in meaningful outcomes. These outcomes drive business decisions and meet organizational growth objectives.
However, if the state of data affairs looks siloed across different departments, stored in incompatible formats, or involves inconsistencies and inaccuracies – one thing is for sure. There is a significant amount of effort required to first integrate and prepare data that can be consumed by AI applications.
Early adopters of AI are not just pioneering AI. They first build deep-rooted foundations in robust data management practices, such as data integration, governance, and security. They promote a data-centric culture and prioritize building a scalable data infrastructure to support real-time data processing, storage, and analysis.
Talent and Skills Shortage
The next challenge is finding the right talent that is underrated until an organization opens up a position. This is a harsh reality because of the shortage of specialized skills in data science, machine learning, AI engineering, and domain expertise.
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If you are wondering why is there a shortage when we keep hearing workforce redundancy news every other day. Two factors are driving the global shortage of the right candidates:
- AI implementations have become more complex in recent years, that require highly specialized and often niche expertise. From designing sophisticated algorithms to seamlessly integrating them into business workflows, building AI systems today demands more than just the basic knowledge of AI concepts. Needless to say, not everyone possesses hands-on experience with cutting-edge technologies such as LLMs, and reinforcement learning systems, or can deploy real-time AI models at scale. This gap between the demand for advanced AI skills and the available workforce creates intense competition for top-tier talent.
- Secondly, organizations, sometimes, are not clear on the right mix of skills required for executing AI projects, which leads to hiring mismatches or delays in building a high-performing team.
Organizations well-prepared to embrace AI are not just early adopters of technology but are also ahead in fostering a culture of continuous learning. They encourage teams to experiment with AI, learn from failures, and share knowledge across the organization.
High Costs and Complexities
AI initiatives are inherently complex and often chaotic in the early stages. I have seen most AI leaders share a similar sentiment that – business requirements are ambiguous and not straightforward. It requires organizations to adopt a strategic approach to mapping their business goals to the right use of technology. In cases where AI is the right approach to growing a business, leaders are immediately faced with decisions involving:
- Investments in the necessary infrastructure, such as powerful computing resources, data storage, and AI platforms
- Costs associated with hiring and training AI talent, and
- Developing and deploying AI models.
Considering such hefty investments, it is best advised to start small to test AI capabilities while proving its value to stakeholders before scaling up. This “start small, scale fast” approach allows the scope to conduct AI experiments in a controlled environment, learn from experiences, and refine strategies before committing significant resources.
Change Management
Claiming “AI Transformation” is easier said than done – for the fact that transformation means change. Yes, whenever we say, AI has redefined the ways businesses work, it essentially involves significant organizational change, including new ways of working, decision-making, and interacting with technology.
Hence, AI-ready organizations maintain a transparent line of communication with their teams discussing their concerns surrounding – fear of job displacement, lack of understanding, or skepticism about the technology’s benefits.
Ethical and Regulatory Concerns
AI development is not just costly and complex but also goes through a complex regulatory landscape. AI governance and ethical development have been a much-talked-about theme this year, especially with the introduction of the EU AI Act. Failure to ensure compliance of AI systems with relevant laws and ethical guidelines not only leads to legal implications but also causes reputational damage and loss of trust among customers and stakeholders.
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Early adopters build systems “ethical by design” implying a clear focus on ethical guidelines, bias detection mechanisms, upholding data privacy, and transparency in AI decision-making processes, right at the outset.
Integration with Existing Systems
Building AI is one thing but integrating it with existing infrastructure and business processes poses a significant challenge. Organizations must factor in the technical challenges and delays that may occur if legacy systems are not compatible with new AI technologies.
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Finally, all these insights from early adopters highlight one common trait – they do not pursue AI for the sake of innovation, or out of fear of missing out. Their approach to AI is deeply strategic offers tangible business value, and is feasible given their data, talent, and resources.
Vidhi Chugh is an AI strategist and a digital transformation leader working at the intersection of product, sciences, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, an author, and an international speaker. She is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.