Is It Too Late for My Organization to Leverage AI?
The good news is that it’s not too late to start leveraging AI -- but the clock is ticking.
- By Brett Hansen
- March 11, 2024
Business leaders have spent years inundated with news about emerging AI applications. As innovations such as generative AI and large-language models (LLMs) become more popular, think pieces about the death of manual work become increasingly common. In this fast-paced environment, many business leaders are asking themselves: Is it too late for my organization to leverage AI competitively?
The short answer is no, but a pragmatic approach to adopting AI is becoming increasingly valuable.
AI Excitement Remains in Its Infancy
The 2023 Gartner Hype Cycle placed generative AI at the “peak of inflated expectations,” beyond the technology’s initial trigger point but before the trough of disillusionment (during which AI excitement may temporarily fade). That’s important because early applications of AI are not always realistic, and enterprise AI isn’t ubiquitous quite yet. According to Altair, most organizations haven’t even implemented large-scale AI. Over one-third (36%) of business leaders expect to adopt AI within the next six months; another 23% expect to do so over the next year.
The AI runway remains open. However, leaders looking to capitalize on AI must prioritize responsible AI use and robust data management as soon as possible.
Laying the Groundwork for Responsible AI
Unregulated AI exposes organizations to several risks, including:
- Ethical concerns, such as bias and discrimination
- Security risks, including potential data breaches and insecure cyber protections
- Accountability problems, especially if AI stakeholders aren’t aligned on the technology’s use cases or desired impact
According to McKinsey, only 32% of organizations have attempted to mitigate AI inaccuracies, and just 28% are addressing cybersecurity risks.This oversight is more than a missed opportunity -- it threatens business continuity. Stats such as these may explain why nearly half (42%) of respondents to the Altair survey experienced failed AI implementations over the last two years.
These potential drawbacks shouldn’t dissuade leaders from implementing AI altogether. After all, the risks of not implementing AI are also severe, with high-performing AI implementations capable of improving an individual worker’s productivity by 40%. This boost gives organizations a competitive advantage, especially in a business environment plagued by labor shortages.
The key to efficient AI implementation is caution and planning. Leaders must assess their enterprise’s organizational, operational, and business challenges and use those findings to guide an intelligent AI strategy.
- Organizationally, successful AI implementation requires interdepartmental collaboration and training. Stakeholders -- including leaders and the daily drivers of productivity -- should understand the benefits of AI implementation. Otherwise, employee anxieties or misinformation might impede progress.
- Operational challenges to AI deployment include inefficient manual processes and a lack of standardization. Remember, AI is not a silver bullet for resolving existing tech inefficiencies. Before implementation, leaders must assess their tech stack, ensuring that all relevant software is in conversation with one another.
- From a business perspective, unclear AI use cases are a recipe for disaster. AI and machine learning (ML) investments should have specific KPIs. Furthermore, all investments should take a phased approach that prioritizes a solid data foundation before deployment.
Managing Data to Enable AI Success
High-quality data engenders organizational trust and enables better decision-making. According to IDC research, trustworthy data also facilitates better customer satisfaction and innovation, leading to benefits such as a potential 29% increase in revenue and 31% reduction in operational costs.
Good data management practices -- including but not limited to regular audits, master data management (MDM) solutions, and workplace training -- can significantly improve AI outcomes. When provided with accurate, timely, and comprehensive data, AI systems draw better conclusions and suggest more advantageous organizational improvements. Conversely, AI systems relying on inaccurate data can make misleading suggestions during critical business decisions.
A high-quality data repository should be centrally located and non-duplicative. In other words, all stakeholders should be able to access relevant information quickly, and data shouldn’t have unnecessary copies (excluding security-related backups). In addition to the expense of storing duplicate data, multiple (and scattered) versions can confuse and overtax AI systems -- not to mention human workers.
The Future Is Artificial
Humans will remain the primary drivers of organizational outcomes, but there’s no question the future of business includes AI co-working. With more executives turning to AI to solve big-ticket problems such as inefficiency and labor shortages, the message for holdouts is clear: there’s no better time to start drafting an AI plan than today. Luckily, with the right tech stack, attitude, and data management strategy, leaders who start now position themselves to achieve tangible and remarkable results over the next two years.
About the Author
Brett Hansen is the CGO of Semarchy where he is responsible for go-to-market operations, including marketing, business development, and alliances and partnerships. Before joining Semarchy, he was the CMO at Logi Analytics, which was acquired by Insight Software. He spent 11 years at Dell as an executive, leading software product and GTM in Dell Client Group, and before that worked at IBM in a variety of marketing and channel leadership positions. You can reach the author via email or via LinkedIn.