Three questions enterprise leaders are asking about AI
Many leaders have been extremely aggressive about their AI strategies this year. However, despite the buzz generated by these technologies, most leaders are still scratching their heads trying to brainstorm real, practical ways to implement AI.
I’ve identified three common questions from leaders about AI for the enterprise and answered them with pragmatic guidance on the steps they can take today to make the most of these innovative technologies.
- My organization has yet to actualize an AI strategy. Is it too late?
It may seem like every enterprise has an established AI plan, but in reality, most organizations are still working on perfecting their data strategy. In fact, according to a recent report by Altair, 42 percent of organizations are still trying to start establishing a data strategy, and 35 percent are just now piloting their initial use cases.
Establishing a data strategy is an integral first step in the process of implementing AI within your organization. After all, AI models are only as good as the data used to train them. Poor data quality leads to low-quality outputs and insights. A strong data strategy ensures processes are in place to continually capture, clean and organize data to feed into AI.
Are you still worried about being behind when it comes to AI? Don’t be (yet), but don’t drag your feet, either. Most organizations are in the planning stages of AI deployment, but testing and rollouts will start soon. In the next year, 59 percent of organizations will start implementing AI for larger-scale projects. To remain competitive, it’s critical for enterprise leaders to act sooner rather than later and build a comprehensive data strategy to lay the groundwork for AI implementation. It’s not too late!
- If my organization overlooks AI risks, what’s the worst that could happen?
As with all new technology, implementing AI solutions in your organization comes with risks, and sometimes AI fails -- in fact, 42 percent of organizations have experienced AI failure in the last two years. Negative impacts of AI failure include misdirected resources, wasted money, compromised cybersecurity and damaged organizational reputation.
So, where did these organizations go wrong with their AI strategies? Most likely, they skipped the aforementioned first step and failed to build a foundational data strategy, meaning AI’s data inputs were less than ideal. The result? Subpar and even downright wrong outputs.
Mitigating the risks of AI is necessary for enterprises looking to profit from the tech. Slowing down to put protections in place won’t leave you behind your peers -- in fact, it may even give you a competitive advantage since only 21 percent of organizations have AI governance policies in place. Fast doesn’t always mean good, and thoughtfully considering the risks associated with AI and acting accordingly to prevent problems will give you a leg up on the competition.
- AI implementation comes with risks and stumbling blocks. What are some best practices to handle potential pitfalls?
The challenges that come with AI implementation are not insurmountable. Follow these best practices for each type of hurdle:
- Organizational challenges. AI solutions will not exist in a vacuum within your organization. Interdepartmental cooperation and collaboration are necessary for the success of AI tools and integrations. Accordingly, it’s crucial to have a diverse team with an array of skills and expertise to draw from to quickly solve problems and generate innovative ideas for how to use the tech.
- Operational challenges. Manual processes and a lack of standardization between AI training environments and production environments can make AI implementation messy. The smartest organizations will work to standardize their tech stack and automate processes, reducing the possibility of human error and regulating tool use.
- Business challenges. Amidst all the hype, it may be difficult for leaders to determine the actual ROI of an AI solution. To speed up time to value, organizations must begin with a strong data foundation and gradually roll out AI implementation in stages.
These best practices may seem straightforward, but enacting them will take considerable effort and coordination across the enterprise. Trust me, that effort will be worth it when your AI solution is up and running smoothly.
The hype surrounding AI shows no signs of waning, but don’t let that hype stop you from implementing thoughtful guardrails and expectations around implementation. Understanding how to deploy and manage AI solutions will ensure your organization stands out from the crowd. And, remember: It’s never too late to harness the power of AI, as long as you do so cautiously, strategically and optimistically.
Brett Hansen, is chief growth officer at Semarchy. 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 eleven years at Dell as an executive leading software product and GTM in Dell Client Group, and prior was with IBM in various marketing and channel leadership positions.