Artificial intelligence (AI) disruption is inevitable. More than 80 percent of CEOs plan to implement AI into their business within the next year and previous studies show most organizations are looking at adopting AI. Organizations need to assess their readiness to embrace how AI can lead to automation of their business. In this blog series we will explore key areas of vulnerability and provide how organizations can prepare for this disruption. This first blog focuses on driving and managing change.
Assessing Change Readiness for AI & Business Automation:
The key to navigating any disruption is your organization’s ability to manage change. Established organizations may have an approved method of implementing new technology while ensuring adoption within your workforce. Smaller and more recently-established organizations may not have had to contend with a significant shift in their day-to-day operations. Preparing for change requires assessing your current tools, operations, and critical business dependencies as well as your workforce’s skills, organization, your leadership, and how talent is managed. We’ll cover each of these in more details
Tools and Skills:
“The future is already here, it’s just not evenly distributed,” is a quote from the 1984 science fiction novel Neuromancer and it likely describes your organization’s tools. Depending on your industry, the need for some digital applications may have superseded other technology that might be considered standard elsewhere. A graphic design agency may have the latest in Adobe tools and remote collaboration technology, but their finances are still done in an excel spreadsheet (and it’s the old file format from 2003). Modernizing tools to take advantage of the utility of digital assets is essential for faster, safer, and more efficient change. Parts of your organization may be completely up-to-date, but with AI you’ll need to review all tools used in your operations. This may require your staff learning new applications and developing skills beyond their current abilities.
Reviewing your organization’s digital tools may require collaboration between business objectives and IT management. It’s easy to get lost in a series of new tools and contracts without oversight over how they’ll work together or if you’re maintaining your organization’s capabilities while changing how operations are structured. Include your organization’s IT in leadership discussions about what tools you want to invest in and how to implement them with your current set-up.
You’ll also want to review your data infrastructure. More importantly, the standardization of new data. We’ve written at length how the prevalence of duplicates or out-of-date files can create problems when you plan to use an AI solution.
Leadership and Organization:
Evaluating your leadership team’s ability to drive change is essential but likely more successful if that evaluation comes from the leadership team. The goal of the evaluation is to consider if your leadership team has the diverse skill set to complete a comprehensive risk assessment and problem-solve for future technologies. Leadership positions can sometimes focus on soft skills such as interpersonal relationships, persuasion, vision, and time management. This may create a hole in your leadership’s expertise and require an outside consultant or elevating the decision-making of another person in your organization to ensure effective solutions are reached. Establish a strategic innovation conversation within your organization and identify areas of knowledge that need to be filled in. Once the team is established, conduct a risk assessment with all stakeholders to avoid fragmentation and miscommunication about priorities.
Business Models, Operations, and Potential AI Automation:
Disruption shouldn’t be perceived as “an inconvenience” or “routine maintenance.” It’s a disruption. If your work isn’t changing in some foundational way to adapt to the disruption, it is unlikely you have adapted at all.
Artificial intelligence provides the opportunity for automation of parts of your business. This could be a small-scope automation such as transcribing notes in meetings to allow for better alignment on objectives, or integrating an AI copilot to make organizational information more accessible. These types of changes may influence your internal operations, but it is also possible your business model needs to change. The degree to which AI can “replace” work is still up for discussion, but it is worth considering if some early use cases of AI suggest the technology’s future development may threaten your revenue sources. For example, a graphic design agency may choose to be more lenient on charging for minor edits considering AI generative tools allow inexperienced users to effectively modify finalized assets. While you may conclude generative AI won’t replace your core business model, it can still influence the value of your organization’s offering.
The potential of AI’s business automation can also be viewed as an opportunity. NVIDIA’s CEO Jensen Huang argues greater productivity leads to an organization taking on more projects or entering additional industries that relate to their core offering. Your organization can practice outcome-based assessments of the value of your organization and see what opportunities present themselves. This can also assist with coordinating technology investments for your organization’s planned outcomes.
Partnerships and Dependencies:
Disruption doesn’t just happen to your organization, but everyone else too — including your partnerships. The technology market has settled into a comfortable equilibrium of key providers, but organizations can’t take that comfort for granted. Currently, there are market leaders such as Microsoft Office, Google Search, Amazon Web Services, NVIDIA hardware, and etc. The magnitude of AI’s potential future suggests these leaders may fall out of their position. This is especially true for smaller-tier partners. Many organizations around the world rely on specialized businesses to resolve specific challenges, but the success of these solutions may change dramatically as AI’s disruption unfolds. Consider your own organization’s partnerships and dependencies. Organize them in a modular fashion to minimize risk to your core operations and consider implementing redundancies to ensure resilience and adaptability as AI’s potential develops.
Conclusion:
Changing your organization to adapt to disruption is difficult but crucial for navigating the future of AI. Evaluating your organization’s tools, leadership, business model, and partnerships can identify areas of improvement needed to find success with your AI strategy. It’s not necessarily the case that AI will lead to automation of all business — and it may be your industry is one of the least-affected by this disruption — but these steps will ensure your organization is prepared for the future.
Follow our blog to receive updates on the next installment of this series. Next time we’ll explore how to assess readiness and accept or manage risk in the context of AI disruption.
