Artificial Intelligence, particularly the new Generative AI, is the most significant tech development seen since the Internet emerged and transformed how we communicate. If you do not see AI as significant, you may just be missing the revolution. AI is impacting too many industry sectors, even to begin naming them.

Many of us recognize the impact of AI, but there’s often a gap that is hard to bridge, preventing us from fully implementing its value in our businesses. How can we advance our companies to effectively adopt and utilize this new technology, and what obstacles do we face?

Here are 5 Bottlenecks that may be Holding Us Back:

1) Unprepared Tech Stack – If AI is layered onto legacy systems, you are likely to hit a roadblock. For instance, Copilot might be a good direction to move for secure AI in your business, but without your key documents stored on OneDrive and SharePoint, you won’t get as much value. Copilot will use your documents as a knowledge base and is much smarter if it can find them. To make this work, you need to migrate key documents from your local file shares to the cloud.

A bit more complex example is having your line of business application accessible to AI.  This could mean having an API-ready system so that AI can integrate, or it could mean that your line of business application needs to be forward-thinking enough to build in AI interfaces.  Either way, your data needs to be accessible to AI for it to truly benefit from the riches stored there.

2) Lack of Clear Use Cases – AI enthusiasm or a concern for falling behind can often spur companies forward on their AI journey without a broad enough understanding of potential use cases for their company. AI is not a magic bullet. It requires specific goals and research to understand how to best adapt it to one’s business. It is essential to take some time to gain specific knowledge, apply it, and test AI within your environment. The quick application might not be where most of the value comes from. The default that people start with is often using AI as a “personal assistant”. There is a great deal of value here for some people. However, looking deeper to find data analytics, automation, and integration opportunities will drive more results.

3) Low Team Readiness – There are always early adopters who want to jump right into anything new, and often these people drive us forward and make us better. However, not everyone does their best with self-teaching. Getting some practical training on how to apply AI to our daily lives can improve AI value. Microsoft offers numerous free Copilot training courses that are pretty effective at getting people up to speed. Additionally, there are multiple podcasts and videos on other tools and applications in vertical markets. Learning how to prompt AI is a great way to start. Vet out the proper training or get some guidance from someone who understands how to apply AI.

4) Lack of Consistency – The absence of AI direction does not imply that the team will not utilize AI. Without direction, people will still dabble with AI and may choose to try out riskier alternatives. We have all heard of the companies that have a “no AI” policy. Interestingly, most companies with this policy don’t get what they ask for, even if they block access to AI from their network. They get shadow-AI, where people use their own devices to access the AI engine of their choice. I know of large, very security-sensitive companies with a “no AI” policy, and their team members bypass the safeguards and use it anyway. Better to invest in AI purposefully and have more control over it!

5) Fear of Misuse or Compliance Risk – Companies block AI primarily due to concerns about data leaks. The early Samsung case study, where engineers exposed trade secrets, has created genuine concern for AI’s privacy. It is good to understand the risks associated with AI and adapt accordingly. There were data leaks early on due to the lack of sophistication in the tools and how people used them. There are still leaks because people use the free tools without any awareness of the risks. All of the paid commercial tools from the major providers like OpenAI ChatGPT, Microsoft Copilot, Google Gemini, Anthropic Claude, and others have safeguards built in to prevent the LLM from using your private data for learning. Granted, you must trust the provider’s policy, but the controls are there and available for review.

Understanding the controls of these chatbots is crucial for preventing public exposure of private data, and it is equally essential to ensure that internal data security is configured correctly. This avoids an internal data leak, such as exposing payroll information to unauthorized internal team members. If a security misstep exists, it is much more likely to be found with AI when it is searching for knowledge in your internal dataset to answer questions.

Many factors bottleneck the successful use of AI. Failing to use it effectively will leave the company behind its competition. It is time to adopt a clear policy for AI usage, implement a training plan, and make sure the team is on the same page moving forward with the exponential benefits that AI produces.

CTaccess works with organizations to conduct AI Readiness Assessments and develop an AI game plan.  Please reach out if you are interested in discovering more. scotth@ctaccess.com

Scott Hirschfeld

Scott Hirschfeld is the President of CTaccess, a Brookfield IT support company that has been helping businesses stop focusing on IT and getting back to doing business since 1990. Under his leadership CTaccess provides the business minded approach of larger IT companies with the personalized touch of the smaller ones. Connect with Scott on LinkedIn.