Your Roadmap to Successful AI integration

In part one, A Guide to AI Integration for Vision Industry Partners, we discussed the key areas that would be affected by AI integration in the eye care space. This installment creates a roadmap for our Vision Industry Partners (VIPs) to take the necessary steps for AI integration. It’s broken down into four distinct phases: Strategic Planning and Assessment; Proof of Concept; Adapt, Integrate, and Implement; and Monitor and Decide. 

Phase 1: Strategic Planning and Assessment

A structured AI strategy aligns with business objectives, ensuring smooth integration and long-term success. Before integrating AI, your company must evaluate its real needs, as well as your readiness in terms of technological infrastructure, workforce capability, and regulatory compliance.

Begin by aligning AI initiatives with the overall strategic goals of the company. What specific problems are you trying to solve? For example, do you want to reduce administrative burden, improve predictive analytics, or enhance patient experience? AI initiatives should directly support your strategic objectives. The goal is to have AI solve real business problems and contribute to measurable outcomes — not just be utilized for the sake of it.

Implementing AI into an existing company is a multifaceted process. Like any strategically important process, start with careful planning. Here is your 10+ step strategic plan.

First: Are you ready to lead?

If you are not, stop here. It will be challenging to incorporate AI into your business. As the organization’s AI CEO, you will face obstacles and unknowns at every step. There are a few key attributes that you need to consider. You need to be able to:

• Articulate the vision you are about to set forward,
• Solicit and listen to input,
• Counter misperceptions, and 
• Change your team’s opinions and lower their fear factor. 

Step 1: Evaluate your needs assessment.

Evaluate your biggest challenges and hurdles with your organization. What is causing the biggest pain points in your operational efficiency and productivity? Select workflow processes that are both critical and ripe for change. Write a comprehensive list of two to three things in each area of improvement. It is important to think big and don’t limit the list to things you “think” can happen. We will worry about feasibility in the next step. Keep this list — you will be coming back to it later.

Step 2: Narrow it down to the “achievable.

Narrow it to the top two to three things that will have the greatest impact and revolutionize the way you do business. Start with well-defined opportunities that have a high probability of success for your operational goals. Which of your existing workflows could be improved? Now pick the simplest one. Ask the following question: Can AI really fix them? Is this feasible? What will the rewards be? Remember, the goal is to be successful on your first try. Build confidence and momentum. Once you do this first one right, then you can attack the next one. Doing too much at once increases the risk of frustration and failure.

Step 3: Define clear objectives for your big goal.

This is business school 101. Take time. Do it right! Set measurable KPI goals. Do this now — prior to implementation.

These KPIs are your benchmarks to evaluate the success of your AI project. Work toward hitting those goals. Keep this in the forefront of your mind.

Step 4: Conduct cost and ROI analysis.

AI implementation can be expensive. Conduct a thorough cost-benefit analysis to determine the potential return on investment. Consider the costs of hardware, software, data storage, and talent.

Step 5: Understand your current system infrastructure and integration challenges.

Compatibility and interoperability are crucial for smooth operations. Ensure that AI models can either integrate with your existing IT systems or replace them altogether. Will you need to invest in cloud computing or other data storage solutions? You may not have these answers until you choose a vendor, but this is an important decision to keep in mind in terms of implementation and costs as you move forward.

Step 6: Select the right people.

AI requires specialized skills. Evaluate your current workforce and identify skill gaps. Is your team tech savvy and open to change if it means improving their job satisfaction? Do you have the talent to pull this off? If not, are you willing to go get it? You may need to hire data scientists or AI engineers if you are going to build and run your own AI. You will need to provide training for existing employees.

Step 7: Analyze your data estate.

Data is king and it is the lifeblood to successful AI integration and implementation. There are four important considerations relating to data.

• • Do you have access to the data necessary to fuel your AI project? Is it your data? Do you have to pay for it? Do you have rights to it? (For example, you cannot use data from an EHR unless you have contacted every patient in the database to ask permission. This is a big one for all those companies that think they can just use current EHR data.)
  • Do you have enough data? The amount of necessary depends largely on what you are trying to achieve. Similarly, do you have the data infrastructure to collect, store, manage, and access the data necessary to feed the AI. If not, does the potential vendor partner you are going to work with provide this as a service?
  • Is it quality data? AI algorithms rely on high quality data. Poor data leads to poor AI performance. Where did the data come from? Is it valid? Is it accurate? Is it clean? Is it standardized? Is it biased? How do you know?
  • Is it secure? Does your data comply with data privacy regulations such as HIPAA and GDPR? Will your data be used outside of your company? If so, what is your plan for navigating these regulatory requirements and obtaining the necessary approvals? What will your cybersecurity protocols to protect your AI systems be? Plan to implement robust cybersecurity measures to prevent data breaches and unauthorized access. Your AI vendor should help you with this!

Step 8: Plan your pilot exercise.

We all want to go big and make sweeping changes that will transform our companies. Don’t do this first. Think big. Start small. Implement AI in a controlled environment before full deployment across an industry. Collect feedback from your pilot group, and make changes before a widespread implementation push. As most of you have experienced, it takes less energy and is much easier to address big issues when they are small. Start with well-defined opportunities to use AI in ways that have a high probability of early success in keeping with your operational and customer service goals. AI projects should be selected based on each opportunity, potential value, costs, and speed of delivery.

Step 9: Build and prepare your team.

Foster a culture of collaboration and innovation. Pick people who are positive. Try to pull people from various departments to get different perspectives. Also remember these will be the people who really roll out the project to their peers. Be sure they are repeated and have the ability to change. If they don’t, teach them!

Step 10: Choose the right AI technology partner.

Research and evaluate different AI solutions and vendors that meet your specific needs. Consider factors such as accuracy, scalability, security, and integration capabilities based on the needs you established earlier. Choose partners who have a proven track record in health care. Ensure vendors comply with health care regulations and data security standards.

Phase 2: Proof of Concept (POC): Your Pilot Project

The temptation is to immediately change the workflow, be sure to start with small, well-defined projects with a high potential for success. Pick a project where AI can deliver tangible results quickly but still align with your organization’s strategic priorities. During this phase, you will validate the AI model that you are using with real-world data. You will have the opportunity to evaluate the performance of AI solutions against predefined metrics and gather feedback from clinicians and other stakeholders. Be sure to collect as much feedback as possible. Think of the pilot as a “test and learn” opportunity. See where the process could be improved and where there might be potential pitfalls with more full-scale implementation.

The pilot phase is also where your staff has the opportunity to receive training on AI concepts and applications. It is crucial to create various training sessions for staff so they can learn how to handle AI-generated information, as well as how to apply specific AI tools to the appropriate tasks. They will have the opportunity to see how AI can solve common issues they struggle with in the current system. Provide the ability to address concerns and misconceptions about AI. The goal is to empower the staff with knowledge and make them more comfortable adopting AI now and as you roll out other AI tools.

Phase 3: Adapt, Integrate, and Implement

Phase 3 involves three distinct steps: revising the AI tools with the knowledge gained from the proof of concept discussed in phase two, integrating AI into your existing workflows, and widespread implementation. 

STEP 1: Adapt. Meet with your team, take what you learned from your pilot program, and make the necessary changes for both how the AI works, what data it uses, and how to better implement it.

STEP 2: Integrate. At this step, your team will integrate the chosen AI solution into your existing workflow in a way that minimizes disruption and maximizes efficiency. The goal is to develop a seamless interface between your chosen AI tool and your existing system. If a redesign of your current workflow is necessary, take time to discuss this with all individuals that are affected by this workflow change. People are more likely to support change if they are ready for it. Show them the frustrations of the current method so they get into the right frame of mind to accept and desire change.

Keep in mind that your AI solution must be user-friendly and accessible to all on-demand or your people will simply not adapt. Take the training sessions from the POC stage and adapt them to your wider audience. Be sure to communicate the benefits of the AI observed during the POC to all staff. Openly address concerns and resistance to change. Foster a culture of continuous learning and adaptation. 

Step 3: Implement.

PHASE 4: Monitor and decide your next step

You are almost there. AI models require ongoing monitoring and maintenance. Establish a process for evaluating performance, identifying errors, and updating models to ensure they remain accurate and effective. AI is not a set-it-and-forget-it technology. Like any change, we need to constantly monitor the changes expected and evaluate the success as you scale your AI tools into your entire organization. Track the metrics you established back in the strategy and goal-setting stage. Is the AI tool changing the metrics in a positive direction? (Remember to give this a little time. It is common to see 90 days of struggle before you see improvement.) Stay true to your commitment through the good and bad. Gather feedback from everyone who uses the tool to identify areas of improvement or needs for greater training or change management discussions. Refine the AI models, as necessary. 

Now that you are a pro and have worked out the challenges of your first AI project, you are ready to tackle the rest of your “issues” list identified earlier. This time you have a proven process that you refined in your first attempt. There will be fewer obstacles and surprises, and you will enjoy a much quicker success curve. What are you going to tackle next?

Conclusion

AI has the power to transform health care by streamlining operations, enhancing product and service awareness and delivery. The secret to winning in the future will not be about adapting any given technology, including AI. Success comes from first recognizing that change is imminent and then adapting to what is coming. As with all changes, there will be leaders and laggards.

You may have heard the phrase: AI will not replace people. People using AI will replace people who don’t. This also applies to companies.  The leaders will set clear goals, invest time and resources early, and build momentum. Laggards will adopt a “wait and see” philosophy moving at traditional industry speed. When they get to their pilot period, they will spend too much time there. They will treat AI like any other technology initiative that incrementally changes efficiency rather than reimaging and reinventing how work is done. They will never catch up to the exponential growth.

You get to decide what type of company you will be or be involved with. The choice is yours! 

Load More