Computer vision is a foundational field of artificial intelligence (AI), largely applicable when tracking and managing tangible objects with precision in real time. It’s proven particularly effective in areas where human oversight ends up being more costly and time-consuming, such as defect detection and object counting.
However, if you work on the technical side of things, you already know this. And while product and engineering teams can easily recognize the necessity of computer vision solutions, securing executive buy-in is often the bigger challenge.
In this blog, we’ll cover the strategic approach we take with customers to build a compelling case for enterprise computer vision. We’ll examine executives’ priorities, the business value of computer vision solutions, and the surefire way to drive home your solution to management’s key concerns.
Step One: Understanding the C-Suite Mindset
Executives make their decisions based on ROI, risk mitigation, and strategic impact. While it can be tempting to present computer vision technical specs front and center, your pitch should center around outcomes.
To start, we suggest shifting perspective from how computer vision works, to instead considering how it provides impacts in terms of efficiency, revenue, and competitive edge. To get the ball rolling, we quantify the impact on the business with the following questions:
- How much time and manual effort are currently spent on tasks that could be automated?
- What specific inefficiencies in our workflows could be eliminated?
- How can computer vision help us scale operations without increasing headcount or costs?
- What missed revenue opportunities could be captured using this technology?
- How does our competitors’ use (or lack) of intelligent solutions position us in the market?
- What tangible impact can automated visual intelligence have on compliance and risk reduction in our industry?
- How will implementing computer vision directly improve customer experience and satisfaction?
- What is the expected payback period for our investment in computer vision? And what are our target KPIs?
- Which teams or departments would benefit the most from visual analysis, and how would it influence their productivity?
- What existing systems and data sources can be integrated with computer vision infrastructure for maximum value?
Step Two: Identifying a Relevant Business Case
Without a clear use case in mind, computer vision technologies can quickly become a costly experiment rather than a strategic asset to your business. Your C-Suite does not care about vague promises of business transformation through AI.
First, you’ll need to think critically about the pain points your project faces. Some common challenges we assist customers with include inefficiencies, labor shortages, and compliance risks.
Next, consider how solving these challenges would align with business goals. How does utilizing a computer vision solution tie directly to revenue, cost savings, focus KPIs, risk mitigation, and/or growth?
Following this, do you believe that you have the executive buy-in, visual data sources, and infrastructure to implement this project at scale?
And finally, quantify the impact of the use case. What KPIs will measure success and how soon will ROI be realized? Case studies and financial projections augment proof points to help show that computer vision AI will put your project ahead of industry standards. Thus, solidifying your case.
Step Three: Addressing Common Concerns
Hesitation is inevitable. Even execs need some sort of reassurance that they’re making the right decision and may ask the following questions:
How can we justify the cost of computer vision?
Our customers have been successful when they position computer vision as an investment, not an expense. While many are still skeptical of AI’s true ROI, you can convey how computer vision’s broad market applicability results in long-term savings and a competitive advantage.
This could look like presenting the stats, potential outcomes, and improvements. Showing them what could be!
Isn’t implementation super complex and resource-intensive?
Computer vision platforms, such as Viso Suite, minimize IT requirements by both solving immediate challenges and ensuring that there is a platform that can also take on future needs. This reduces the complexity of implementation and scaling.
How could computer vision integrate with our existing systems?
Modern AI solutions are designed for interoperability with APIs and cloud-based integrations. These connect to your current workflows and omit the need for major overhauls and engineering work.
What about security and compliance risks?
To justify answering this question, you can ensure that you choose a platform that remains under your complete control and operates above board. With platforms that implement industry best practices, computer vision solution automatically meets GDPR, HIPAA, and other security and compliance standards.
How long until we see ROI?
Again, you should consider quantifying the impact. You can cherry-pick relevant case studies and pilot projects similar to yours. These should balance quick wins while paving the way for long-term success.
Step Four: Implementing Your Plan
You’re going to show that as soon as you are given the green light, you’re ready to go.
- Pilot to Validate Impact: this shows that you have thought about how to minimize risk before committing to full deployment. You can refine details and tech specs before scaling up the application.
- Measure Results to Prove ROI Early: here, you’ll refocus from tech spec to business impact. The KPIs to examine here can include accuracy and performance, efficiency gains, cost savings, and risk reduction.
- Scale to Build a Long-Term Deployment Strategy: once you’ve been able to prove tangible business value with the pilot, you can think about scaling adoption to other use cases and parts of the organization. Think about cross-departmental collaboration and further iterative improvements here.
Step Five: Building Internal Support
While securing exec buy-in is an important milestone, the true success of computer vision implementation comes from cross-departmental support.
Identifying Key Stakeholders
Your colleagues will deal with different parts of the computer vision solution, so it’s important to involve the right people at the right time.
- Operations & Engineering will be responsible for deployment and ongoing optimization
- IT & Security Teams will ensure system compatibility and data protection
- End Users & Frontline Employees will work with the technology daily
They might need help understanding how computer vision improves their workflows and helps them meet their respective targets.
Addressing Concerns & Resistance Proactively
You need to be prepared for some pushback, which is normal. Such as the obvious AI is taking our jobs. Clarify how automation enhances roles rather than replacing them.
Your colleagues could also have objections about how difficult pulling off something like this will be. You can provide materials that demonstrate successful, low-disruption implementations. You’ll also need to repeat how IT and security risks are carefully addressed with compliance and data protection measures.
Managing Cross-Departmental Collaboration
Adoption is much smoother when teams feel involved in the process. Encourage:
- Early pilot program participation to demonstrate practical value.
- Regular feedback loops to refine implementation based on real user experiences.
- Workshops or demos to help teams see computer vision in action.
Proceeding With Enterprise-Grade Computer Vision
In case you didn’t hear us screaming from the rooftops, you need to quantify the business impact at every stage in the computer vision application!
While it is true that computer vision has become a mainstream business solution, many execs still need the extra nudge in the right direction. Numbers, figures, and case studies are going to be your number one advocate on your quest for achieving the buy-in you need.
We’ve developed Viso Suite as an industry-agnostic computer vision solution. Viso Suite works with enterprise teams to develop, deploy, manage, and scale computer vision solutions. To learn more about how we can work with you on a full-scale implementation, get in touch with our team of experts.
