In a 2024 survey by McKinsey, 65% of respondents said their organizations are already using generative AI on a regular basis. But just because AI is everywhere, that doesn’t mean businesses are leveraging it effectively.
To ride the AI wave towards increased efficiency and improved results, business leaders need to understand how best to implement new technologies across their tech stacks. Intelisys Sales Partners can give them that knowledge – closing deals and building relationships in the process.
In order to implement an AI enterprise solutions, organizations must have 4 key components:
- Data. This could consist of a structured database or unstructured files. In either case, the AI system will draw on data to perform its task – which makes data quality crucial.
- A machine learning (AI) system. The AI application itself typically runs on Azure, AWS, or another platform developed by a third party.
- Compute power. AI systems require a lot of power, especially graphics processing units (GPUs).
- Security and governance. By enacting a sound AI policy and hardening cybersecurity, organizations increase the possibility of a successful enterprise AI implementation.
Vertical-Specific Use Cases for Enterprise AI
Across verticals, organizations are most commonly using AI for contact center optimization. That said, it’s important to remember that AI is far more than a contact center tool, and it can help a business with more than just customer experience.
Here’s how businesses in some key verticals are leveraging enterprise AI:
- Retail. Along with contact center optimization, retail businesses are using enterprise AI to manage inventory, optimize pricing, and even forecast demand.
- Manufacturing. AI can automate supply chain management, quality checks, and many elements of production.
- Healthcare. By quickly and accurately analyzing large data sets, machine learning systems can diagnose diseases, develop treatment plans, and help doctors make decisions.
- Banking and finance. Banks and financial institutions use AI for research, financial modeling, risk management, tax compliance, and more.
- Shipping and logistics. AI can help predict maintenance needs, process orders, manage inventories, and optimize routes.
If you have a client in one of these verticals who isn’t using enterprise AI, mentioning a potential use case is a great way to start the AI conversation.
Building Your AI Roadmap: A Practical Framework
In the midst of the AI explosion, business leaders may be tempted to implement solutions without a plan or strategy. But for enterprise AI implementation to be effective, it’s best to proceed with deliberation and purpose. That’s why Sales Partners should encourage their clients to follow a basic AI roadmap.
Most organizations want to adopt AI for one of 2 main reasons:
- To automate systems
- To increase workforce efficiency
By following the roadmap described below, businesses can achieve these goals while mitigating cybersecurity risks.
Step-by-Step Approach
Incorporating AI into an organization’s tech stacks is a complicated endeavor – and as such, it’s best accomplished in stages. While every business will have its own AI implementation journey, here’s a breakdown of the common steps to take:
- Step 1: Define objectives. Common goals include increasing efficiency, driving innovation, improving customer experience, and increasing revenue. Be sure to establish clear metrics that will define success.
- Step 2: Organize data. AI models, especially machine learning systems, generally leverage client-owned data. That’s why organizations should consider how they store and label their data before implementing enterprise AI. They should also make sure their data is relevant, high-quality, and up-to-date. For example, companies don’t want a chatbot to train on data that relates to discontinued products or services.
- Step 3: Build a team. Limited access to talent is one of the biggest barriers businesses face when trying to implement AI. As a Sales Partner, you can help solve this problem – partly with your own expertise, and partly by finding the right AI solutions that offer assistance with implementation.
- Step 4: Implement the AI solution. This should be a collaborative process, likely accompanied by AI training across teams. Organizations need to be prepared for both the technical and business-related impacts of implementing an AI system.
- Step 5: Monitor, maintain, and measure. Organizations should monitor their AI systems for functionality and keep them properly maintained – which often requires updates and adjustments. They should also measure how the AI solutions have impacted key metrics related to the initial objectives defined in Step 1.
As an Intelisys Sales Partner, you can increase your value to your clients by walking them through each of these steps.
Risk Management Strategies
Risk management is a crucial component of any responsible AI implementation plan. Without proper precautions, an AI system could lead to costly data loss and cybersecurity vulnerabilities.
Here are 2 risk management strategies to employ:
- Data loss prevention (DLP). Many organizations store sensitive data like credit card information or social security numbers. If an AI system accesses that data, it could release it. To prevent this from happening, organizations should apply sensitivity tags to the types of data they want to protect, and then configure the AI system to avoid querying data containing those tags.
- Cybersecurity hardening. This classic strategy outdates the recent AI explosion – but it’s especially important when AI enters an organization’s IT systems. Permissions need to be properly configured before an AI system is introduced, and those permissions must be maintained on user accounts, folders, repositories, databases, and applications. Otherwise, the AI could access sensitive data and start sending it places where it doesn’t belong.
Sales Partners can earn trust with clients by surfacing these security-related concerns in discussions about AI implementation.
Ready to Monetize AI? Watch the SE Webinar
2024 saw mass AI implementation – and the revolution is just beginning. For Intelisys Sales Partners, it’s time to cash in on the new technologies flooding the IT marketplace.
But what exactly should you be selling? What vertical-specific use cases should you be aware of? Listen as Intelisys solutions engineers Bryan Nelson and Tyson Crosby discuss all that and more on our SE Webinar (titled “Monetizing AI”) – now available in Intelisys University.
