If you want to maximize the potential of artificial intelligence, you need the right strategy.
In the first blog of our series, we talked about [the basics of AI technology]. Next, we’ll discuss how you can lay the groundwork for a successful deployment.
It’s a topic we already briefly touched on when we covered do’s and dont’s of employing AI in the workforce — but today, we’ll be covering it in greater depth.
5-Steps to Building a Strategic Foundation
The first question you need to answer is simple: Why?
You can’t adopt AI simply because everyone else is doing it. You need to think about which specific areas of your business will benefit from AI integration. You also need to consider how you’ll determine whether your implementation was successful.
The steps below will help you get started
1. Start Small, Then Scale
One of the biggest mistakes you can make with AI is trying to do too much right out of the gate. Approach implementation as a tiered process. Target limited-scope, high-value applications first, then expand from there.
2. Consider Where You Won’t Implement AI
Always remember that artificial intelligence is meant to augment human intelligence, not replace it. There are certain tasks that AI cannot do, and certain processes where it’ll be a hindrance rather than a help.
Some examples include:
- Developing and maintaining strategic partnerships
- Customer advocacy
- Policy planning and implementation
- Network implementation planning
- AI ethics and governance
- Marketing and outreach
Most of the above tasks can benefit from AI integration, but all of them still require human employees.
3. Gather Feedback
The best way to figure out what AI can do for your business is to ask people what they want from AI. Focus on the following:
- Pain points that AI could potentially address
- Automation opportunities
- A wishlist of AI features
- Goals and objectives that AI might support
Once you’ve done that, identify which departments and use cases have the greatest value for your business. Generally, the easiest way to do this is by considering how AI might impact each department’s key performance indicators.
4. Be SMART
Identify one or two key objectives associated with each of your high-value use cases, then apply the SMART framework to make them into actionable goals. Let’s say, for example, you want to use AI to help your accounting team operate more efficiently:
- Specific: Reduce manual data entry for accounting team with automation.
- Measurable: Save ten hours per employee per week.
- Achievable: Find, test, and implement an AI assistant for accounting within six months.
- Relevant: Free accounting team to focus more attention on budget optimization and long-term planning.
- Time-Bound: Assign specific target completion dates to vendor selection, POC, training, and implementation.
5. Think About Your Long-Term Plan
Once you’ve established the SMART goals for each potential initiative, map each to your overarching business objectives. Make sure you’re able to both quantify and explain the value that AI can deliver — this will be essential in securing leadership buy-in.
Ensuring Cultural Alignment
Once you’ve established the strategic groundwork for your implementation, the next step is stakeholder involvement.
Assuming you haven’t already done-so, your first step is to secure leadership buy-in. Using the strategic materials you’ve developed, engage your leadership team and provide them with a thorough explanation on why and how AI will benefit your organization. You may also want to appoint someone whose full-time job is overseeing AI implementation.
From there, your next steps depend largely on what types of AI solutions you’re looking to implement. Some general best practices include:
- Assessing skill gaps in areas such as machine learning, data analysis, and AI utilization
- Identifying role-specific training that might be required for successful implementation
- Clearly communicating that AI will empower rather than replace people
- Support a culture of continuous learning with professional development opportunities
- Develop clear guidelines around responsible and acceptable AI usage
Getting Your Infrastructure Ready
Generative AI tends to be incredibly resource-intensive, with bandwidth, computing, and power demands well beyond what traditional infrastructure can support. Before implementing any AI solutions, it’s in your best interests to perform a thorough assessment of the following.
Network
If you’re using AI services based in the cloud rather than on-premises, you’re still going to need high-bandwidth, low-latency hardware to ensure optimal performance. You’ll also want to measure both the utilization and reliability of your current network.
If you’re training and operating your own AI model, you’ll want all of that while also accounting for the fact that a basic AI model may require upwards of 400G connectivity.
Computing Hardware
Comparing an AI workload to that of a standard application is sort of like comparing a commercial airliner to a mid-sized sedan. The former requires immensely more computing resources than the latter. In broad strokes, this typically entails:
- Multiple GPUS for inferencing and model training
- Specialized high RAM memory configurations
- Optimized CPUs
Fortunately, we’re increasingly seeing companies like Intel and NVIDIA release hardware designed to support AI workloads, making it far less complex for businesses to employ the technology.
Data Storage
AI models require an absolutely immense volume of training data while also generating substantial information. If your storage solutions weren’t specifically architected to work with big data or generative AI, then there’s a good chance they’re going to fall short in terms of both capacity and access speed.
Software Stack
Computing hardware aside, you’ll also want to upgrade your software ecosystem prior to adopting AI. To start with, we highly recommend ensuring your software architecture is integration-friendly. Beyond that, you may want to consider exploring some of the following:
- Frameworks such as PyTorch, Tensorflow, or PySyft for building and training AI models
- DevOps tools specifically designed for machine learning, such as dataset version control
- Data processing tools like Apache Spark, Apache Kafka, and Hadoop
- A deployment platform such as Docker or Kubernetes
Organizing Your Data
Even the most powerful generative AI model is highly dependent on the quality and quantity of its datasets. Feeding a model inaccurate, incomplete, or low-quality data can lead to an entire cornucopia of problems, including:
- More frequent inaccuracies and incorrect decisions
- Inability to generalize to new information
- Regulatory issues owing to the use of sensitive data
- Data poisoning by threat actors
At best, you’re left with an AI model that wastes considerable time and resources. At worst, you could end up facing regulatory penalties, reputational damage, or even a cyber incident. Avoiding these scenarios requires you to do a few things:
Consolidate
Most AI models aren’t designed to crawl through data silos, and siloed data isn’t exactly good for operational efficiency, either. Start by establishing a single source of truth for all business-critical data. This will make it easier to ensure anything your AI needs to use is readily accessible.
Clean
Having access to data is meaningless if that data is incomplete, inaccurate, or dirty. Provided you’ve consolidated your business’s data, you’ve likely already taken the first step toward cleaning it — eliminating redundancies. Other steps include:
- Delete irrelevant or “junk” data
- Make sure categorical data is correctly encoded
- Address structural errors such as typos and incorrect capitalization
- Deal with missing values via a predictive model, manual input/replacement, or deletion
- Regularly validate data to ensure
Control
Lastly, define a comprehensive data governance policy that clearly establishes the following:
- How data quality is measured and validated
- Data cleansing, enrichment, and monitoring processes
- Access controls and encryption standards
- Compliance considerations
- Anonymization and minimization processes
- AI governance roles with clear privileges and responsibilities
- Data integration, standardization, and consolidation
- Data collection and minimization
- Reporting, auditing, and documentation
- Storage and scalability
Finding the Right AI Technology for Your Business
Preparing your business for AI adoption can be an immense undertaking. The good news is that the right technology can go a long way toward making things easier. Microsoft Teams is a perfect example of this in practice.
Teams centralizes your organization’s communication and collaboration into a single, unified hub. This provides a rich foundation of conversations, meetings, documents, and workflows for AI tools. Teams is also built to integrate seamlessly with Microsoft’s broader AI ecosystem.
Microsoft 365 Copilot, for instance, can provide AI assistance with everything from meeting transcription and content ideation to IT governance. Microsoft also offers a Teams AI library that allows your organization to develop custom LLMs tailored to specific business processes. And that’s in addition to the vast AI ecosystem present on Microsoft Azure.
Microsoft is a relatively heavy hitter in the AI space, particularly for telecom organizations. But they’re also far from the only option. There are countless AI vendors on the market, with solutions tailored to businesses of every size and across every industry.
That’s what we’ll discuss in our next blog. We’ll go over some of the top solutions on the market for SMBs, mid-sized businesses, and large enterprises alike — and more importantly, we’ll help you choose the right option for you. Stay tuned!
Ready to explore how you can integrate AI into your business? Feel free to reach out to learn more about your options.
