How to create a comprehensive AI strategy for your business
No matter the industry or company size, every organization should be thinking about implementing artificial intelligence. There’s no doubt about the efficiency AI can bring, and that helps businesses grow and innovate faster.
It is not about specific AI-driven out-of-the box tools anymore. The powerful combination of data science and machine learning is creating incredible opportunities. It can revolutionize every single aspect of your operations.
The possibilities can feel both exciting and overwhelming. This is why it’s important to first create a comprehensive AI strategy and realize where implementing AI makes sense for your business. Here’s how you can get started.
Identify current technology gaps
When implementing AI, you’re not going to start from scratch. You already have technologies and processes your employees use. That’s also one of the main challenges. How do you evaluate where AI would add value? And more importantly, how do you make sure you don’t end up burning a village to save it?
Artificial intelligence brings disruption to your organization. That’s inevitable. What you need to figure out is how to make sure this disruption remains only temporary and that your business benefits from it in the long run.
The first step is to audit existing technologies and processes. This means you have to answer the following questions:
- What technologies do we currently use, and how well do they help us achieve business goals?
- How do we manage data, and what are its quality and accessibility?
- What are the critical business processes we use? How efficient are they? What do employees complain about when it comes to these processes?
To identify inefficiencies, bottlenecks, and opportunities for automation, you need to assemble an audit team.
Don’t make the mistake of limiting your team to IT professionals. You need a thorough, 360 view of the current infrastructure and business operations to increase the chances for successful AI implementation. Check the table below to get an idea of who should be on the audit team.
| Role | Importance in the audit |
| IT professionals | IT professionals identify potential integration challenges and evaluate the current infrastructure. They are responsible for ensuring AI solutions can be seamlessly integrated into the existing systems. |
| Data analysts | Data analysts are responsible for evaluating quality, management, and accessibility of your data. They play an important role in making sure the data is properly prepped for AI applications. |
| Data scientists | Data scientists design models and algorithms to solve complex problems. They are responsible for evaluating the quality of data and planning the best suitable AI solutions. |
| Data engineers | Data engineers build and optimize the infrastructure and pipelines for data flow. They are ensuring seamless data flow. |
| Department leads | Department leads provide insights into what their teams need, highlighting challenges, common bottlenecks, and repetitive tasks. Their feedback is the key building block of your AI strategy. |
| Business analysts | In addition to feedback collected from department leads, business analysts are here to map out the processes and identify opportunities for optimization. |
| Employees/users | To complement the feedback brought by department leads and business analysts, employees as end users share what they struggle with in their everyday work. |
After this step, you’ll get the idea about the current infrastructure, organizational needs, and the gap between what your employees need and how they operate in reality. This will form a picture of AI readiness from the technical perspective.
Get internal consensus on goals and priorities
Your AI strategy has one important goal: to answer the “why” and “how” behind implementing AI in your organization. When you have multiple stakeholders, it can get increasingly hard to agree on priorities. As the saying goes–too many cooks spoil the stew.
This is exactly why forward-thinking companies decide to first get their feet wet with AI, make the change manageable, and then they scale from there. You have to make every decision-maker a part of the conversation while also drawing a line, i.e. being transparent about who has the last say. The truth is, every department will think their pain points are the most urgent ones.
The best way to prioritize projects is based on three factors–business impact, feasibility, and alignment with goals. Ask yourself what matters the most when prioritizing the first AI projects. Is it efficiency? Revenue operations? Customer satisfaction? What are the KPIs that have proven to move the needle the most? Get input from all departments before launching the pilot project.
Additionally, it’s incredibly important to ensure leadership support and advocacy. The change comes from the top. Your employees need to understand AI is of company interest. People of authority and influence need to communicate this clearly and get involved. That’s how you handle change management and create a truly collaborative environment. The success of the AI project depends on it.
Vet strategic AI vendors and tech partners
Implementing AI requires expertise. It’s likely that you have skill deficiencies within your organization, and that’s completely normal.
You need internal experts who know the current state of affairs like the back of their hands. Then, you need AI experts to help you uncover not-so-obvious opportunities where artificial intelligence can make a difference for your business. Finally, you need leaders who will take care of change management.
It all starts with the skill gap analysis. Not only do you need to map out the technical expertise you have in-house, but you also need to think about the domain knowledge, soft skills, and experience with data management. You need to be objective here. The worst thing you can do is turn a blind eye to incompetence because you wish you had internal expertise.
The process of vetting strategic AI vendors and tech partners comes down to three key steps:
- reaching out to your personal network, industry leaders, and recommendations to identify potential AI tech partners
- sending RFPs to your shortlisted potential partners to understand their approaches, methodologies, and competencies
- making sure the partner understands your goals and vision and is a cultural fit
One of the key things here is to create conditions where you’ll be comparing apples to apples. Use the same scorecard for all potential partners to evaluate expertise, track record, scalability, and technical expertise. This is how you’ll standardize the evaluation process and make the best decision for your business.
Develop and follow an AI implementation plan
Now that you have achieved internal consensus on what you want to achieve with AI, it’s time to figure out how to get there. Depending on the size of the project, you might need quite a few hands on deck.
Similarly to assembling your audit team, you should have input from different experts. Here’s who would need to participate, regardless of the AI project size:
- executive leadership: To provide strategic direction, approve objectives, and ensure alignment
- AI project manager: To oversee the development of the roadmap and coordinate between teams
- data scientists and business analysts: To define use cases and provide technical expertise
Once you have your team, it’s time to create a detailed plan with milestones and deadlines. You should define timelines for data collection, model development, testing, deployment, and evaluation.
The next step is to create a RACI matrix to clearly define roles and responsibilities. This is important not only for accountability but also for easier decision-making.
Make sure to continuously track progress and adjust the timeline accordingly. Frequent check-ins will allow you to communicate early and often. This is the key to preventing scope creep and ensuring successful AI implementation.
Create an AI strategy that sets you up for success
When you get to the point where you know why you need AI and what your most immediate business priorities are, you’re halfway through reaping the benefits. But it all starts with a good AI strategy.
A good strategy will prevent wasting resources–both in terms of finances and time. But the ripple effects of a poor strategy are even worse. Think innovation stagnation, operational inefficiencies, issues with data, technical debt, and more.
If you’re looking for a tech partner that will make sure your starting position is optimal for implementing AI, you’re in the right place. Contact Vega IT to schedule consultations.
