Artificial Intelligence has become a top priority for virtually every decision maker at this moment. This revolutionary technology is poised to profoundly influence numerous facets of the future. Therefore, it's entirely comprehensible that executives are giving it the attention it rightly warrants. Neglecting to do so might even be considered a risky proposition?
According to a survey by PWC, almost all business leaders say their organization is putting AI at the forefront in the near term. Learn more about effective IT strategy planning here.
One particular area of AI that is exciting business leaders across boardrooms is Generative AI. But to derive the best out of this or any other type of AI that your organization has set focus on, you will have to start by creating a wholesome AI strategy.
Use our planning guide to help you focus on AI initiatives that will bring tangible value to your organization.
Meanwhile if you are keen to use AI for cybersecurity, please check this comprehensive guide where we cover AI and ML in cybersecurity.
Fundamental steps in creating an AI strategy
These steps serve as the foundational pillars for rolling out AI. They provide a clear roadmap that will lead you to areas where the utilization of AI can yield maximum returns, both in the short term and the long term.
Step 1: Set the AI agenda
Establish your AI objectives, then link them to the organization's goals. It's all back to the basics here. In other words you need to restate the organization's vision.
Once you restate the vision, go ahead and state how you want AI to support the vision. Here are some examples.
- This is how AI will support the following areas
- The organization will use AI to achieve the following outcomes
You need to be very clear on what role exactly AI is going to play in supporting the vision, and which specific areas it will support.
This simple process will ensure that you find the right activities. You want to put money on AI projects that will not only deliver substantial ROI but ones that will also give birth to more beneficial outcomes.
Use this sample table to clearly align AI objectives with clear outcomes.
Goal | How can AI support the goal | Example use case |
Improving customer service | We can use AI to greatly improve the efficiency of our customer care services. | Deploy virtual assistants for common customer queries to reduce workloads on customer care staff. |
Revenue growth | We can use AI to help open new revenue opportunities | Use AI to analyze customer behavior and introduce new products/services. Please check our guide on Internet of Behaviors. |
Using this template, add as many goals that you consider critical from your organization’s vision book. The point is to look at each goal and ask questions such as “how can AI support this goal, how can we use AI to get this goal to 100% ”.
PS: It’s important that you involve all stakeholders in this deliberation. While AI is a largely IT issue, its impact is organization-wide. So it’s critically important that everyone is involved right from the beginning.
Step 2: Set realistic metrics that will be used to measure results
For each goal and accompanying case study, please set a milestone with clear metrics that will be used to track the success rate.
For example, the revenue growth goal can have a metric that focuses on measuring the revenue increase for specific product or service lines. Set a percentage that you will use as the success indicator. Something like:
«3 months after deploying AI, the goal is to increase the revenue of product/service XYZ by 5%. We'll review this metric on MM/DD/YY.»
As you can see, this metric is very specific — down to the date when the goal should have been achieved and will be reviewed. This way, your teams have a tangible outcome to pursue. So they understand that the deployment of AI is not just another fancy technology to flow with the tide. Instead, they clearly understand what the deployment is intended to enable them to achieve. They can see the goal, and each individual is allocated equally clear responsibilities in this pursuit.
Step 3: Risk mitigation
Artificial Intelligence, like any other technology, comes with a number of risks. In fact, for AI specifically, many businesses are hesitant to deploy it because they are too afraid of the risks. However, you don't have to fear to this level. Instead, risk assessment is your ally.
Risk assessment involves a comprehensive evaluation of the potential risks and challenges specific to your AI initiative. This assessment enables organizations to identify and prioritize risks, determine their potential impact, and devise strategies to mitigate them.
For instance, if you plan to use AI for customer service chatbots, consider the risk of inadvertently alienating customers due to miscommunication or misunderstanding. Implementing a risk assessment process would involve the following:
- Evaluating the impact of misunderstandings
- Ensuring continuous monitoring
- Implementing measures to improve chatbot responses and customer satisfaction.
To guide you further, these are the common types of risks you will need to look at as you lay down the AI strategy:
- Regulatory risks: Failure to comply can result in legal penalties
- Reputational risks: Potential harm to a company's image and brand due to negative public perception, ethical concerns, or AI-related incidents.
- Competency-based risks: The potential problems that can emerge due to a lack of expertise in AI within the organization.
- Ethical and social impact risks: AI systems can inadvertently amplify bias or have unintended social consequences.
Top IT leaders like the CIO and CTO should be strongly involved in the risk assessment function.
Also Read:
- Chatbot Prompt Injection Attack: A New Threat
- The Impact of Large Language Models (LLM) on Cybersecurity
Step 4: Prioritize projects
Each AI initiative must be tied to a project with reasonable feasibility.
Projects that directly contribute to core business objectives should be given higher priority, as they are more likely to deliver tangible value.
Consider both budget and talent resources. Projects that can be executed within existing constraints may be prioritized over resource-intensive ones. Those that can be implemented with current constraints could be more feasible to implement.
Equally evaluate the complexity associated with each project. High-complexity projects with significant risks may require more careful planning and resources. It's important to consider your organization's risk tolerance and capacity to manage complex initiatives.
Additionally, take into account the project timeline. Some AI initiatives may offer quick wins. Others may require a longer development cycle. Consider your organization's appetite for both short-term and long-term results when prioritizing projects.
Lastly, factor in market and competitive dynamics. Consider how each project can provide a competitive advantage or address market trends. Projects that align with current market needs and competitive pressures may warrant higher prioritization.
For a practical example, let's say a retail company is considering three AI projects:
- AI-Driven Product Recommendations
- AI-Powered Inventory Optimization
- AI-Enhanced Customer Support Chatbots
In this case, the company will need to prioritize these projects based on the factors mentioned above.
For example, if the company considers increasing revenue a critical need at the moment, then it may find it very feasible to prioritize AI-Driven Product Recommendations over AI-Powered Inventory Optimization and AI-Enhanced Customer Support Chatbots.
Not sure where to start?
Experiment!
Even for an experienced IT leader or decision maker, it’s possible to feel lost in all the AI wave. We all find ourselves here sometimes. Unfortunately when faced with this dilemma, some leaders decide to put it at the back of the mind. Others give up on it and let things take a natural path. This is dangerous.
If this is where your organization finds itself, the best way out is to experiment. Sanction a couple of relevant AI tools and let the teams get used to them. Let them use these tools to experiment and see where they can find valuable applications. Soon a culture of AI will take root in the company and ideas will start flying around. Harvest these ideas and channel them into a pipeline that will eventually bring out suitable areas where the organization is ripe to deploy AI.
In other words do not let the buzz pass you. Don’t be a bystander while the competition is trying out different AI tools. This is especially true for small to medium businesses with no long term oriented vision nor resources to hire AI consultants to guide them. When done correctly, experimentation is more likely to unearth the right opportunities.
For example, PWC is using generative AI to help clients to reinvent their businesses. The keyword here is reimagine, meaning they are essentially experimenting with AI to see how it can revolutionize their operations. PWC is investing $1B into this project over the next three years, a clear signal that this is an important phase.
For small businesses, we have previously discussed the major types of IT services for small businesses. You might also want to start experimenting with AI around some of these areas as a first step.
Decision makers are happy with AI!
A survey by Microsoft discovered that decision makers and staff with access to AI-driven tools are reporting a «more fulfilled» feeling. This is an important takeaway.
When you deploy a technology that fulfills the people who drive the organization, this is a remarkable step. This is what AI is doing across many companies, and our brief AI strategy blueprint right here should help get your organization to this happy point.
