Selecting the optimal AI approach for your business.
Artificial intelligence isn’t a new technology anymore. AI is transforming the business world by automating tasks, enhancing customer experiences, analysing data, and making quicker decisions, with applications across multiple sectors.
From automating tasks to creating better customer experiences, analyzing data, and making quicker decisions, AI’s usefulness in the business world is extensive. The mere adoption of AI does not ensure added business value.
If not targeted correctly businesses can find themselves investing in standalone tools, conducting countless AI endeavors, or starting initiatives that only ever touch down at a pilot stage.
It is better to build an AI strategy around what you need to accomplish, rather than picking up technology and sticking it at the end of your business processes.
A robust AI strategy integrates business goals, business cases, data, technology, people, governance, and measurable outcomes. How, then, to select the right AI strategy to suit your business? Here are the very first steps you need to take.
The first thing is to grasp the role that AI should play. Don’t ask “Which AI technology should we use?” Pose yourself the question of “What problem is the business trying to solve with AI technology?”.
You may choose to work on your priorities such as the following:
Examples include predictive maintenance or demand forecasting for a manufacturer, or improving product suggestions and keeping customers for a retail company.
Begin by focusing on business goals to ensure that the AI strategy you develop is results-driven rather than technology-based.
With all priorities defined, look for processes that could use AI and can deliver measurable improvements.
Seek repetitive processes, bulky data, slow decision-making, an abundance of mistakes, issues regarding customer service, forecasting, or information-heavy processes.
Next, assess possible scenarios from a business impact, feasibility, cost and risks perspective.
It’s not a process that needs to be done in all departments simultaneously. Less widely distributed valuable use cases can contribute more effectively and validly to the beneficial results than dozens of unconnected experiments.
To name a few, set a measurable target to reduce the time taken in customer support operations by 30% using generative AI, rather than stating that you’ll adopt it into your operations.
This provides your team with a straightforward path for measuring and quantifying the value of adoption, and a compelling reason to do so.
Data is a key pillar of any successful AI approach.
Based on this, do a bit of research before you branch into an AI solution to find out if your organization has reliable, available data. Think about data quality, data ownership, data security, data governance, data integration and data availability of historical data.
Ask questions like:
This is of special significance for generative AI and AI agents that could possibly require access to structured and unstructured enterprise data. Before you scale AI efforts, you might need to work on the data foundation to move your data toward being more complete, current, and accessible.
Familiarize yourself with the problem at hand, and know what kind of data you are ready for, before deciding on the right AI technology for you.
It may be as simple as:
It is not the most up-to-date or most advanced AI model that must be the most suitable option. Take into account factors like accuracy, scalability, price tag, security, data sensitivity, integration demands, and workflow complexity.
A smaller specialization version can be used in some of the applications. For others, coupling AI with their existing CRM, ERP, data or business systems may generate greater value than the creation of an existing AI platform.
There are risks to AI with regard to data privacy, cybersecurity, compliance, IP rights, bias and accountability. That’s why governance must play a key role in your AI strategy from inception.
Outline the stakeholders allowed to access AI systems, data they are allowed to access, sensitivity of data, review of AI outputs, and who to blame for decisions made with AI influences to the detriment of their stakeholders and their audiences.
It also needs to be decided how to track performance of a model and what to do when it generates incorrect or unexpected outputs. The need for governance is heightened when AI systems can actually make decisions on someone else’s behalf, like on a customer’s or employee’s behalf.
AI transformation should be more than a technology initiative. It’s a ‘people and organizational change’ initiative too.
You might need to go for skills in AI, data engineering, data science, software development, cybersecurity, and business analysis, as well as AI governance, depending on what you are aiming at.
Meanwhile, current staff must be trained to be effective with the AI tools.
Your strategy should then address the following questions: Who will be responsible for deploying and overseeing the AI capabilities and how will employees interact with the AI in their workday?
Having clear responsibilities, training and adoption plans can be key for delivering an AI solution that is deployed and adopted.
An enterprise-wide transformation with AI may introduce cost and risk. Rather, have a plan of roadmapping to learn and scale it a step at a time.
Establish business priorities, consider use cases, data readiness, risk analysis and existing capabilities.
Choose a few selected high-value use cases, and run tests against a set of goals.
Evaluate the accuracy, uptake, cost, productivity benefits and impact on the business.
Scale up the successful use cases and incorporate them into larger processes and systems.
Always monitor and enhance models, processes, governance and user experiences. This gradual process ensures that you don’t make substantial costs before knowing how effective it will be.
Initial KPIs should be agreed to during your AI strategy. Depending on the initiative, you could measure:
Don’t mistake the number of AI tool deployments or pilots performed as the means of success. The real concern here is: Has AI made an impact on a significant business outcome?
If the solution doesn’t lead to a measure of value, your organization shouldn’t be afraid to tweak, redesign, or even end the project.
Investing in AI can be less effective if there are several mistakes are made, like –
It’s not the most sophisticated AI technology that will solve all your business problems. It involves knowing when and how AI can provide tangible and long-term business advantages. Begin with goals, create a list of “higher value” use cases, audit your data, determine your true technology needs, set up governance, educate staff, and create a scalable plan.
Most importantly, view your AI strategy as a business competency that is continually developing and improving. Your strategy needs to adapt along with the other things that change: your goals, your customers’ expectations, the technology you use, and the regulatory requirements.
When exploring AI initiatives or seeking experienced consulting partners who can aid your transformation, research local firms and discover consulting firms by industry or technology.
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