With more and more companies focusing on implementing AI in some form across existing business processes, understanding how to do so is a common quandary that many business leaders have in mind.
In this article, we discuss everything you need to know about building a powerful AI business strategy, so your teams (both on the business and development side) can steer their efforts towards meeting crucial business goals.
We’ve also included a mini guide to help you choose the best AI partner for your business. Read on to know more!
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We know that no two businesses are the same – and neither are their needs. This list of components can serve as a blueprint for your team to understand, at a granular level, what you really need out of your AI project, what challenges you stand to face, as well as how they can be mitigated.
Any AI business strategy needs to start with a comprehensive assessment (or two). While assessment types may vary depending on what your exact needs are, the following assessments are generally suitable for most business use cases.
Before layering AI on to your product, your product needs to be verified for overall readiness. This includes identifying whether all working parts are functioning as normal, and that the user journey is mapped meaningfully. If there are any gaps pertaining to the product, these need to be fixed before any AI is layered on – as said gaps can lead to issues larger and more expensive to fix, once deployed.
Although it may seem on the outset that you and your team are sure about what should be achieved by leveraging AI, conducting a goal assessment can offer deeper insights on the nitty-gritty of each goal. Furthermore, a comprehensive goal assessment can also quantify objectives, helping your team align through KPIs, both on an individual and collective level.
Risk assessments aren’t just applicable for highly regulated industries, such as security, legal or financial services. With every modern digital application now processing vast amounts of data, businesses are obligated to govern this data, depending on regulations that pertain to their specific industry, state or country.
Identifying key items such as individual data points, channels of data inflow and outflow, as well as who has access to each item of data can help both your business and development teams understand what security mechanisms should be in place, for maintaining optimal protection.
Alongside assessing other areas, it is essential to also assess how much of a budget is available at your team’s disposal, as early as possible. This helps mitigate the fairly common likelihood of a misalignment between product development plans and available budgets; if budgets are insufficient, your product plan will require a significant overhaul, which can negatively impact turnaround times.
KPIs help both your business and AI development teams understand what each team member is responsible for on an individual level, while also being cognisant of macro-level objectives. Your KPIs need to directly connect to every objective that is laid out during the assessment phase – so progress can be measured to eventually determine whether objectives are being met.
This means that objectives need to be quantified through KPIs, as much as possible. There may be exceptions where some objectives may not be quantifiable; anecdotal feedback, especially in small amounts, may be difficult to process. For such exceptions, using your best judgement to analyse and identify insights is the most viable way forward – if the data is significant enough to be considered, that is.
Setting timelines and budgets is a more administrative aspect of any AI business strategy. With most of the knowledge-based work such as assessments and the setting up of KPIs now concluded, it’s time to finalise timelines and budgets for each step of the process. While budgets are best discussed during the assessment stage as we highlighted above, they can now be set in stone on an itemised basis, by listing the cost of each resource and process.
Timelines, on the other hand, are set in order to make sure teams complete tasks on time. These could vary in light of unforeseen circumstances, but shall still serve as a point of reference for your business teams especially, in order to determine when deliverables can be expected.
Your AI project is an ongoing one, and isn’t going to end at deployment. Measure performance against the KPIs set prior to development, to gauge whether your project is performing as expected.
Configuring real-time reporting tools that track your KPIs accordingly can make a world of difference for this, so your teams always have the insights they need to adjust and/or pivot, as and when required.
While the below guide is inclined towards businesses that are looking for a brand new AI development company to partner with, it will serve to be just as helpful to evaluate current partnerships.
With AI advancing at a rapid pace, it is essential to determine whether your AI development partner is staying in lockstep with trending technologies – and is nimble enough to act and evolve with changing needs.
All of this starts with an AI business strategy; one that’s built to meet the challenges and needs of your organisation. Whether it’s automating a single process or forecasting trends to guide future efforts, your AI development partner needs to work alongside your internal business teams to deliver a plan that will meet goals – on time, and within budget.
A powerful AI business strategy is only made possible through:
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