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With software companies in Australia now pivoting towards AI application development, there’s much buzz out there on what works and what doesn’t, as well as how to scale with intelligent technologies. In spite of the mainstream adoption of AI, this is still uncharted territory for many businesses, especially smaller ones.

This 3-step guide therefore focuses on the nuances of AI application development: what to do before you begin, how to incrementally expand your solution, as well as considerations that go well beyond the post-implementation stage. Read on to know more!

Looking to implement AI development for your business, or pick up a lagging project? With a 100-member team that has hands-on expertise across multiple domains on a global scale, EFutures can give you the solution your business needs to satisfy customers, stay within budget, and meet bottom-line objectives.

 Contact us today to learn what we can do for your business, through our free and no-obligation assessment.

Step 1: start where you are

Before embarking on any AI-driven software development project or taking the call to hire AI developers, it is crucial to understand where your business stands as it relates to all the various working components that keeps it afloat – and not just your IT infrastructure.

This is because the implementation of AI will affect all areas of your business that depend on cloud-based systems for determining workflow processes, as well as analysing data for decision making.

This first step will give your team the ammo they need to architect suitable solutions, while keeping security and compliance in mind.

Assess the current state of your business

Start by gathering relevant team members to discuss the workings of your business. While an assessment of this nature is typically associated with what’s amiss, take this opportunity to also acknowledge what’s working, and why – as it shall provide clarity on what can be retained in order to maintain smooth operations.

Business challenges come in various forms, but they generally take the shape of:

• Bottlenecks and inefficient utilisation of existing resources,
• Complaints and feedback from customers,
• Silos in data, especially when poor or incomplete data causes delays and loss of revenue.

Transform and enrich data for model training purposes

AI projects require vast amounts of data for training purposes. Where this data comes from is a highly sensitive matter, especially as compliance standards for fair use get tighter by the day.

Many companies are usually sitting on big data that has been untouched for long periods of time. Raw and unfiltered, it can be a treasure trove for training AI models. However, the catch of whether this data is compliant for use, still applies. This is a conversation that needs involvement from your legal and/or compliance teams. If mechanisms to obtain user consent have been established in the past, this process is bound to be smoother.

In the event of first-party data being unavailable, or first-party data being unusable due to Personally Identifiable Information (PII), numerous alternative methods exist, such as obtaining compliant third-party databases or anonymising data. But once again, this is a quandary that is best addressed by your legal or compliance teams, depending on your industry and the region you operate in.

Step 2: build a beta version or MVP

Starting with a beta or MVP is the best course of action, no matter how necessary or urgent your AI-powered tool or workflow is. With AI likely to make mistakes, starting with a rudimentary version in combination with parallel implementation can ensure business continuity, while your employees get accustomed to the new system.

Release internally, or to a select set of users

Engage select employees to test your system, if it is meant for internal use. For front-facing systems, gather select users (preferably with some kind of incentive, such as discounts or gift cards) to provide unbiased feedback on what works, and what doesn’t.

Dedicated development teams for startups that are built in partnership with a software development company can assist in sourcing users for beta versions too; other team members can play the role of prospect/customer, if a customer pool directly from your business isn’t available.

Monitor performance and feedback loops to train models better

AI subset technologies such as machine and deep learning improve with more data, and by reinforcing (or disapproving) certain outcomes. Contrary to popular belief, AI isn’t a ‘set it and forget it’ technology – it needs to be constantly monitored to ensure results are accurate, and hallucinations are mitigated.

Top software development companies in Australia offer AIOps and MLOps well after the initial implementation is over, and the MVP of your product has evolved. This provides ongoing maintenance of code, while keeping security tight and ensuring the ethical use of AI.

Step 3: Continue to evolve

If you are partnered with a top software company in Australia such as EFutures, our project management lifecycle focuses on the ongoing maintenance of, and security of your AI-powered solution. As a cloud services provider, we’ll also manage the use of your application’s cloud resources across cloud computing vendors such as AWS and Microsoft Azure, while also conducting cloud cost optimisation to stay within budget.

What your AI project needs eventually depends on what it fulfils, and the regulatory obligations expected by the industry or region your business operates in. However, the below considerations are widely applicable to most (if not all) AI projects.

Your model has matured. Now what?

You’ve successfully implemented your AI-powered tool or workflow and evolved well past the MVP stage, to now own a product that is well received by your users. At this stage, your model has also improved thanks to organic data being added through daily use, giving your product and model the reinforcements they need to improve.

While this is a valuable milestone to achieve, continue AI and MLOps to make sure your model behaves as expected. Further augment with newer LLMs if possible, so that your proprietary AI solution is one that will stand the test of time.

Train employees to improve, and be perceptive

With AI-powered solutions, many get complacent as they can now forgo the need to fixate on the little things that they otherwise had to, before. While this is, in effect, the result we’ve been expecting through AI (so teams can be freed from manual tasks), it can cause employees to skip quality checks, assuming the ‘AI will take care of it’.

Always train your employees to do their share of quality checks, and report any bugs if found. Newer versions of your AI tool, on the other hand, may also require formal training of your employees.

 

Key takeaways

Building an AI-powered application involves stringent planning before development can begin, with post-implementation phases also requiring ongoing maintenance to ensure results continue to stay accurate, and are up to par with required standards.

In essence, the below 3-step process successfully delivers AI projects:

• Step 1: Thorough business assessments for identifying optimal solutions, and data transformation prior to model training,
• Step 2: Always starting small with a beta version or MVP, to gauge functionality and user sentiment before expanding further,
• Step 3: Continuing to evolve by improving models, using newer LLMs, and fostering an environment where employees always hold the AI responsible – instead of the other way round.

Should you allocate extra time and budget for your AI project?

Software outsourcing companies know that any AI or software project bears the likelihood of running late and/or going over predetermined budgets. It’s sensible to set aside some buffer time and an extra budget, if your business and your software development team can help it.

Should you add features incrementally to your AI project?

This resonates directly with the concept of MVPs; start with the most basic features, gauge user reception around these, and either expand them further, or replace them with new capabilities that users have expressed a need for.

While MVPs are popular for building software, they also apply to any AI-powered solution. From chatbots to generative AI, beginning with minimalism that’s baked into user journeys as well as the code can give teams more time to focus their efforts on training models more effectively, so they can produce accurate results.

Once accuracy is achieved (to a certain extent that is acceptable), newer capabilities can be augmented to your existing product, thereby creating an AI-driven solution that is truly useful as well as reliable.

Should you utilise manual efforts for your AI project?

Contrary to popular belief, even AI-powered tools may need some manual reinforcements to make sure it eventually functions as per expected standard. Manually labelling data for training purposes is a good example. As AI learns from human thinking, manually giving it the insight it needs to progress may be most optimal.

Testing and continuous monitoring are two other areas where AI shouldn’t be left unbridled; the alternative case scenario being massive errors that can cause significant damage if left undetected.