What’s stopping businesses from taking an AI-sized byte?

Artificial intelligence (AI) is eating software, but do organisations in Asia have the appetite to take a byte and embrace AI deployments?

The APAC region has shown consistent commitment to AI, with the 2022 IDC Worldwide Artificial Intelligence Spending Guide forecasting investments almost doubling to around US$32 billion by 2025. Despite increasing AI adoption and an exploding number of use cases, decision paralysis can stop companies from committing to AI.

Those leaders looking to transform their business through AI need to understand what bottlenecks to expect. Data, systems, and talent will all play a role in this transformative process.

Is my data AI-ready?

The most frequent reason expressed by business leaders for not adopting AI is the belief that their data is not AI-ready. A concrete data strategy is a fundamental part of maximising one’s return on AI investment. However, leaders must realise that they will never have perfect data. Deploying AI to analyse different data sets will point, more often than not, in the right direction and allow the business to make better-informed decisions.

To address and minimise potential concerns around data quality and historical biases, involve SMEs and evaluate early AI outcomes against current thinking. This will not only accelerate an organisation’s AI journey and begin driving business value, but also troubleshoot any potential data problems and help garner support of an important stakeholder group.

Build versus buy?

Another early question leaders face is whether building or buying an AI stack is better for their organisation. Both approaches have pros and cons; buying an AI stack could lead to losing a competitive edge, while building brings the potential risk of IT missteps and poor execution. A business building its own AI stack means having to decide between trading IT risk, long-term upkeep, and maintenance for potentially lower costs and more flexibility in implementation. For early adopters of AI, this was a straightforward decision. As AI has become more pervasive, newer organisations adopting AI realise they may not have the technical capabilities, or the risk and financial appetite to build their own platform. With a rich ecosystem of new and mature AI platforms available, more often now leaders are investing in these new, off-the-shelf solutions.

Either way, deploying an AI tool will involve commitment. This commitment will include time, people, and costs. The name of the game early in AI deployments is to get projects into production. Simply put, platforms and projects that remain undeployed naturally have a lower value. The project team’s role is to identify the right short-term, small-scale projects; achieve quick wins; plan for the long term; and limit the technical debt in deployment. Small-scale projects and quick wins enable the team to showcase value and benefits, building credibility and support for larger, enterprise-wide transformation projects.

Further, leaders must be vigilant, as the AI data and software landscape changes every six months. Today’s hot tech could be tomorrow’s abandoned project. One can efficiently limit the risk of poor technology investment by minimising the technical debt carried by the organisation. This lends the business the freedom to course-correct and potentially change the AI stack, if or when the landscape dictates it at the speed of business. A great example of this approach is the use of cloud-based services. Smaller businesses can circumvent the need for new servers and powerful processors with this approach.

Do I have the right talent?

For all leaders contemplating AI transformation, the discussion of talent is salient. Who will create the AI? Who will use the AI? Are the skills necessary present already? Can we upskill our own people or will we need to go into the marketplace?

For organisations starting their AI journey, this may be the most critical question of all. Companies that are progressive in nature are the ones that focus on systemising and scaling AI to a level that will future-proof businesses and economies alike. By systemising access to data and enabling them with AI platforms, experts and analysts alike can make use of company assets to drive business results and build new data projects.

Democratising both data and AI increases the number of potential new ideas and viewpoints for each project. It encourages experts, SMEs, and laypeople to work collaboratively and apply data and AI to all problems. This allows analysts to complete work that previously only experts could. With analysts completing more work, this frees experts to push the stage of the art in their business and mentor the next generation of data leaders. All of this creates a virtuous cycle of new projects and value thanks to AI.

Working with AI every day

By empowering all employees to use AI, the whole organisation can make better decisions, transform their work, and propel the company forward. That’s what we call “everyday AI”.

Everyday AI makes the use of data and AI systems mainstream. It allows leaders to integrate data into their day-to-day operations, have better visibility of organisational challenges, make insights-led decision-making, and position themselves to capture newer growth opportunities.

AI is already a part of our everyday life from websites we visit and the shopping we do, to the notifications that are on our phones. For AI in our workplace, it is just warming up.