Navigating the AI landscape: Is your business truly prepared?
Businesses are on the cusp of an era of transformation with the widespread take-up of AI. Many have already invested time and resources in exploring how these tools could bring additional value. For some that may be improving efficiency and increasing productivity, or creating tailor-made customer experiences to increase sales; for others, it could mean developing new product concepts. The list is endless.
AI promises ground-breaking innovation across industries and professions, provided organizations are fully prepared to take advantage of its capabilities. But are businesses anywhere near ready for AI? Or are they in danger of rushing ahead, akin to the early days of cloud technology, and could live to regret decisions made in the race not to miss out?
What 'The Cloud Rush' Taught Us
With the benefit of hindsight, it’s clear that some of the first cloud projects were classic examples of organizations getting carried away with unrealistic expectations for over-promised and under-planned deployments. This led to lengthy and costly implementations, with in-house teams lacking the skills to maintain a completely different IT infrastructure and not fully appreciating security obligations. Inadequate scoping hampered the mass migration of data and legacy applications, and systems that had been designed for on-premises environments suffered from compatibility and performance problems.
To avoid similar painful experiences with AI, organizations will need to be far better prepared and exercise more caution this time around. The pilot projects that get the go-ahead in the next 12 months will be expensive outlays, and right now CIOs are unlikely to have dedicated AI budgets. Therefore, business leaders will need to be pragmatic and selective about what’s achievable in the short term.
Starting with Optimization
Before embarking on any major projects, organizations must review their data management and governance strategies. AI relies on huge volumes of data and its quality determines the effectiveness, and ultimately, the value of its output. Investing in AI without accurate, structured, and centralized data management will lead to unnecessary expense if information needs to be consolidated, cleaned and deduplicated at a later date. Ensuring the availability, reliability, and security of large volumes of data for analysis is of paramount importance to generate relevant, actionable insights.
While AI offers massive potential to bring greater efficiencies, existing tools shouldn’t be overshadowed by the novelty of bringing in new technology. It’s important to ensure that maximum value is extracted from the tools in place, and there is a focus on optimizing the current infrastructure to make the most of available resources. Businesses need to prioritize efficiency and cost-effectiveness to create the foundations for deploying AI. Otherwise, they will incur greater costs in the long run when they belatedly try to rationalize data and systems in readiness for AI tools.
As highlighted at the beginning of the cloud revolution, a lack of skilled resources is likely to cause difficulties in delivering and maintaining solutions. Investing in talent and retraining now will be crucial to ensure the successful implementation of AI projects over the coming years. In addition to technical jobs for data scientists, software architects, integrators, and deployment specialists, there will be requirements for other skills too. For example, problem solvers who can translate real-world issues into AI solutions, and communicators who can explain complex AI concepts to stakeholders. Internal teams must have the necessary skills to evaluate potential suppliers as vendors are jumping on the AI bandwagon, and trusted partner relationships will be essential in this pioneering stage.
Preparing customers for the introduction of AI is another step that shouldn’t be overlooked. AI can seem mind-boggling to many consumers, but transparency builds trust. By communicating how it will be used openly and setting expectations, customers can appreciate both the value AI brings and what it cannot do. Additionally, addressing ethical concerns upfront about privacy and bias shows a commitment to responsible AI use. Communicating via multiple channels such as websites, blog posts, email newsletters, and social media will help to inform and reassure customers about changes to processes and how their personal data is being used.
Surviving The AI Stampede
In the stampede to embrace AI, organizations must be ever mindful not to disregard the hidden potential and value in their existing infrastructure and data management tools. Taking the initiative by optimizing systems and cleansing data now will be vital for AI success and avoid costly reworking later. While implementation is likely to require investment in new talent, businesses can also take the opportunity to train and develop existing teams to equip them to deal with the challenges ahead.
In summary, before any organization embarks on its AI journey, it should review its existing infrastructure, assemble the right team, and develop communication plans for all stakeholders. Taking these steps in advance will help to minimize mistakes during the first wave of AI projects, preparing the way for efficient and effective deployments.
Photo Credit: Photon photo/Shutterstock
Reece Gohil is Microsoft Product Owner at Six Degrees.