Member Article

Beyond the Hype: The Reality of Preparing for AI Adoption

By Ambroży Rybicki, CEO and co-founder of ARP Ideas

Artificial Intelligence (AI) has become a common buzzword in the business sphere, with everyone eager to leverage the potential that AI holds. Following the development of large language models (LLMs) like ChatGPT and other AI based technologies such as Microsoft’s Copilot, business leaders have been quick to ask the right questions about adopting the technology into their everyday operations. Questions such as; How can AI be used to optimise and streamline processes? How can it be used to identify more opportunities to expand the business, and maintain a competitive edge?

Clearly, businesses are eager to unlock the power of AI and stay ahead. However, amidst the excitement, it’s crucial to recognise that AI works with the tools and information that it is given. Therefore, it is essential that businesses lay the right foundations before jumping on the AI bandwagon. Currently, many organisations lack the essential technological framework that is the prerequisite for a successful AI integration. The article will explore what steps businesses need to take before embracing AI, and the barriers that may arise when these fundamentals are not in place.

What Businesses Need Before Integrating AI

Before integrating any new technology, especially something as complex and power-consuming as AI, businesses need the right foundations in place. The capabilities that companies may lack before they begin integrating AI into their operations include:

Data Infrastructure

AI needs access to high-quality, well-organised data to work effectively. Without a strong data architecture, AI will struggle to deliver value. Data infrastructure will be the bedrock upon which success of AI in a business is built. Without high-quality and well-organised data, AI will falter. Businesses that invest in their data infrastructure are not only setting the stage for AI success but also future-proofing themselves against data-related challenges.

Established Customer Relations Management Software

Companies with robust customer relationship management (CRM) systems already in place and commitment to basing decisions on data are well-positioned to begin implementing artificial intelligence into their everyday operations. Over the next few years, these organisations can be expected to begin integrating AI capabilities into their already existing CRM platforms, which would enable more personalised and automated interactions, taking their customer experience to the next level.

Businesses that have fragmented customer data across multiple sources, on the other hand, are predicted to face more obstacles in their AI journey. Consolidated and organised customer information is a standard prerequisite for unleashing the potential for AI. In this instance, compiling dispersed data from various legacy systems and spreadsheets into a unified database, needs to be prioritised before the organisation can begin to fully implement AI across all their systems.

Digital Workflows

In the age of AI, digital workflows are crucial for success, and straightforward digital processes are necessary to layer AI on top of. Businesses that invest in streamlining their processes and transitioning to digital workflows pave the way for successful AI integration. Additionally, data governance becomes more manageable in digital workflows. Businesses can implement data quality checks, privacy controls, and compliance measures more effectively when processes are digital. This ensures that AI operates within legal and ethical boundaries. Disorganised analog workflows, on the other hand, with their inherent inefficiencies and limitations, can act as stumbling blocks, inhibiting AI’s transformative potential.

Cloud Adoption

Most modern AI solutions are designed to run on cloud infrastructure rather than on-premises systems. There are a few key reasons why the cloud is critical for enabling AI. One of these reasons is scalability. AI models require immense compute power, especially during the training phase. Cloud platforms allow businesses to rapidly scale up and down compute resources as needed. On-premises data centres typically lack this flexibility. Major cloud platforms also provide scalability in terms of AI-expertise, with armies of AI researchers and engineers continuously improving their platforms. This expertise can be challenging for a single company to replicate internally, especially when it comes to small-to-medium enterprises (SMEs). It should also be noted that cloud-based models are compatible with most devices and can be accessed from anywhere in the world, a feature that most on-premises legacy systems lack, thereby making AI-integration more challenging for organisations.

Additionally, AI requires a significant amount of agility. Cloud providers frequently update their AI services with new algorithms, tools, and accelerators. This makes it easier for companies to always leverage state-of-the-art AI capabilities, without major upgrades to internal infrastructure.

Strategy

An effective AI Strategy should be put in place to ensure any successful integration of AI. A clear roadmap for how and where AI can drive impact is crucial. Ad hoc AI projects without direction tend not to yield results.

This strategy serves as a guidepost for determining where and how to apply AI across the business to drive real value. A good AI strategy will take into consideration the core principles that will govern all AI initiatives, the data that will be required to enable said initiatives, as well as all ethical considerations pertaining to the use of AI.

Building a Trustworthy foundation in AI

For businesses to truly embrace AI, they need to create a foundation of trust for the new technology. While AI is a powerful tool that can complement human capabilities, it is not a complete replacement for the human element. Fear of AI stealing jobs often hinders integration.

Although AI will advance to mimic more facets of human behaviour, it cannot fully replace human judgement and critical thinking. There will always be a necessity for human participation in high-stakes decision-making and resolving complex problems. Therefore, workers need to understand and trust AI for effective adoption.

To ease anxiety over AI, businesses should strive for a “glass box” AI model rather than opaque “black box” systems, Engineers should design systems where users can understand how outputs are reached so as to use transparency to set minds at ease about using the new technology. User feedback is key. Surveys, focus groups and two-way communication channels can uncover public concerns. AI should be piloted in low-risk environments to gauge user reactions.

Businesses can also help in easing this AI-related anxiety through various training programs for employees. Equipped with more information about AI and how best to use it, it is difficult to view the technology as anything more than a tool to enhance productivity. Transparency from organisations is key when communicating plans for integrating AI, especially when it comes to the capacity in which the technology will be integrated into usual systems. It is recommended that benefits for employees are highlighted in these plans.

For example, if a company’s plan involves using AI to automate data processing and recommending solutions based on the data consolidated, it should be clearly communicated that this will reduce the tediousness of repetitive tasks and any AI recommendations must be further elaborated upon using human discretion. This will enable companies to fully embrace the potential of AI while avoiding the risk of hindrance due to lack of employee trust in the technology.

Organisations must also consider ethics. Review boards and oversight procedures should ensure fair and safe AI aligned with human values. Impact on vulnerable groups should be examined to avoid biassed outcomes. Overall, organisations should implement AI in a transparent, responsible manner. Proactively addressing trust, ethics and feedback shows commitment to stakeholders. This thoughtful approach enables organisations to become leaders in advancing AI for the benefit of society.

The Barriers for AI

AI functions with the information and tools that it has been given. Too often, companies want to implement AI without building on the above foundational elements first. Lack of quality data, convoluted processes, and distrust from employees can place significant constraints on AI performance.

What else needs to be considered is that AI is not universally applicable. The best outcomes come from targeted AI use cases that move specific KPIs tied to business priorities. Companies can’t simply overlay AI broadly and expect miracles.

Of course, even with the right foundations, AI has limits. Certain manual processes cannot be fully automated. And AI still makes mistakes, requiring human oversight. The hype around AI tends to downplay these realities.

Enhancing Your Technology for AI

The good news is that with deliberate effort, businesses can become AI-ready. Here are three areas to focus on multiple aspects.

To ready their data infrastructure for AI, companies must pursue comprehensive modernisation efforts including consolidating data from across systems into a centralised data lake or warehouse. Breaking down data silos is imperative. It is also important for businesses to be cleaning their data by fixing inconsistencies, formatting issues, duplication, and integrity problems. Low quality data cripples AI performance.

It is also essential that businesses are cataloguing data to make it discoverable for reuse in AI applications. Metadata helps data assets become AI assets. Following this, choosing flexible storage like cloud object stores will help accommodate growing volumes of unstructured data that AI ingests

By making data aggregation, quality, governance and preparation for Machine Learning (ML) central priorities, companies equip their infrastructure for AI integration. The payoff is higher quality training data leading to better performing models.

It is perhaps a relief for most business leaders that they do not have to navigate this new innovation alone. A trusted IT partner can equip leaders with a checklist of what specific requirements their organisation needs to check off before beginning to implement AI into their everyday lives to make the most out of their investment. Consultants can also be on hand to offer bespoke solutions that make apt use of AI, which would enable most companies to maintain their competitive edge in the market.

The Bottom Line

The promise of AI is real. However, due considerations must be made before businesses can begin implementation. Appropriate groundwork needs to be laid down and companies must master the basics before moving on to the next new thing.

With a strong foundation, AI can then augment human capabilities and drive real competitive advantage. Patience and realistic expectations are essential. It is crucial that business leaders not let the hype around AI cloud their judgements. By focusing on the basics, businesses are set up for AI success when the time comes.

This was posted in Bdaily's Members' News section by Lucie Hayes .