Over the past 10 years, AI has gone from an emerging technology to a must-have for businesses. Companies using AI are seeing benefits like better operations, lower costs, improved customer experiences, and new data-driven products. However, bringing AI in-house involves getting over some common challenges around skills, costs, infrastructure, data, and making changes.
I've been looking at the problems with bringing AI into businesses and why it's not happening faster. In this post, I'll talk about the key things blocking businesses from adopting AI and possible ways to get past them. Whether you're just starting with AI or already hit some roadblocks, understanding these challenges is the first step to successfully using AI in your company.
1. The Shortage of AI Skills - Building In-House Know-How One of the most common challenges is the lack of qualified AI talent. Data scientists, machine learning engineers, AI researchers and other experts are in extremely high demand. With big tech firms like Google and Facebook paying top dollar for experts, hiring is tough for companies without deep pockets. More than older technologies like ERP or CRM systems, AI projects need specialized know-how. Without key technical roles, adoption stalls.
However, for most companies, building a full in-house AI team is unrealistic to start. AI professionals make £300,000 or more in many markets. The limited supply of qualified people makes hiring even a few too expensive. Businesses also rarely need full-time data scientists right away.
The solution is to build up AI skills internally over time. While some hiring might be needed, AI knowledge can come through training employees. With proper coaching and experience, existing staff can shift into AI roles, or at least learn what they need to use AI effectively.
2. Managing the Costs of AI On top of hiring and training challenges, AI software, infrastructure and services require a lot of investment. The raw costs of adoption are a hurdle, especially for smaller companies without huge IT budgets. For businesses already transforming digitally, directing more funds to AI may not be doable.
Unlike old enterprise tech though, AI costs aren't fixed. The flexible nature of AI development means you can start small and spend more as capabilities mature. However, companies must manage costs carefully from the start. A practical approach focused on showing value fast is key.
Prioritizing High-ROI Applications The most critical step is picking the right first AI projects. It's vital to choose uses with clear business impact where ROI can be quickly measured. Applications focused on increasing revenue or reducing identifiable costs make showing the value of AI easy.
For example, using AI-powered personalisation to lift ecommerce conversion rates by 2% can translate to millions in direct sales impact. Applying AI to cut staff hours spent manually reviewing documents by 30% provides real ongoing savings. Moving up the AI learning curve with uses that are easily justified helps further development.
It also helps to line up AI projects with changes already happening if possible. If you're already transforming parts of the business digitally, integrating AI will seem less out of the blue. Tight alignment to company priorities increases executive support for additional projects.
Piloting Before Scaling Another best practice is to pilot AI focused on one use case before committing to company-wide platforms. While big setups from vendors sound appealing, they can be costly. It's better to start small, show AI working for one application, then expand.
Piloting also allows time for organizational learning. Many companies underestimate the workflows needed to fit AI into business processes. Developing skills around data pipelines, model monitoring, and change management is critical. A pilot focused on completeness rather than scale teaches these lessons. Only after a successful small-scale rollout should AI go company-wide.
The gradual approach prevents overspending before value is proven. Once the first pilot succeeds, the foundation for bigger implementations is there.
3. Accessing and Preparing Quality Data “Garbage in, garbage out” is a truism for any analysis. But for modern AI systems, the quality and scope of training data has outsized importance. Unlike rules-based software, AI algorithm performance depends entirely on input data. As a result, data issues ruin many AI projects.
AI data platforms are not turnkey solutions. Real-world data requires major cleansing and preprocessing to be usable. Collecting sufficiently large and representative data sets is also tricky. However, practical data management strategies let organisations overcome these hurdles and power effective AI.
4. Change Management for AI Success - Addressing Organisational Complexity So far we’ve addressed mostly technical barriers to AI adoption. However, organizational challenges around processes, culture and project management are equally critical. Because AI systems behave very differently from traditional software, a new approach focused on change management and continuous improvement is required.
AI solutions aren’t configured products that behave predictably once deployed. Rather, they are probabilistic systems that must be continually monitored and updated based on real data. As the real world changes, so must the algorithms. This paradigm shift conflicts with legacy IT project lifecycles. To successfully adopt AI, companies must embrace far more agile processes.
AI Projects Require Iterative Development Best practices in enterprise AI development mirror those of lean startups. Because algorithm performance is hard to predict upfront, regular iteration based on real results is crucial. AI is not amenable to traditional waterfall development lifecycles. Starting with a minimum viable product (MVP) allows faster feedback.
There is no need for extensive requirements gathering or design docs before the first prototype. Simple baselines establish the framework, which is then improved weekly or monthly based on data. Larger milestones focus more on operational metrics than specifications.
This agile approach extends through deployment too. Sandbox environments with easy availability accelerate experimentation. Models are incrementally promoted through higher environments based on performance benchmarks, unlike monolithic software releases. Continuous integration and deployment enables rapid iteration based on user data.
Flexible Governance Accommodates Change These dynamic development cycles conflict with inflexible IT processes at many organisations. Procurement, security, and change control policies that assume predictable software behavior slow AI progress. Governance and auditing must flexibly allow for algorithmic change.
This starts with compliant access to data, which powers AI systems. Data usage policies should enable sharing and retention unless risks clearly warrant otherwise. Similarly, pre-production sandbox environments require relaxed restrictions that don't hamper experimentation. Above all, organizations must overcome analysis paralysis. Endlessly auditing and theorizing about possible risks instead of empirically testing limits practical innovation. Governance policies should encourage evidence-driven development.
Communicating Progress Over Perfection Perhaps most critically, communication around AI projects must emphasize their evolutionary nature. Stakeholders and executive sponsors need to understand progress will be incremental rather than immediate perfection. Unrealistic expectations result in wasted effort over-engineering systems rather than practical deployment.
Being transparent about intended outcomes and milestones is key. Rather than promising a complete solution by a fixed date, aim for progressive improvements that deliver regular business value.
Adopting overall metrics aligned to company KPIs helps maintain focus on real-world use. Emphasising user feedback helps reinforce this point. Alerting sponsors when models measurably improve conversion rates or customer retention shows that value generation is ongoing.
Avoiding the illusion of “set it and forget it” AI is crucial for buy-in. With deliberate change management and communication, the organisational complexities of AI adoption can be addressed. The technology is only one piece of the puzzle. Taking an adaptive, outcome-focused approach to culture and processes enables getting started and showing business impact.
Start Your AI Journey - Turning Potential Into Practice For today's businesses, adopting AI is not optional. It is a core building block of digital change and competitiveness. However, as we’ve explored, real-world barriers exist around skills, costs, tools, data, and organisational alignment. Knowing these challenges is the first step to conquering them.
By taking an incremental, outcome-focused approach, companies can adopt AI in a controlled, cost-effective way. With the right strategy, the barriers limiting AI's potential can be overcome, paving the way for transformative solutions.
If you want to talk about how your organisation can start on a pragmatic AI journey, reach out to us today and schedule a FREE Software Consultation .
