Demand for artificial intelligence in the enterprise is exploding. It’s not just our own proprietary research that highlights this fact. Countless reports show companies are investing more into AI strategies and tools. Unfortunately, there are still a lot of AI adoption challenges to overcome.
In fact, reports from McKinsey suggest that virtually every business is now using AI in some way – but only 1% feel like they’ve achieved any kind of true “AI maturity”. Many organizations are still fumbling their way through initial experimentation projects, agonizing over new governance policies, and even desperately searching for the right talent to drive AI implementation efforts.
Clearly, enthusiasm for artificial intelligence isn’t enough on its own. Companies need a comprehensive plan for how they’re going to overcome a few major hurdles. Here are the top AI adoption challenges you’ll need to prepare for, and how you can tackle them head-on.
Enterprise AI Adoption Challenges: The Hurdles
Obviously, the biggest AI adoption challenges faced by any organization will vary depending on their resources and goals. But some major issues seem to appear in just about every major report, study and survey. Let’s take a closer look.
1. Data Privacy, Governance, and Ethical Concerns
AI adoption challenges revolving data privacy, security, governance, and even ethics are some of the biggest facing every business. Even if your organization doesn’t have to navigate complicated industry regulations, like HIPAA and PCI-DSS, you have a lot of headaches to address.
Every AI model, for instance, relies on huge volumes of data, and that data needs to be protected at all costs, particularly as criminals look for new ways to infiltrate and sabotage models. Failure to adequately protect AI models can lead to massive fines. For instance, Meta was fined over $1 billion in 2022 for improperly handling data.
Then there are the various new governance guidelines and regulations emerging all the time, reshaping how companies create, manage, and implement AI tools. To overcome these AI adoption challenges, companies need an end-to-end governance strategy.
That means building policies for mitigating risks like bias, misuse, and privacy infringement, running regular fairness audits, tracking AI outcomes, and even developing internal AI committees. Transparency is particularly important for companies that want to side-step the challenges of black box AI models. Making AI’s decision-making process transparent builds trust, satisfies stakeholders, and keeps regulatory scrutiny at bay.
The good news is that many business leaders have already recognized the importance of governance, with around 61% of senior executives in an IBM study saying they’re actively prioritizing responsible AI strategies.
2. Data Quality, and Availability Issues
A number of AI adoption challenges revolve around data – but it’s not just keeping data secure that’s an issue – it’s making sure you have the right data to fine-tune your models. In one report, around 42% of respondents said their organization didn’t have enough proprietary data to train models.
While there are some great companies out there, like Microsoft, Google, and Amazon, that already pre-train their models with huge volumes of information, infusing a bot with your own data increases its value, and helps to tackle bias issues.
Companies are exploring creative solutions to this issue. Some are using data augmentation techniques to creatively enhance existing data sets. Others are exploring the benefits of “synthetic data” to fill in the gaps. For instance, healthcare companies frequently use simulated patient data to safely train AI models without compromising patient confidentiality or regulatory compliance.
Strategic partnerships can also be helpful here. Joining forces with research institutions and non-competing businesses can sometimes enable access to more diverse data sets. The key to success will be figuring out how to build a robust data repository without accidentally exposing too much sensitive information to potential criminal attacks.
3. Overcoming Integration and Infrastructure Bottlenecks
Integration and infrastructure bottlenecks are one of the main AI adoption challenges many vendors are attempting to tackle right now. Companies worldwide are building innovative solutions designed to integrate seamlessly with existing systems (like Salesforce’s Agentforce agents, or Microsoft’s Copilots). Some companies, like Google, are even designing tools that tackle computing issues, reducing the need for advanced GPUs and networks.
Still, aligning new AI tools with legacy infrastructure is still complicated. Many larger companies deal with a tangled web of legacy databases, vintage ERP systems, and proprietary software. As a result, about 70% of companies cite infrastructure issues as their biggest hurdle to adoption.
There’s no easy solution here, but some companies are attempting to tackle challenges by experimenting with “pilot” AI systems in sandbox environments intended to minimize disruption. Others are shifting more of their technologies into the cloud, to ensure they have the tools necessary to bridge the gaps between workflows, without major implementation hurdles.
4. AI Adoption Challenges: Skill Gaps
AI might be everywhere today, but the people who know how to actually use the technology effectively aren’t easy to find. Skill shortages are ranked as one of the top AI adoption challenges for companies worldwide. Data scientists, machine learning engineers, and implementation specialists are in short supply, and consulting partners can only offer so much support.
The easiest solution to this issue is to focus on upskilling existing employees. Offer team members access to specialized training programs, certifications, workshops, and courses. Give them a safe environment to experiment with AI tools without risk.
Take AT&T’s massive upskilling initiative, which provided hands-on AI and tech training to tens of thousands of employees. This not only created an internal pipeline of AI-savvy employees, but it also helped to reduce fears around job displacement.
Strategic partners can be helpful too. AI vendors, startups, and academic institutions can give organizations access to crucial expertise. Additionally, embracing low-code or no-code AI tools enables employees without extensive technical backgrounds to deploy and customize AI solutions effectively.
5. Change Management and Employee Resistance
Fears about AI solutions eventually replacing human workers are growing. While most AI leaders promote AI as a tool for “augmenting” rather than “eliminating” human employees, staff members are concerned. In fact, one study found that 42% of enterprise leaders think AI adoption is tearing teams apart. The same study found many millennial and Gen Z employees actually admitted that they were undermining or sabotaging adoption attempts.
Concerns about job displacement aren’t going away, but companies can take a proactive approach to managing them. A robust, people-first approach that shows employees they have a future in a world enhanced by AI makes all the difference.
Companies effectively overcoming AI adoption challenges are creating dedicated AI champions across departments to drive alignment and enthusiasm. They’re investing in clear communication, transparency about job changes, and ongoing training and upskilling initiatives.
More importantly, these leading companies are also listening to their team members – paying attention to, and addressing their concerns actively over time. Prioritize alignment, collaboration, and trust-building, and you’ll pave the way for smoother, more successful AI adoption.
6. Financial Justification
This might not seem like one of the most significant AI adoption challenges. After all, there are plenty of reports and case studies that highlight the potentially massive ROI of AI tools. But initial experiments with AI solutions among enterprises haven’t always generated great results.
Companies investing millions into AI tools, training, and even custom software development, can’t afford to wait around to see results. But ROI rarely materializes overnight.
How do successful companies navigate this? It starts by aligning AI initiatives closely with concrete business objectives. Pilot programs or proof-of-concept projects are often helpful – allowing companies to experiment and determine when and how to scale.
Setting clear KPIs can also help. Instead of looking for vague examples of “transformation” leading businesses need to track quantifiable metrics, like reductions in operational costs, increased revenue per customer, or massive efficiency improvements.
Turning AI Adoption Challenges into Strategic Opportunities
AI adoption in the enterprise is definitely increasing – but that doesn’t mean companies won’t face hurdles along the way. The biggest AI adoption challenges remain consistent across industries – but these issues also represent opportunities.
Companies that make the decision to tackle the hurdles head-on, with robust governance, training strategies, or strategic partnerships, will see bigger results from AI adoption faster – while minimizing potential risks. Find out why companies are still investing in AI despite the challenges with this guide to enterprise AI benefits. Alternatively, find out how you can successfully scale AI solutions in the enterprise, with this guide to long-term AI success.
