The pressure to adopt AI systems or risk falling behind is growing by the day, with many business leaders believing AI is a one-size-fits-all solution to solving their biggest pain points. However, according to The Rand, over 80% of AI projects are predicted to fail.
Unfortunately, prioritizing AI adoption tactics that work in the real world for real businesses tends to get lost in the hype of integrating the latest technology, which can have serious ramifications for business strategies and hinder ROI.
A recent McKinsey study cites that 92% of companies plan to increase their investments in AI over the next three years. However, if businesses are too eager to integrate AI systems without strategic planning, we could see millions of dollars going down the drain.
Investment failures often come down to a few major stumbling blocks, including a lack of emphasis on matching tools and workflows to achieve specific business goals, ineffective change management processes and insufficient metrics for measuring success.
In order to be part of the 20% of adopters getting it right, having a targeted business roadmap which includes these three key strategies is crucial.
Matching Tools To Workflow Processes
Many AI integrations fail because adoption happens too fast, without keeping core business goals top of mind. To avoid this common pitfall, starting small and scaling slowly is the name of the game. Tools should always be selected with this scalability in mind, as well as a clear focus on how these tools will help to achieve these core goals.
AI operationalization is at the heart of driving successful deployment. Before choosing AI tools, it is worthwhile considering if the workflow process should be redesigned to incorporate AI, as simply adding AI to a sluggish and inefficient system is unlikely to improve much.
Along with this, it’s important to decide which tasks within the workflow should be assigned to AI systems, and which to leave to humans. AI tools are excellent at speeding up decision-making and increasing operational efficiency, but not very good at coming up with creative solutions or displaying empathy and emotional intelligence.
As an example, Siemens uses AI to match candidates to jobs based on their skills and experience, but people still handle interviews and make the final hiring decisions because human connection and discernment matter in recruitment.
When it comes to selecting the right AI tools for integration, consider the following:
- Interoperability: In the best-case scenario, integration won’t require too many changes to an existing IT ecosystem (provided it functions well already). AI developers should work to integrate and customize AI tools to interoperate with DevOps tools wherever possible to avoid adoption friction.
- Standardize tools: Ahead of operationalizing AI, standardizing models is an essential consideration. As an analysis from Harvard Business Review iterates, standardization is pivotal to streamlining AI development for streamlining MLOps development.
- Avoid data silos: A common roadblock to AI adoption is the siloed nature of poorly managed data. Without an effective data management strategy in place, as well as data that’s organized and usable, AI cannot function optimally because of missing or incomplete datasets. Some remedies to this roadblock include cross-department collaboration and a centralized data governance framework.
Creating A Change Management Plan
While the allure of AI is its intelligence and autonomy, AI tools still need people to guide their development, set ethical boundaries, and provide context, as well as identify and correct them when they make mistakes.
The rate of change affecting businesses has risen steadily since 2019, up to 183% over the past four years and by 33% in the past year alone. However, if almost half of C-suite executives don’t feel prepared for the accelerating rate of technological change, how can they expect their employees to embrace the AI revolution and spearhead the movement?
If companies want to avoid investing in ghost systems, they need to invest in structured, staggered change management initiatives, with appropriate leaders appointed to lead this process from start to finish.
An important part of this change management process involves staff training, which should be personalized, as workflows tend to vary from one department to another. One mass training session where individuals don’t get any hands-on training is bound to lead to mistakes, frustration, and apathy among employees.
For the AI solution to continue to be successfully integrated and leveraged, training should also be ongoing, as there are likely to be a number of teething problems as tools start being used in day to day operations.
Defining Metrics For Success
Not measuring success is like building a ship without a compass: significant resources have been laid down, but there’s no way of knowing if it’s sailing toward its destination or drifting in the opposite direction.
If a business invests in a new AI initiative, including training, integration and developer support, there needs to be clearly defined metrics for measuring how well the AI model functions, as well as whether or not it’s helping to reduce costs and increase revenue.
Model-Based Evaluation
The model-based evaluation measures the accuracy of an AI model in terms of how well it performs tasks such as identifying defects or making predictions. Here, it’s key to measure latency, meaning how quickly the AI model makes decisions and processes information.
For models that produce bounded outputs (responses limited to a specific range or set of parameters), computational-based model metrics are highly effective for evaluation.
For unbounded models capable of generating original, unexpected and even harmful outputs, a more subjective model of evaluation is necessary. Here, large language models (LLMs) can be used as auto-raters which assess aspects such as creativity, accuracy and relevancy.
Model Quality KPIs
Model quality KPIs include metrics such as uptime, error rates and the frequency of incorrect predictions made. It’s important to note that the exact metrics used will depend on the AI platform, as well as the type of generative AI model that’s chosen.
Business Operations KPIs
These KPIs measure the impact of an AI system on businesses outcomes and processes. Operational KPIs will differ considerably from industry to industry, for example, within a customer service setting, KPIs include the average handling time or a customer satisfaction score. In product or service discovery, this could be the click-through rate and revenue per visit.
Instead of rushing with AI to get ahead of the hype, organizations should take a measured and operationalized approach to it for maximum impact and ROI. These strategies are designed to help businesses bridge missing links in AI adoption so they’re not just investing in new tools, but profiting from them, too.
