Explainability in AI is the process of making AI systems transparent and understandable to humans. It's the bridge between what the model does and why it does it—a must-have in any business using AI to make decisions.
Explainability (AI) refers to the ability to understand and communicate how and why an AI model reaches specific decisions. Instead of getting a mystery output from a black-box model, explainable AI (XAI) gives people—your team, your regulators, your clients—visibility into how inputs are processed, features weighted, and conclusions made.
Explainability can range from visual feature importance charts in machine learning models to step-by-step reasoning in large language models. It helps strip the magic out of the machine so teams can debug problems, audit ethical implications, and trust the results.
Think of it as a post-game replay. You don’t just care about the final score—you want to see how the play went down, especially if someone fumbled on the 1-yard line. In AI, that fumble might mean a false rejection for a loan applicant, a biased hiring recommendation, or a wildly inaccurate forecast. Explainability helps catch that in time.
Business teams are adopting AI faster than they can sometimes spell "algorithm." In 2024, 78% of companies reported using AI in at least one business function, especially in marketing, sales, and service ops, according to McKinsey’s State of AI report.
That’s a whole lot of decisions being partially or fully influenced by machine outputs. But without explainability baked in, a few things can (and do) go sideways:
In practical terms, explainability helps your business get better results with less risk. And considering that 41% of organizations using AI have already experienced adverse outcomes due to lack of transparency (Gartner, 2023), this is not a “nice-to-have.” It’s survival mode for scaling responsibly.
Here’s a common scenario we see with marketing teams at SaaS firms or agencies:
You're running lead scoring automations powered by a machine learning model. The model was trained to prioritize certain behaviors—like webinar attendance, email clicks, and site visits—without much human tuning.
So far, so good... until sales starts complaining that 40% of the “hot leads” don’t convert. After a backlog review, you realize that the model was overweighting behavior signals and completely ignoring buyer firmographics, deal size, or prior relationship history.
Here’s how explainability can get you out of this mess (and ideally prevent it):
This pattern repeats across industries: law firms with risky intake automations, MSPs with chatbots that hallucinate ticket priorities, or eComm brands using personalization engines that misfire because they were never told to avoid certain biases. Better explainability = fewer black-box decisions that leave teams guessing.
At Timebender, we believe good AI is like good coffee—strong, reliable, and doesn’t keep you up at night wondering what went in.
We teach teams how to build explainability into their AI workflows from the start. Whether you’re using off-the-shelf tools like OpenAI, Jasper, or Zapier AI, or running custom LLM apps, we help you:
Ready to stop trusting your AI like it’s a psychic and start managing it like a pro? Book a Workflow Optimization Session and let us help you build smarter, clearer AI systems that won’t burn your team (or your budget).
1. Prevalence/Risk: Lack of Governance and Transparency Leading to Adverse AI Outcomes
Figure: 41% of organizations deploying AI had experienced an adverse AI outcome, often due to lack of oversight or transparency.
Year & Source: 2023, Gartner (reported in user query context)
Business Implication: For regulated or trust-sensitive industries, inadequate AI governance and explainability significantly increase risks like erroneous automated decisions and compliance failures.
2. Impact on Business Functions: Growing AI Use in Marketing, Sales, and Service Operations with Explainability Needs
Figure: 78% of surveyed organizations reported AI use in at least one business function in 2024, notably marketing, sales, and service ops.
Year & Source: 2024, McKinsey State of AI Survey
Business Implication: AI adoption in customer-facing functions is surging, elevating the need for explainable AI to ensure accountability, performance, and safe deployment.
3. Improvements from Implementation: Explainable AI Market Growth Driven by Risk Reduction, Compliance, and ROI
Figure: The Explainable AI market reached approximately USD 9.54B in 2023 and is forecasted to grow at a CAGR of 18.22% through 2034.
Year & Source: 2023–2034, Precedence Research
Business Implication: Adoption of explainable AI reduces bias, improves auditability, and boosts operational ROI—especially for businesses in legal, marketing, and service delivery.
