Mitigating AI risk

Successful AI implementation requires strategies that mitigate potential technical, operational, and strategic risks. Many enterprise organizations are already creating centralized AI committees or councils to guide their organization to the AI-first enterprise.

These groups are working to establish governance frameworks that ensure AI systems operate reliably, ethically, and in alignment with business objectives while maintaining appropriate human oversight and control. Like any tech deployment, without proper guardrails, AI will enable and amplify bad behaviors just as easily as it does good behaviors. 

As such, these AI councils are working to mitigate risk across several key areas.

Data privacy and security

Organizations should implement data protection measures, including encryption, access controls, and audit trails that demonstrate compliance with regulatory requirements—and put these same expectations on AI vendors with whom they partner. This includes establishing clear policies for data collection, usage, and retention that respect customer privacy while enabling AI system effectiveness. It also includes ensuring the right permissions, both for who has access to the AI tech and what the AI tech has access to in terms of data and systems.

Model reliability and performance

Organizations should establish performance baselines, implement continuous monitoring systems, and develop procedures for model retraining and updates to ensure AI systems deliver accurate insights and recommendations—and put these same expectations on AI vendors with whom they partner. This includes creating fallback processes that maintain business continuity when AI systems require maintenance or adjustment.

Change management

Selecting AI solutions that are purpose-built for GTM teams makes it easy for users to get quick and lasting value. Because these solutions are designed around how GTM teams work and what they need to be successful, they often map into existing motions, and both leverage and enhance existing frameworks and processes to make working with AI familiar to Sales users.  

Without this, implementations can require comprehensive training, clear communication about AI's role and limitations, and support systems that help team members adapt to AI-enhanced workflows. Organizations should also address concerns about job displacement while demonstrating how AI enhances, rather than threatens, career development.

Vendor risk management

Vendor risk management becomes critical as organizations increasingly rely on AI platforms and services provided by partners. This includes evaluating vendor financial stability, technical capabilities, security practices, and long-term roadmap alignment. Revenue leaders should look to work with vendors who engage as strategic partners, bringing both their technology and expertise to bear in helping their customers map (and achieve) their path to the AI-first enterprise.

Regulatory and compliance risk

Organizations must stay current with applicable regulations, industry standards, and best practices while implementing internal controls that demonstrate responsible AI usage—and put these same expectations on AI vendors with whom they partner. This includes maintaining documentation of AI decision-making processes and establishing procedures for addressing compliance inquiries or concerns.

Authenticity

Artificial intelligence is a relatively new area for most every organization, and building trust matters. Asking customers, partners, and employees for permission to do things with AI where it makes sense is a good best practice, as is telling them when AI is being used or that they are interacting with AI. This helps in earning the right to full automation by building trust in AI with all stakeholders at each step on the path to becoming an AI-first enterprise.

Design AI for today and the future

AI is delivering enterprise value today, but implementing it in a vacuum without thinking through the longer-term roadmap and journey to becoming an AI-first enterprise could cause missteps. Look for a partner with the technology and expertise to help map the path and bridge the gaps from current to desired state, implementing AI technologies that not only deliver enterprise value today but enable and advance your organization’s journey to the AI-first future.

Pilots and proof of concepts

Take advantage of trials and proof of concepts to ensure that an AI technology does what is being promised. When engaging in a pilot or proof of concept, look for how the AI vendor shows up. Are they operating as a strategic partner? Are they bringing insights and expertise to the table that are helping you think—or think differently—about your organization’s path forward? Does their product roadmap address future needs? These considerations help to ensure selection of both the right technology and partner for current and future AI needs.

Next Steps

Leading CROs are already deploying AI and achieving enterprise value, like to build org-wide competency around account intelligence to unlock value selling at scale. These CROs are enabling their teams with deep knowledge about customers—their priorities, needs, and challenges, and how they uniquely solve them—to ensure relevance at every stage of the customer lifecycle to drive both business and customer success today.

With ubiquitous access to this AI-enabled tailored point of view (POV) for each customer, GTM teams show up smarter than competitors, speak in the customer’s language, confidently lead with value, and attach to their top priority—to win. And customers win, too, with a consistent, world-class experience focused on their success in every interaction.

Revenue leaders looking to kickstart their organization’s journey to the AI-first enterprise can start a trial of Poggio to arm their GTM teams with tailored POVs to show up smarter than competitors and win.