What to look for in an AI implementation partner

For many enterprises, going it alone when launching major AI deployments is a daunting task. That’s where solid implementation partners can help.

AI, particularly agentic AI, is still relatively new to a lot of organizations, and having the expertise of an experienced partner can be extremely valuable.

A report commissioned by technology provider Lenovo and published earlier this year notes that professional services partnerships are dominant in terms of CIOs’ AI implementation strategies.

The study, based on a survey of 2,920 IT and business decision-makers worldwide, states that most organizations are leveraging professional services partnerships for AI deployment, driven by data management challenges, particularly the availability of high-quality data.

Good AI implementation partners need not be limited to big professional services firms. Smaller firms such as AI consultancies and startups can provide lots of value. Regardless, many organizations require outside expertise when deploying, monitoring, and maintaining AI tools and services.

“As a tech lead working with scalable NLP [natural language processing] and AI systems, I’ve seen firsthand that the success of AI adoption often hinges less on the technology itself and more on the quality of the implementation partner,” says Chirag Agrawal, senior software engineer and technology lead at online retailer and cloud services provider Amazon.

What should enterprises look for in an AI implementation partner? Here are some suggestions from technology leaders and other experts.

Expertise in your specific vertical

A one-size-fits-all approach does not work with many AI implementations. Different sectors have different requirements, particularly in the areas of regulatory compliance and data governance. A good AI partner needs to have a thorough understanding of the customer’s industry, otherwise issues can come up in a hurry.

“Many firms understand AI tools at a surface level, but what truly matters is the ability to contextualize AI within the nuances of a specific industry,” says Hrishi Pippadipally, CIO at accounting and business advisory firm Wiss.

“For example, in professional services and accounting, we’ve seen that deploying AI without understanding regulatory and client-data constraints leads to failed pilots,” Pippadipally says. “A strong partner bridges this gap by knowing not only the technology, but also how it applies to your business model, compliance requirements, and workflows.”

Wiss recently partnered with AI-native accounting platform Rillet to deliver a new type of accounting model for growing businesses.

“I’ve worked with partners who were brilliant in AI models but lacked a deep understanding of the regulatory nuances and data governance frameworks specific to our industry,” Amazon’s Agrawal says. “That gap creates hidden risks, particularly when it comes to compliance and explainability.”

An effective partner must be able to balance innovation with the guardrails of security, privacy, and industry-specific compliance, Agrawal adds. “Otherwise, IT leaders will inherit long-term liabilities,” he says.

Amazon once worked with a partner that was great at NLP but had trouble fitting its AI models into the company’s strict compliance environment. “Their prototypes were impressive, but because they didn’t know our industry well, we had to spend extra time checking for regulatory issues,” Agrawal says. “This slowed things down and showed how important it is to have both technical skill and industry knowledge.”

Ability to smoothly blend AI into existing workflows

AI by its very nature is disruptive. It can change the way things get done and the roles people play in their organizations. Not all disruption is bad, but enterprises need to guard against interfering with critical business processes when deploying AI-powered systems.

Therefore, technology decision-makers need to consider whether potential AI implementation partners have the ability to new blend tools and services without interrupting key processes.

“The mistake many organizations make is focusing only on technical credentials or flashy demos,” Agrawal says. “What’s often overlooked and what I prioritize is whether the partner can embed AI into existing workflows without disrupting business continuity. A good partner knows how to integrate AI so that it doesn’t just work in theory, but delivers impact in the complex reality of enterprise operations.”

One AI partner Amazon used “focused on fitting their solution into our workflows right from the start,” Agrawal says. “They worked closely with our operations teams, learned how our data pipelines worked, and built their deployment to fit our systems. This made the process smoother, reduced downtime, and made the change almost unnoticeable for business users, which was a big reason for the project’s success.”

Cultural fit with your organization

A successful relationship with an AI implementation partner takes into account the impact AI will have on corporate culture.

“Most evaluation checklists focus on the technical side — security, compliance, data governance, etc.,” says Sara Gallagher, president of The Persimmon Group, a business management consultancy. “While that matters, too many execs are skipping over the thornier questions.

They treat AI like a tool or feature, but it’s really part of a system. That system is shaped by culture, politics, and informal workflows. It’s unpredictable, and that’s exactly why it requires advanced thought.”

When advising technology executives about how to work with AI partners, Gallagher suggests they ask how a partner would manage a gap between what data shows and what people feel.

“AI can measure sentiment, but it can’t feel the stakes of its decision,” Gallagher says. “Because it understands sentiment — but not emotion — it makes recommendations that make sense mathematically but could impact informal workflows in unpredictable ways.”

Something else to consider is how the partner and/or enterprise will measure whether the tool is helping or hurting corporate culture.

“CIOs often track cost and efficiency savings, technical performance, security risk, etc.,” Gallagher says. “But they may overlook whether AI is quietly damaging trust and engagement. The danger isn’t so much tools that employees choose themselves, like ChatGPT. It’s the ones that are inflicted on them.”

Willingness to share skills for lasting impact

A good AI implementation partner doesn’t just help a client with a project and move on. It takes time to learn about the needs of the client and its employees so it can deliver long-term impact by upskilling workers so they get the most value out of AI now and in the future.

“The right AI partner should bring a mindset of enablement rather than replacement,” Wiss’s Pippadipally says. “AI should enhance how people work, not create fear or disruption. In our experience, the most successful implementations pair automation with reskilling: freeing accountants from manual reconciliations so they can spend more time advising clients.”

“I look for partners who bring operational empathy — not just delivering models but committing to the cultural and organizational changes that AI requires,” Amazon’s Agrawal says. “The best AI consultants don’t just hand over code; they help upskill teams, build trust in AI outputs, and leave the organization stronger, not dependent. That ability to ‘teach us to fish’ is, in my experience, the true differentiator.”

Agrawal has seen how valuable it is when partners help Amazon teams build new skills. “For example, one consultant delivered models and also ran workshops with our engineers and product leads,” he says. “This helped our teams trust the AI results and continue the work on their own, so we didn’t end up with a confusing handoff.”

Deep knowledge of security and data privacy risks

AI initiatives come with a set of cybersecurity and data privacy risks. An implementation partner needs to know about these and be prepared to address them. In addition, organizations must ensure their partners are following best practices to secure critical data.

“While AI is a powerful tool that can transform organizations, careful implementation is critical to ensure organizations are safely and effectively leveraging AI,” says Tim Williams, vice chairman at physical security and investigative company Pinkerton.

“This is a conversation that involves stakeholders across the organization, from CIOs to CSOs and CEOs, as well as legal and [human resource] representatives,” Williams says. “Due diligence is critical in identifying the right AI tools and partners. It’s important to look into your potential partners to gauge their ethical track record and ensure they have a history of trustworthy behavior.”

It’s also important to examine how AI tools will impact privacy concerns, such as using AI-powered cameras in public areas, sharing sensitive information, how third-party or personnel will be captured, and so on, Williams says.

In addition, enterprises must ensure that the AI tools they are implementing carefully tie to organizational objectives. “Are the AI tools tailored to your needs?” Williams says. “Are you trying to regear functions that are better left to human judgment with automated tools that may be prone to hallucinations?”

Healthcare technology provider Metadoc worked with its partner, Careful Security, to develop an AI-powered musculoskeletal analysis platform. Protecting personal data is a major concern.

“AI systems deployed without privacy measures create major security vulnerabilities,” says Bruce Hoffman, CTO of Metadoc. Careful Security “established complete privacy protection through a policy which blocked all personal data from entering the model. The mobile device-based data collection of user biometrics and pain indicators and mobility data undergoes complete anonymization before the analysis starts.”

The system analyzes movement and posture patterns through data without accessing any personal identification information, Hoffman says. “The architectural system of Careful Security provides complete analytics and wellness improvement data and trend detection capabilities to clients while protecting individual privacy rights.”