Corporations are spending big on AI. According to IDC, total business investments in generative AI are expected to increase 94% this year to reach $61.9 billion. However, just investing in AI does not guarantee a payoff.
In fact, as Laura Clayton McDonnell, President of Corporates at Thomson Reuters explains, new research from McKinsey finds that the vast majority of companies implementing AI have seen no significant bottom-line impact from the technology. These findings are echoed in our Future of the Professionals Report 2025, which found that although 71% of C-suite leaders say their company has invested in AI tools in the past year, and a further 18% plan to invest in AI within the next 12 months, just 19% of corporate professionals say their department has a clearly-defined AI strategy in place.
As investment in AI increases, it becomes ever more important for businesses to develop an AI strategy to maximize the value of their AI investments. A solid AI strategy will define the investment, training, and guardrails necessary for departments to effectively utilize the technology. An excellent AI strategy can drive top-line growth for companies. However, this growth will never occur without a clear plan.
Based on our experience at Thomson Reuters helping large corporations integrate AI into their tax, legal, risk, compliance, and HR workflows, we’ve seen what can happen when businesses have a clear strategy in place and how expectations can be missed without a plan. We recommend that organizations follow seven key principles to maximize the effectiveness of new AI tools they adopt. These principles emphasize the necessary steps—from developing protocols to training employees—that are essential for achieving your business’s core objectives.
The seven key principles for a successful AI strategy
Align your AI strategy with your firm’s overall strategy
AI initiatives must directly support the core objectives of in-house departments and complement their organization’s overarching AI strategy. This includes reducing legal and regulatory risk exposure, improving compliance, streamlining procurement, and speeding up contract review. Leaders should also consider how to reinvest the new time savings into handling a greater volume of value-added work.
Corporate leaders should consider where they want their in-house functions to be in a year. They should begin by identifying the obstacles that are now blocking their departments’ strategic progress.
Establish clear AI goals and objectives
Leaders should convert broad company goals into specific, measurable, achievable, relevant, and time-bound (SMART) AI objectives. For example, if a departmental goal is to improve regulatory compliance monitoring, a good AI objective could be to boost department efficiency in handling particularly tedious manual tasks, like drafting updated contracts or researching local tax laws. Additionally, leaders should encourage input from different departments on how AI can support these goals. They should also promote early experimentation with AI tools across legal, tax, and compliance teams.
Corporate leaders should identify and prioritize an AI goal that tackles the departments’ most urgent issues, developing relevant initiatives to achieve realistic objectives.
Create a data strategy
Remember that AI’s effectiveness depends on the data it is trained on or references. Leaders should ensure their departments develop strong strategies for managing, securing, and utilizing data for AI purposes—while upholding confidentiality and legal privileges.
Leaders should work with internal teams and external resources to establish the best data strategy for their organization, considering factors like company size, industry, structure, and best practices.
Establish strong governance & ethical frameworks
It’s essential to establish clear policies on data privacy, security, and responsible AI use. This involves creating processes for identifying bias and ensuring accuracy. When verifying GenAI outputs, it is important to clearly define policies related to confidentiality, transparency, and the preservation of legal privileges.
Leaders should assign AI responsibilities within each department and establish approval procedures for new AI tools that consider the specific ethical and legal issues of each department. Another important step is to develop and document standard protocols for selecting AI tools and verifying outputs.
Invest in talent and training
While AI can be a powerful tool, people drive its success. Leaders should train staff not just on how to use AI tools but also on how to develop judgment to review AI outputs critically — an essential skill for building trust and ensuring compliance. Leaders must also identify skills gaps within the organization, address professional liability concerns, and foster a culture of responsible experimentation. They should also communicate openly about the organization’s overall AI strategy and its benefits to gain better buy-in from all professionals.
Businesses should consider using free or low-cost training resources from professional associations and technology providers. This is a cost-effective way to boost your organization’s training programs.
Prioritize and pilot
Leaders should identify two or three high-impact, high-feasibility pilot projects involving AI tools. Ideally, these projects should address critical pain points, such as contract analysis, regulatory monitoring, or tax provision automation. Early successes can build momentum, offer important lessons, and demonstrate the value of a solid AI strategy — all of which will facilitate broader adoption. Piloting new AI tools should be viewed as an ongoing process, incorporating feedback from frontline professionals.
Measure, iterate and adapt
Leaders should establish key performance indicators (KPIs) to measure the success of AI initiatives in areas like reducing compliance incidents, speeding up risk detection, and increasing the accuracy of tax provisions. It’s also important to measure AI initiatives against departmental goals to better evaluate their impact on overall performance. Additionally, regularly reviewing progress and being ready to adjust strategies is crucial as regulatory requirements, technology, and organizational needs change.
You should routinely track each department’s progress using simple before-and-after comparisons. This approach can often show return on investment without the need for complex analytics.
The winners in the AI arms race are those organizations that have all the elements of their strategic plan both mapped out and carefully implemented. We think that careful planning is worth it. With AI the risks of getting it wrong may look high but so are the potential returns.
Read more:
Seven keys to a successful AI strategy for corporate enabling functions
