Winning Executive Buy-In For AI Initiatives in the Enterprise – with Leaders from PwC, Genpact, CONVOY, and

Riya Pahuja

Riya covers B2B applications of machine learning for Emerj - across North America and the EU. She has previously worked with the Times of India Group, and as a journalist covering data analytics and AI. She resides in Toronto.

02 – Winning Executive Buy-In For AI Initiatives in the Enterprise@2x-min

AI has emerged as a powerful tool with the potential to revolutionize industries, drive growth, and unlock unparalleled opportunities for organizations. As AI technologies continue to advance, many businesses are keen on harnessing their potential to gain a competitive edge and stay ahead in the market.

However, despite AI’s promising possibilities, one crucial challenge that often arises is gaining executive buy-in for AI projects. Convincing top-level executives to invest in AI initiatives can be complex and multifaceted. Executives may have encountered unfulfilled promises or skepticism surrounding AI’s capabilities, leading to a hesitancy to embrace it fully.

A recent survey from KPMG shows that C-suite executives may still be hesitant regarding AI adoption. Two-thirds of executives polled reported feeling uncomfortable accessing or using data from advanced analytic systems. Although AI has made significant strides in specific companies, there is still a need to boost executives’ confidence in this technology.

A similar survey from Accenture put a finer point on the accelerating pace of technology as the source of executives’ anxiety. The survey found that, across six factors, 88% of C-suite officials surveyed said they were anticipating an acceleration in the rate of technological change from 2023 to 2024.

However, only 60% of executives polled saw the environment as an opportunity for growth, with nearly half (47%) reporting that their teams are not fully prepared for technological acceleration. In the same survey, not even a third (27%) claimed their organizations are ready to scale up generative AI.  

Organizations must navigate this hurdle to successfully implement AI projects with compelling strategies to win executive support. This article will explore practical approaches and insights to secure executive buy-in for AI initiatives.

The following article examines strategies and insights shared by leaders who appeared on ‘AI in the Business’ podcast in a special series focusing on best practices for winning buy-in from enterprise management for AI initiatives. These leaders include Bret Greenstein, a partner specializing in  Cloud and Digital Analytics Insights at PwC; Convoy Chief Technology Officer at Dorothy Li; CEO & Co-founder Zohar Bronfman; and Genpact SVP and Global Business Leader Amaresh Tripathy. These insights include:

  • A personalized, practical approach in engaging CIOs for data and AI solutions: Tailoring discussions with CIOs by actively listening to their concerns, asking open-ended questions, and proposing practical data and AI solutions aligned with individual needs, fostering a more personalized and effective engagement strategy.
  • Viewing AI as a tool to augment human capabilities: Leveraging AI as a tool to empower human capabilities and not to entirely replace them, turning humans into superheroes for their roles. 
  • Utilizing a strategic 2×2 matrix framework to tailor AI strategies: Implementing a strategic 2×2 matrix framework to assess organizations based on innovation/scale and time dimensions ensures alignment with specific goals and constraints for successful AI adoption
  • Prioritizing quick wins while transparently addressing challenges:  Accelerating AI adoption by prioritizing quick wins with simple use cases that can bring measurable and tangible value and addressing challenges, including the potential for failure. 

A Personalized, Practical Approach in Engaging CIOs for Data and AI Solutions

Episode 1 – Getting the C-Suite from ‘Spreadsheets and Gut Feelings’ to Data-Driven Insights

Guest: Bret Greenstein, Partner, Cloud & Digital – Analytics Insights at PwC

Expertise: Cloud computing, business transformation, and data analytics

Bret shares how to approach discussions with Chief Information Officers (CIOs) or other business stakeholders when introducing data and artificial intelligence (AI) solutions. He suggests there may be more effective ways to engage these stakeholders than the typical approach of starting with ROI or architecture. Instead, the key is understanding the individual stakeholders’ specific concerns, challenges, and goals and then tailoring the data and AI solutions to address those needs.

He advises asking stakeholders about the top three issues that bother them and what they believe their competitors are doing better. It allows the focus to be on identifying personalized solutions for each stakeholder based on their particular job responsibilities and business objectives. By understanding their threats and concerns, the speaker believes it becomes easier to propose data and AI strategies to help them navigate and solve those problems.

Bret continues his point, noting that the key is to engage the stakeholders in open-ended conversations to understand their aspirations, fears, and goals for the business. By doing so, one can identify areas where data and AI could significantly impact and address specific challenges.

“So it requires an open ended dialogue and a style of communication which mirrors your understanding of data and its capability and AI its capability to business objectives. So it’s not just getting to know the person and having them tell you what their performance criteria are. It’s asking them open-ended questions, getting them talking about what they wish they could be doing for the business, what they fear their competitors might be doing, what they, you know, what would, you know, really get, you know, big game-changing in the annual report?”

– Bret Greenstein, Partner, Cloud & Digital – Analytics Insights of PwC

The speaker emphasizes the importance of listening actively and attentively to the stakeholders’ responses. By showing genuine care and interest in understanding their concerns and desires, the speaker suggests that the stakeholders will become more open and willing to share valuable information.

Lastly, Bret suggests that when leaders have a good grasp of the stakeholders’ needs and challenges, the next step is to propose data and AI solutions that could address those specific problems.

He emphasizes that the proposals must be practical and realistic, ensuring they align with what data and AI can achieve by citing past examples. 

“Be really, really practical: ‘I think we could do something with this,’ ‘I’d love to try that.’ ‘I know someone who did this,'” Bret suggests, listing welcoming introductions to various suggestions. 

Viewing AI as a Tool to Augment Human Capabilities

Episode 2 – Winning Buy-In From Logistics Leaders for AI Projects 

Guest: Dorothy Li, Chief Technology Officer, Convoy

Expertise: Data analytics, machine learning, and digital transformation

Dorothy begins her podcast appearance by addressing the executive audience on how she approaches conversations about AI with clients in the logistics industry, starting with highlighting the benefits of AI specifically tailored to their industry.

She explains that throughout the logistics space, a significant amount of manual labor and repetitive tasks can be automated using AI. While stating she prefers to view AI as a tool that augments human capabilities rather than replacing humans entirely, Dorothy further explains that she likens AI to a technology that can transform humans into superheroes by empowering them to focus on tasks that require a human touch and critical thinking.

She then focuses on examples of how AI can streamline operations in logistics. For instance, AI can handle tasks like tracking shipments, determining when a facility will be open, and managing workflows for shippers. 

By offloading these repetitive and labor-intensive tasks to AI-powered systems, humans can concentrate on more complex and value-added activities. For example, truck drivers may still prefer interacting with humans, so customer service operators can focus on providing personalized support and addressing exceptions that require human intervention.

Dorothy goes on to emphasize the foundational role of data in implementing these use cases and more machine learning projects. Before an organization can begin to contemplate adopting these use cases into the enterprises, Dorothy stresses that senior leadership must understand the state of the organization’s data.

Such an evaluation involves looking at the tech stack, extant data platform, and general IT infrastructure in place and determining how mature the organization’s data strategy is. To facilitate this process, having a chief data officer or dedicated personnel focused on data management can be helpful.

“So the very first step every organization needs to take when you’re thinking about taking on AI and machine learning is looking at the data and needs and looking at where you are in your data platform and where you are in your data journey. So if you don’t already have a chief data officer, or you have folks that are really focused on where your data is, and whether you have way bigger your data, infrastructure, data strategy  usually a good starting point is to start with that data.”

– Dorothy Li, Chief Technology Officer at Convoy

By assessing the state of their data, organizations can identify any gaps or challenges that need to be addressed before moving forward with AI projects. The quality and availability of data directly impact the effectiveness of AI algorithms, so understanding the data landscape is a critical first step.

Additionally, the speaker suggests that some external companies or services can assist in this evaluation, especially for organizations needing a well-established IT and data platform or more external expertise.

Her advice to external vendors/consultants and internal project leaders is to demonstrate AI’s value when talking to C-suite executives. She stresses the importance of discussing AI projects with a purpose, emphasizing tangible benefits rather than treating technology as a standalone concept. The goal is to move beyond a plug-and-play mentality and highlight the broader value that AI can bring to the organization.

Utilizing A Strategic 2×2 Matrix Framework to Tailor AI Strategies

Episode 3 – Balancing AI Innovation with Project Urgency

Guest: Amaresh Tripathy, SVP and Global Business Leader, Genpact

Expertise: Business and Customer Analytics, Data management

In our podcast interview with Amaresh Tripathy, Genpact’s then SVP and Global Business Leader, he begins his podcast appearance by discussing the communication challenges between business-oriented individuals and technical experts when adopting AI solutions. 

He highlights the need for “bilinguals,” referring to people who can understand both the context of the business and the technical aspects of AI. These bilingual individuals are crucial in bridging the gap and facilitating effective communication between business stakeholders and technical experts.

Aramesh points out that the fundamental issue is that the parties involved –i.e., the buyer (business stakeholders) and the vendor or catalyst (technical experts) –, often have different perspectives and priorities. The buyer is focused on solving business problems and may need to be more aware of the technical complexities of AI. On the other hand, the vendor or catalyst tends to have more enthusiasm about the possibilities of AI technology.

Due to this disparity, it can be challenging to initiate conversations about AI initiatives with C-suite executives or decision-makers. Aramesh acknowledges that many individuals within the organization and external vendors may be eager to pursue AI projects, but approaching C-suite stakeholders requires a thoughtful and strategic approach.

When starting conversations with C-suite executives, Aramesh advises finding common ground and speaking their language is essential. Instead of overwhelming them with technical jargon, focus on the business value and the potential impact of AI initiatives. 

Just as AI vendors should work with customers in an educative sale, stakeholders should start by understanding their pain points, objectives, and concerns, then present AI as a solution that aligns with their strategic goals and the company’s competitiveness.

He further introduces a framework to think about AI and innovation within organizations. He suggests visualizing it as a 2×2 matrix with two axes, innovation/scale and time.

Innovation/Scale Axis: 

The speaker explains that every organization falls somewhere along the innovation/scale axis. On one end of the spectrum, there is a desire for high innovation, where companies focus on pushing the boundaries and coming up with groundbreaking solutions. On the other end, there is a focus on scale, where companies prioritize executing established strategies efficiently and achieving results quickly.

Time Axis: 

The second dimension is time, which refers to the timeframe in which the organization wants to achieve its objectives. Some companies may have a short-term focus driven by immediate shareholder pressure or quarterly financial targets. Others may have a long-term perspective, working towards ambitious goals that may take years or decades to accomplish.

The speaker emphasizes that understanding these two dimensions is crucial for making strategic decisions around AI and innovation. It will approach AI initiatives differently depending on where a company falls on the innovation/scale and time axes.

  • A: High Scale, Short Timeframe: If organizations prioritize scale and have limited time, they will focus on achieving results quickly and generating value at scale. AI can be used to address immediate challenges and drive rapid growth.
  • B: High Scale, Long Timeframe: For organizations focusing on scale and a longer timeframe, efficiency and optimization are crucial. They may use AI to streamline existing processes and achieve incremental improvements.
  • C: High Innovation, Short Timeframe: Organizations desiring innovation but facing a limited timeframe must balance innovation and efficiency. They may invest in a large workforce to solve problems quickly while generating value.
  • D: High Innovation, Long Timeframe: Organizations seeking high innovation and extended timeframes can invest in more exploratory and research-focused approaches. They may take more risks and focus on inventing new solutions to differentiate themselves in the market.

Lastly, Amaresh insists candid conversations across leadership about where the organization stands in these dimensions are essential, as the 2×2 framework helps foster informed ROI discussions about AI initiatives. By understanding the innovation/scale and time dynamics, organizations can tailor their AI strategies to best align with their goals and constraints.

Prioritizing Quick Wins While Transparently Addressing Challenges

Episode 4 – Winning Executive Buy-In for AI Projects – with Zohar Bronfman of

Guest: Zohar Bronfman, CEO & Co-founder at

Expertise: Analytics, AI, Deep Learning and Computational Psychology

Zohar begins his appearance on the podcast by delving into how to speak truth to power in the AI adoption process. He acknowledges that many executives have become cynical and doubtful about AI due to unfulfilled promises of it magically solving all business problems. Therefore, Zohar suggests that the first step in the AI journey for executives is to strive for fast value.

He advocates starting with simple use cases that can quickly demonstrate measurable and tangible value. By achieving quick wins, organizations can build internal trust and overcome skepticism. Demonstrating the effectiveness of AI projects early on can increase commitment and support for more extensive AI initiatives in the future.

Furthermore, the CEO and Co-founder highlights a unique property of AI projects where they can encounter failure even when implemented correctly. It In explaining how projects can be likened to the startup concept of “failing fast” to learn and improve, Zohar emphasizes the importance of embracing and learning from the possibility of failure to pave the way for long-term success in AI adoption.

He also shares an approach he feels vendors should take when engaging with clients on AI projects. He points out that from his perspective and experiences, long-term capability-building strategic projects are not the right way for vendors to start an engagement or partnership, except in specific extreme cases like governmental projects or highly specialized and oriented projects.

“Because as we build the company, we many times internally face a dilemma over whether we should optimize growth and revenue at all cost or optimize good revenue, for lack of a reverse strategy. I’m definitely part of the second camp in the sense that when I talk to an executive, I’m going to be painfully transparent with them; I’m going to tell them exactly what they should expect. I’m highlighting the painful elements that are an inherent part of implementing AI. I’m talking about the statistics of failure. Even if everything’s done correctly, I’m talking about the realistic timelines for the entire setup of whatever that might be the use case, the platform, and the training.”

– Zohar Bronfman, CEO & Co-founder at

Given his background, Zohar tells the executive audience that an organization seeking a tangible, AI-enhanced enterprise capability that becomes an infrastructural cornerstone of its strategic edge may ultimately need to build that capability in-house. 

Building such a strategic capability may involve taking bold risks and investing in internal resources to create a foundation that aligns closely with the organization’s long-term vision. If the organization has the vision and the stomach for the risk, seeking outside help may sacrifice significant opportunities. 

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