Innovation and Technology
AI Agents Need to Be Managed as If They Are Human

Agents Need Organization, Too
The Rise of Agentic AI
This year’s hot topic is agentic AI, and the specific tasks or services that AI agents can accomplish with minimum human intervention. The question is, how prepared are organizations to make AI agents work as intended?
The State of Agentic AI
A recent KMPG survey shows that more than half (51%) of organizations are exploring the use of AI agents, while another 37% are piloting AI agents. However, only 12% have deployed AI agents for use. Agentic AI may provide a path forward to returns on AI investments. "Faced with growing economic pressures, investor expectations, and the need for greater efficiency, business leaders are earnestly working to match their genAI goals with feasible implementation strategies," the KMPG authors stated.
Preparing for Agentic AI
Companies and their technology teams "are not quite ready, but there’s appetite from both technology teams and business teams to lean in," said Rodrigo Madanes, global innovation AI officer for EY. Two stages are necessary to prepare: formulating an agentic AI strategy and setting up infrastructure to implement agents – an agentic AI framework – following compliance processes.
Empowering, Delegating, and Verifying AI Agents
Michele Franceschini, head of AI communications engineering at Bloomberg, emphasized the importance of empowering, delegating, and verifying AI agents. "Empowering AI agents means ensuring they have access to the key elements they need to operate effectively," he explained. "Common examples are data and analytics. Are your data and analytics accessible programmatically? Is your data well-governed, clean, and managed scalably?"
Delegation and Verification
Delegation, another fundamental management tenet, is also essential to managing organizations of AI agents. "Delegation requires trust, and one key difference with agentic AI workflows is that AI agents are not perfect and are nondeterministic," said Franceschini. "The goal then becomes to build a robust delegation network utilizing imperfect, nondeterministic agents and a key challenge is that most engineering organizations require a cultural shift to accomplish this."
The Promise of Agentic AI
When properly managed, AI agents can prove to be really good workers. "Imagine a world where your best employee amplifies your work, learns faster than anyone else on your team, and helps you deliver assets like videos with unmatched speed and precision," said Chris Savage, CEO and co-founder at Wistia. "That’s the promise of agentic AI – digital teammates designed to learn from you, adapt to your needs, and revolutionize the way work gets done."
The Need for Cultural Shift
At the same time, a cultural shift is needed to help the productivity of AI agents measure up. "I believe most companies are not ready to support agentic AI because it fundamentally changes the workflow and how that set of actions is handled between humans and machines," said Arnab Mishra, CEO at Xactly. "I also think that most employees have not been thoroughly educated yet on how to truly understand how AI can help them do their jobs better, and thus are likely to be resistant to change."
Conclusion
Agentic AI has the potential to revolutionize the way work gets done, but it requires a cultural shift and a well-thought-out strategy. Organizations must empower, delegate, and verify AI agents to ensure they are working effectively. "The winners will be the companies that use it to go faster. The ability to delegate routine tasks to AI so humans can focus on work that requires creativity, strategy, and empathy will be a massive differentiator," said Savage.
FAQs
- What is agentic AI?
Agentic AI refers to the use of artificial intelligence agents that can accomplish specific tasks or services with minimum human intervention. - How prepared are organizations for agentic AI?
According to a recent KMPG survey, more than half of organizations are exploring the use of AI agents, while another 37% are piloting AI agents. However, only 12% have deployed AI agents for use. - What are the key elements to prepare for agentic AI?
Empowering, delegating, and verifying AI agents are crucial to ensure they operate effectively.
Innovation and Technology
Process Drives B2B Growth

From Strategy to Success: How Process Drives B2B Growth
The Importance of Process in B2B Growth
B2B organizations often find themselves at a crossroads between strategy formulation and strategy execution. The harsh reality is that many fail to transform their well-crafted strategies into tangible results, a disconnect stemming from a fundamental oversight: the underestimation of process as a critical lever for execution. Process is not merely a conduit for efficiency; it’s the backbone of converting strategy into results. Without a robust process in place to develop and execute strategy, reaching your goals becomes a herculean task. This necessity becomes even more pronounced when executing a customer-obsessed growth strategy in which the focus intensifies on delivering value at every customer touchpoint.
The Disconnect between Strategy and Execution
Why do so many organizations falter in executing their strategies? The answer often lies in the gap between those who devise strategies and those tasked with implementing them. Many B2B companies possess a growth strategy, or at the very least, target revenue goals. Yet instead of focusing on aligning their strategy with how they operate, they quickly turn to buying new technology or reorganizing resources, thinking that it will solve their problems.
The Power of Process
Process matters immensely. It has a direct impact on the customers’ experiences, whether the process is internally focused or is a customer-facing activity. Processes accelerate an organization’s time to market, allow organizations to adapt to market changes, lead to predictable outcomes, enhance efficiency, reduce costs, and increase profitability. In essence, process is the engine that drives growth, not just by optimizing performance but by enabling companies to be agile and responsive in a dynamic market environment.
Implementing Process Optimization
To adopt process optimization as the required tool used to execute your strategy, follow these steps:
Articulate the Business Challenge
- Clearly define what you are trying to solve for; assess the impact, importance, and urgency of the challenge; and sell the business case that justifies the resources necessary to deliver the value.
Connect Root Causes to Potential Solutions
- Understand why the challenge exists and how it can be addressed. This often involves linking multiple processes — planning, preparation, and production — to craft a comprehensive solution.
Take Action
- Move beyond planning and preparation to actual execution. Ensure that you have the processes sequenced to minimize rework and costs, allocate resources effectively, and measure baseline performance to gauge the impact of your efforts.
Allow the Change to be Progressive
- Processes are critical to driving a culture of alignment and collaboration. Conducting periodic retrospectives, creating an iterative feedback loop with stakeholders, and breaking down your strategy into manageable, agile pieces all helps to maintain alignment and enable progression toward business-objective achievement. Understand, however, that perfection is often the enemy of progress.
Celebrating Successes and Maintaining Momentum
Finally, don’t forget to celebrate wins, both big and small, along the way! Providing an avenue for team members to acknowledge their successes fosters a culture of recognition and motivates teams to strive for continuous improvement. It’s a vital component of maintaining momentum and reinforcing the value of the processes that have been put in place.
Conclusion
The significance of process in strategy execution cannot be overstated. It’s the linchpin that connects strategic planning with tangible outcomes. By investing in and meticulously designing interconnected processes, organizations can not only anticipate but also adapt to market changes, ultimately driving sustainable growth. The journey from strategy to execution may be complex, but with a robust process as your guide, achieving your B2B goals is not just possible — it’s probable.
FAQs
- What is the importance of process in B2B growth?
Process is the backbone of converting strategy into results, driving growth, and enabling organizations to be agile and responsive in a dynamic market environment. - Why do many organizations falter in executing their strategies?
The answer often lies in the gap between those who devise strategies and those tasked with implementing them. - How can I implement process optimization in my organization?
Follow the steps outlined in this article, starting with articulating the business challenge and connecting root causes to potential solutions, and then taking action to execute your strategy.
Innovation and Technology
The Impact of Cloud Computing on the Workplace: What You Need to Know

As technology continues to evolve, emerging trends in workplaces are revolutionizing the way we work. With the rise of cloud computing, organizations are increasingly adopting this technology to improve efficiency, reduce costs, and enhance collaboration. In this article, we’ll explore the impact of cloud computing on the workplace, its benefits, and what you need to know to make the most of this revolutionary technology.
What is Cloud Computing?
Cloud computing is a model for delivering computing services over the internet, where resources such as servers, storage, databases, software, and applications are provided as a service to users on-demand. Instead of having to manage and maintain physical hardware and software, organizations can access these resources on a pay-as-you-go basis, allowing for greater flexibility and scalability.
Benefits of Cloud Computing in the Workplace
### Increased Flexibility and Scalability
Cloud computing allows organizations to scale up or down according to their needs, without the need for significant upfront investments in hardware and software. This flexibility is particularly beneficial for small and medium-sized businesses, which may not have the resources to invest in expensive infrastructure.
### Reduced Costs
Cloud computing eliminates the need for capital expenditures on hardware and software, reducing costs and freeing up resources for more important things. Additionally, cloud providers typically take care of maintenance, updates, and support, further reducing the burden on in-house IT teams.
### Improved Collaboration and Communication
Cloud computing enables teams to collaborate more effectively, regardless of location. With cloud-based tools and applications, remote workers can access and share files, documents, and resources, making it easier to work together and stay connected.
Challenges and Concerns
### Security and Data Protection
One of the primary concerns with cloud computing is security and data protection. With sensitive data stored in the cloud, organizations need to ensure that it is properly secured and protected from unauthorized access, theft, or loss.
### Dependence on Internet Connectivity
Cloud computing requires a reliable internet connection, which can be a challenge in areas with poor or unreliable internet connectivity. This can impact productivity and efficiency, particularly for remote workers.
Best Practices for Implementing Cloud Computing in the Workplace
### Plan and Strategy
Before implementing cloud computing, it’s essential to develop a clear plan and strategy for adoption, including identifying the right provider, assessing security and compliance requirements, and determining the most suitable applications and services.
### Choose the Right Provider
With so many cloud providers to choose from, it’s crucial to select the right one for your organization’s needs. Consider factors such as security, reliability, scalability, and customer support when making your decision.
### Monitor and Manage
Once cloud computing is implemented, it’s essential to monitor and manage the environment to ensure optimal performance, security, and compliance. This includes regular backups, updates, and maintenance to ensure the cloud infrastructure is running smoothly and efficiently.
Conclusion
In conclusion, cloud computing has revolutionized the way we work, offering increased flexibility, reduced costs, and improved collaboration. While there are challenges and concerns, with careful planning, strategy, and implementation, organizations can reap the benefits of cloud computing and stay ahead of the curve in a rapidly changing digital landscape.
Frequently Asked Questions
Q: Is cloud computing secure?
A: Yes, cloud computing can be secure, but it requires proper planning, implementation, and management to ensure data protection and security.
Q: Can I use cloud computing for my small business?
A: Yes, cloud computing is suitable for small businesses, offering flexibility, scalability, and cost savings.
Q: How do I choose the right cloud provider?
A: Consider factors such as security, reliability, scalability, and customer support when selecting a cloud provider for your organization’s needs.
Q: What are the benefits of cloud computing in the workplace?
A: Cloud computing offers increased flexibility, reduced costs, improved collaboration, and improved scalability, among other benefits.
Q: What are the challenges of cloud computing in the workplace?
A: Common challenges include security and data protection, dependence on internet connectivity, and vendor lock-in.
Q: How do I get started with cloud computing in the workplace?
A: Start by developing a clear plan and strategy for adoption, choosing the right provider, and implementing cloud-based tools and applications for your organization’s needs.
Innovation and Technology
7 Deadly Mistakes That Kill Most Enterprise AI Projects

The Expectation-Reality Gap
Think of AI projects like icebergs. What executives see in vendor presentations and tech magazines is the gleaming tip above water – the finished, polished success stories. What remains hidden is the massive underlying structure of data preparation, infrastructure requirements, talent needs, and organizational change management that makes those successes possible.
This expectation-reality gap is perhaps the most fundamental reason AI projects fail. There’s a persistent mythology that AI is a magical technology you simply "apply" to business problems like a high-tech bandage. The truth is messier and more demanding.
Flying Without Instruments: The Data Dilemma
If there’s one factor that dooms more AI projects than any other, it’s poor data quality and governance. Organizations consistently underestimate both the quantity and quality of data required for AI to function effectively.
The reality is that AI systems are fundamentally data processing engines. Feed them poor data, and you’ll get poor results – a principle computer scientists call "garbage in, garbage out" that has existed since the 1950s but somehow keeps surprising executives.
Missing The Human Element
Another fatal error is treating AI implementation as purely a technical challenge rather than a socio-technical one that requires human adoption and integration.
I recall a manufacturing firm that spent $1.8 million on an AI system to optimize production planning. The technology worked perfectly in testing, but on the factory floor, supervisors continued using their traditional methods and simply ignored the AI’s recommendations. Why? Because no one had involved them in the development process, explained how the system worked or addressed their legitimate concerns about how it would affect their roles.
The Strategy Disconnect
Many AI projects begin with a critical flaw: they lack clear connections to genuine business problems and strategic objectives. They’re solutions in search of problems rather than the other way around.
I’ve watched organizations launch AI initiatives because competitors were doing so or because the C-suite read about the technology in a business magazine. These projects inevitably fail because they’re not anchored to specific, measurable business outcomes.
Talent And Governance Shortfalls
The AI talent gap remains enormous. Data scientists are in short supply, and those with the rare combination of technical expertise and business acumen are as scarce as diamonds in a sandbox.
Beyond talent, many organizations lack proper governance structures for AI initiatives. Who owns the project? Who makes decisions when trade-offs arise between speed, cost, and quality? Without clear accountability and decision frameworks, AI projects drift into ambiguity and eventually failure.
Skipping The Foundation Work
Think of enterprise AI as a house. You can’t build the roof before you’ve laid the foundation and framed the walls. Yet organizations routinely attempt to implement advanced AI capabilities before establishing basic data infrastructure and analytics competencies.
The Path Forward: Making AI Projects Succeed
The high failure rate of AI initiatives isn’t inevitable. Organizations that approach AI with appropriate planning, resources, and expectations dramatically improve their odds of success.
Start with problems, not technology. Identify specific business challenges where AI might provide solutions and articulate clear, measurable objectives. This anchors the project in business reality rather than technological possibility.
Conclusion
AI isn’t magic – it’s a powerful set of technologies that, when properly implemented, can deliver extraordinary business value. However, that implementation requires rigor, realism, and resources that many organizations underestimate.
FAQs
Q: What are the most common mistakes organizations make when implementing AI?
A: Underestimating the quantity and quality of data required, treating AI implementation as purely technical, and failing to involve end-users in the development process.
Q: What are the key factors that contribute to the high failure rate of AI initiatives?
A: Poor data quality and governance, lack of clear connections to genuine business problems and strategic objectives, and inadequate talent and governance structures.
Q: What are the essential steps to ensure AI project success?
A: Start with problems, not technology; invest in data quality and infrastructure; treat AI implementation as organizational change; take an incremental approach; and establish clear governance and decision-making frameworks.
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