According to AI and digital transformation specialist Bane Hunter, adaptive project management is essential for modern businesses to thrive. Today’s business landscape is more volatile than ever. This volatility brings varying degrees of uncertainty for ever-growing numbers of companies of all shapes and sizes.

Bane Hunter on Transformation and Effective Program Management through AI and Adaptive-Based Data

Hunter says leveraging all-important, often invaluable data is one of the best ways to combat this volatility and uncertainty. Bane Hunter is a global executive specializing in digital transformation, AI, and program management, highly regarded for his ability to create value by integrating and leveraging cutting-edge technologies. In addition to this, the focus on speed of execution, cost containment, and commercial upside are always key milestones.

The following is a closer look at effective business transformation facilitated through adaptive data-led project management, courtesy of the New York-based executive.

What’s the difference between traditional and adaptive data-led project management?

Traditional and adaptive data-led project management follow many of the same core principles. However, several key differences set the two poles apart in various regards. Conventional project management cycles tend to stick very closely to predefined strategies. Traditional non-agile project managers tend to lay out their plans well in advance, allocating all known resources in a somewhat rigid model that is commonly known as a waterfall. Although there are many instances where waterfall still is the preferred methodology, failing to consider and/or deploy all the tool sets available to a project/transformation executive, severely reduces the efficiency and capability of the team.

These plans are usually quite rigid, meaning there’s little or no scope for significantly changing them over time without significant effort or cost – especially in larger or matrixed organizations. Work then gets underway, with all parties closely adhering to the strategy laid out by the project executive at the outset. The project is typically only reviewed in detail once all work is complete in each gate check stage. This can be a little too late, very much too expensive. More to the point there are occasions when even more effort is spent justifying the lack of results vs pivoting or optimizing the project track. Your shiny plane may end up having serious qualitative issues in other words.

By contrast, adaptive data-led project management involves continuous, ongoing reviews, planning, and adjustments/improvements. By taking a more adaptive agile approach, project managers can utilize data, take feedback, and reallocate resources as necessary. Combine this with an appropriate AI model, and you have supercharged your organization’s qualitative, quantitative, cost, and time to operational deployment deliverables.

It’s an approach far better suited to today’s fast-paced business environments where needs and requirements can change mid-project and even mid-day. It’s time to go beyond an Agile/Scrum mindset and evolve this into a symbiotic project and product management track that embraces an AI-driven data approach with an Agile foundation.

What are the main benefits of using adaptive-based data to manage projects?

Bane Hunter notes the benefits of adaptive data-led project management don’t just apply to businesses operating in fast-paced environments. The ability to tailor needs and requirements mid-project can benefit almost any organization.

These benefits become the most apparent when businesses and other organizations have grown used to only planning projects annually, for example. A lot can change in a year, especially within today’s highly volatile global business landscape. The problem some organizations face is the marriage to budgets that are planned on a yearly basis and the need to manage and plan within those constraints. The more evolved organizations have found ways to both satisfy the requirements of the CFO and CIO while keeping the CEO happy with the commercial results.

Other significant benefits are the ability to take on board multiple rounds of course correction and remain flexible throughout a project’s entire life cycle. Meanwhile, clever data usage allows project managers to anticipate bottlenecks, tackling them head-on before they can affect schedules, scope, or budgets.

Elsewhere, project managers can save money by utilizing data for adjusted, more cost-effective resource allocation. That’s as opposed to wasting capital on a scope that is not yielding the projected outcomes. These savings can be significant, particularly versus traditional long-term, fixed projects and their cycles.

What types of data are most effective for adaptive project management?

Project managers can utilize almost any available data to plan and manage their projects more effectively. Crucially, historical data is just as valuable as up-to-minute figures collected over the course of a live project. Data from past projects is invaluable for most accurately allocating resources at the outset. Competitive, research or market data is equally valuable, sometimes even more so.

Historical figures dating back weeks, months, or years are equally vital in many additional business transformation efforts. That’s because data from past projects allows those responsible to prioritize learning from earlier decisions and outcomes. The same is true from both project and business management standpoints. It’s all like an F1 or NASCAR team. With AI, data is the fuel that will power your engine, and your AI model is the engine that will power your car. A race can only be won with a good driver and pit crew using the right strategy and methodology.

Whether through adaptive project management or separate business transformation efforts, the goal is to cleverly utilize past and present data to improve current, ongoing, and future business practices.

Where else do adaptive, data-led approaches differ from traditional project management efforts?

Businesses often see a shift toward an entirely different way of running their operations by using data to facilitate a more adaptive approach to project management. For example, traditional project management has long allocated resources on a per-person basis. Adaptive project management, whether chiefly data-led or not, differs here by relying instead on teams.

By assigning work in teams, project managers are utilizing the skills and abilities of groups of people within their businesses rather than solely individuals. By doing so, there are far fewer opportunities for projects to hit bumps in the road where a lack of resources, as just one example, may be holding up the schedule.

When moving from traditional to more data-led project management, a significant shift in thinking is most definitely required. However, it’s an almost definitely worthwhile shift, factoring in the potential benefits.

Furthermore, allowing multiple rounds of feedback, rather than only reviewing a project on completion, means that input becomes fundamentally iterative. This iterative feedback isn’t only helpful in ongoing resource allocation and elsewhere. It also helps to prevent organizations from sinking money into what could otherwise turn into flawed finished products.

What does the future of business transformation and program management via adaptive-based data look like?

The future of program management and business transformation through adaptive-based data will likely mirror other agile methodologies in the same spaces. Whether used for project management, business transformation, or elsewhere, AI and agile methodologies are becoming increasingly central to success for organizations of all shapes and sizes today.

Almost across the board, adaptive data usage forms a large and inescapable part of broader technology transformation efforts. Such efforts are now the norm among record numbers of organizations where top and bottom-line efficiency are ultimately leading priorities. The same is true of most businesses and other for-profit operators alike.

With that, Bane Hunter explains that even in the age of rapid AI deployment and use, adaptive project management and other data-led strategies look set to become even more heavily utilized, as do strategic portfolio management efforts and similar corresponding approaches. If anything, AI will require a higher level of project/program/portfolio management expertise.

Whether looking to promote more robust project execution or streamline company strategies, these processes are now essential for driving meaningful value within organizations across the U.S. and worldwide.