Abigail Tate

Business Development Manager – IBM Software | TD SYNNEX UK&I

Harnessing the power of IBM watsonx with business fundamentals 

If you’d have told me when I was studying a Business Analysis degree that it would lead to 20 years with IBM, 7 years (and counting) with TD SYNNEX and a lifelong love affair with data, I’d have given you one of my ‘looks’. All I’d wanted was to study an interesting topic, close to where I grew up, and yet, I’d made a choice that led me to the cutting edge of business and technology in a way no careers officer could have predicted. 

Today, I’m responsible for helping IBM Business partners understand how the IBM watsonx portfolio can solve business problems. And what strikes me most is not the rate at which the world has moved on from assessing datasets by hand to using AI-powered software models, but rather, how little has changed when it comes to businesses’ priorities. 

So, as a counterpoint to articles focused on whether AI is headed the same way as the dotcom bubble, and when the ‘rise of the robots’ will take our jobs, I thought it would be useful to offer a perspective that showcases what happens when you combine the power of IBM watsonx with getting the business basics right. 

Setting the Scene: Pilots Are Easy, ROI Is Hard 

My earliest days of study focused on using data to answer fundamental business questions. “What products sell best and when?” “What campaign yielded the best results and why?” We soon learned that spinning something up is easy, but demonstrating a project has paid for itself is hard. And despite all that AI can do, this challenge persists today. 

A 2025 MIT study concluded that whilst it’s easy to launch AI pilot programmes, c.95% fail to deliver measurable ROI. The core issue reported was the difficulty of integrating AI into complex business workflows, achieving scale, and managing the human element of change.  

This isn’t unique to AI – we have all seen technology waves come and go, and each with a similar pattern: 

Excitement → experimentation → the inevitable leadership question: 

“Where’s the business value?” 

Gartner noted that “most agentic AI projects today are early-stage experiments or proofs of concept driven by hype.” Predicting more than 40% of agentic AI projects will be cancelled by 2027 due to cost, complexity or lack of clear business value.  This goes some way to explaining why many organisations are in a place of: 

• Lots of AI pilots 
• Lots of proofs of concept 
• Very little scaled business impact 

So how do we make sure businesses don’t fall at the first hurdle? We go back to basics! 

Business Basic 1: Technology can’t fix bad data 

The importance of data quality and accessibility remain vital and relevant. Just like people, Agentic AI systems need context and high-quality trusted data to make decisions and execute tasks. When data is fragmented across silos, AI agents don’t have the business intelligence they need to operate effectively.  

Platforms like IBM watsonx.data help organisations unify data across hybrid environments using a data lakehouse approach – giving AI systems the context they need to operate effectively.  But technology alone isn’t the answer. The real challenge is building a data architecture and culture that supports AI at scale. 

Business Basic 2: Automating tasks isn’t the same as transforming the business 

Another age-old issue is the temptation to focus on small automation wins.  It’s easy to deploy an AI agent to solve one problem; it’s much harder to redesign a business process. The organisations seeing real results are the ones thinking about workflow transformation, not just task automation. 

IBM watsonx.orchestrate helps organisations connect AI agents across workflows – but the real work is rethinking how those workflows operate in the first place. 

Business Basic 3: Governance must be a strategic imperative 

In the early days of enterprise data platforms, governance was an afterthought. That now can’t happen with autonomous AI systems. Without clear governance, organisations risk bias, compliance issues and unpredictable outcomes.  

IBM watsonx.governance helps organisations monitor AI behaviour, manage risk and maintain trust as AI scales. Organisations getting the most value from AI treat governance as a strategic capability, and not a compliance exercise.  

Getting the basics right = successful outcomes 

The good news is that we’re starting to see what success looks like when data, AI and workflows come together, as these three IBM case studies demonstrate: 

• The Ultimate Fighting Championship (yes, you read that correctly) worked with IBM to use AI-powered insights to accelerate how content and data are generated for fans – helping teams access insights faster and improve fan engagement.

• Pfizer has long partnered with IBM to use AI and advanced analytics to help accelerate research and improve how complex scientific data from vast datasets is extracted and analysed.

• The City of Helsinki is using AI assistants to combine multiple healthcare and social service chatbots into a single experience, making it easier for citizens to access services.

What’s different to previous technology cycles is that AI isn’t just another upgrade – or one that organisations can opt out of.  It’s a fundamental change in how organisations operate. The companies that succeed will be the ones who build stronger enterprise data foundations, orchestrate AI across multiple business workflows and implement robust governance frameworks, all whilst bringing their people along for the journey.

Technology creates opportunity, but the real value is created when real organisational change happens, and in the era of Agentic AI, that distinction has never mattered more.

This article was written by Abigail Tate, Business Development Manager for IBM Software at TD SYNNEX. For more information, contact Abi by emailing her at abigail.tate@tdysynnex.com or by completing the form below.