A study by Boston Consulting Group (BCG) suggests that organisations that lead in technology development are gaining a first-mover advantage when it comes to artificial intelligence (AI) and using agentic AI to improve business processes.
What is striking about BCG’s findings, according to Jessica Apotheker, managing director and senior partner at Boston Consulting Group, is that the leading companies in AI are mostly the same ones that were leaders eight years ago.
“What this year’s report shows is that the value gap between these companies and others is widening quite a bit,” she says. In other words, BCG’s research shows that organisations that have invested disproportionately in technology achieve a higher return from that investment.
Numerous pieces of research show that a high proportion of AI initiatives are failing to deliver measurable business success. BCG’s Build for the future 2025 report shows that the companies it rates as the best users of AI generate 1.7 times more revenue growth than the 60% of companies in the categories it defines as stagnating or emerging.
However, there is certainly a case to build on previous initiatives such as robotic process automation (RPA).
Speaking at the recent Forrester Technology and Innovation Summit in London, Bernhard Schaffrik, principal analyst at Forrester, discussed how agentic AI can be built on top of a deterministic RPA system to provide greater flexibility than what existing systems can be programmed to achieve.
The analyst firm uses the term “process orchestration” to describe the next level of automating business processes, using agentic AI in workflow to handle ambiguities far more easily than the programming scripts used in RPA.
“Classic process automation tools require you to know everything at the design stage – you need to anticipate all of the errors and all the exceptions,” says Schaffrik.
He points out that considering these things at design time is unrealistic when trying to orchestrate complex processes. But new tools are being developed for process orchestration that rely on AI agents.
A strong data foundation
Boston Consulting Group (BCG) says prerequisites for the successful roll-out of AI agents include strong data foundations, scaled AI capabilities and clear governance.
Standardisation of data is a key requirement for success, according to Twig. “A big part of the issue is data,” he says. “AI is only as strong as the information it runs on, and many companies don’t have the standardised, consistent datasets needed to train or deploy it reliably.”
Within the context of agentic AI, this is important to avoid miscommunications both at the technology infrastructure level and in people’s understanding of the information. But the entire data foundation does not have to be built all at once.
BCG’s Apotheker says companies can have an enterprise-wide goal to achieve clean data, and build this out one project at a time, providing a clean data foundation on which subsequent projects can be built. In doing so, organisations are able to gain a better understanding of the enterprise data these projects require while they ensure that the datasets are clean and good data management practices are followed.
A working agentic AI strategy relies on AI agents connected by a metadata layer, whereby people understand where and when to delegate certain decisions to the AI or pass work to external contractors. It’s a focus on defining the role of the AI and where people involved in the workflow need to contribute.
This functionality can be considered a sort of platform. Scott Willson, head of product marketing at xtype, describes AI workflow platforms as orchestration engines, coordinating multiple AI agents, data sources and human touchpoints through sophisticated non-deterministic workflows. At the code level, these platforms may implement event-driven architectures using message queues to handle asynchronous processing and ensure fault tolerance.
Data lineage tracking should happen at the code level through metadata propagation systems that tag every data transformation, model inference and decision point with unique identifiers. Willson says this creates an immutable audit trail that regulatory frameworks increasingly demand. According to Willson, advanced implementations may use blockchain-like append-only logs to ensure governance data cannot be retroactively modified.
Adapting workflows and change management
Having built AI-native systems from the ground up and transformed the company’s own product development processes using AI, Alan LeFort, CEO and co-founder of StrongestLayer, notes that most organisations are asking completely the wrong questions when evaluating AI workflow platforms.
“The fundamental issue isn’t technological, it’s actually organisational,” he says.
Conway’s Law states that organisations design systems that mirror their communication structures. But, according to LeFort, most AI workflow evaluations assume organisations bolt AI onto existing processes designed around human limitations. This, he says, results in serial decision-making, risk-averse approval chains and domain-specific silos.
When you try to integrate AI into human-designed processes, you get marginal improvements. When you redesign processes around AI capabilities, you get exponential gains Alan LeFort, StrongestLayer
“AI doesn’t have those limitations. AI can parallelise activities that humans must do serially, doesn’t suffer from territorial knowledge hoarding and doesn’t need the elaborate safety nets we’ve built around human fallibility,” he adds. “When you try to integrate AI into human-designed processes, you get marginal improvements. When you redesign processes around AI capabilities, you get exponential gains.”
StrongestLayer recently transformed its front-end software development process using this principle. Traditional product development flows serially. A product manager talks to customers, extracts requirements and then hands over to the user experience team for design, the programme management team then approves the design, and developers implement the software. It used to take 18-24 months to completely rebuild the application in this process, he says.
Instead of bolting AI onto this process, LeFort says StrongestLayer “fundamentally reimagined it”.
“We created a full-stack prototyper role-paired with a front-end engineer focused on architecture. The key was building an AI pipeline that captured the contextual knowledge of each role: design philosophy, tech stack preferences, non-functional requirements, testing standards and documentation needs.”
As a result of making these workload changes, he says the company was able to achieve the same outcome from a product development perspective in a quarter of the time. This, he says, was not necessarily achieved by working faster, but by redesigning the workflow around AI’s ability to parallelise human sequential activities.
LeFort expected to face pushback. “My response was to lead from the front. I paired directly with our chief product officer, Joshua Bass, to build the process, proving it worked before asking others to adopt it. We reframed success for our front-end engineer around velocity and pioneering new ways of working,” he says.
For LeFort, true speed to value comes from two fundamental sources: eliminating slack time between value activities and accelerating individual activity completion through AI automation. “This requires upfront investment in process redesign rather than quick technology deployment,” he says.
LeFort urges organisations to evaluate AI workflow platforms based on their ability to enable fundamental process transformation, rather than working to integrate existing inefficiencies.
Getting agentic AI decision-making right
Research from BCG suggests that the best way to deploy agents is through a few high-value workflows with clear implementation plans and workforce training, rather than in a massive roll-out of agents everywhere at once.
There are different models with different strengths. We want to use the best model for each task Ranil Boteju, Lloyds Banking Group
One of the areas IT leaders need to consider is that their organisation will more than likely rely on a number of AI models to support agentic AI workflows. For instance, Ranil Boteju, chief data and analytics officer at Lloyds Banking Group, believes different models can be tasked with tackling each distinct part of a customer query.
“The way we think about this is that there are different models with different strengths, and what we want to do is to use the best model for each task,” says Boteju. This approach is how the bank sees agentic AI being deployed.
With agentic AI, problems can be broken down into smaller and smaller parts, where different agents respond to each part. Boteju believes in using AI agents to check the output from other agents, rather like acting as a judge or a second-line colleague acting as an observer. This can help to cut erroneous decision-making arising from AI hallucinations when the AI model basically produces a spurious result.
IT security in agentic AI
People in IT tend to appreciate the importance of adhering to cyber security best practices. But as Fraser Dear, head of AI and innovation at BCN, points out, most users do not think like a software developer who keeps governance in mind when creating their own agents. He urges organisations to impose policies that ensure the key security steps are not skipped in the rush to deploy agentic AI.
“Think about what these AI agents might access across SharePoint: multiple versions of documents, transcripts, HR files, salary data, and lots more. Without guardrails, AI agents can access all this indiscriminately. They won’t necessarily know which versions of these documents are draft and which are approved,” he warns.
The issue escalates when an agent created by one person is made available to a wider group of colleagues. It can inadvertently give them access to data that is beyond their permission level.
Dear believes data governance needs to include configuring data boundaries, restricting who can access what data according to job role and sensitivity level. The governance framework should also specify which data resources the AI agent can pull from.
In addition, he says AI agents should be built for a purpose, using principles of least privilege: “Just like any other business-critical application, it needs to be adequately tested and ‘red-teamed’. Perform penetration testing to identify what data the agent can surface, to whom, and how accurate the data is. Track and audit which agents are accessing which data and for what purpose, and implement real-time alerts to flag unusual access patterns.”
A bumpy ride ahead
What these conversations with technology experts illustrate is that there is no straightforward path to achieving a measurable business benefit from agentic AI workflows – and what’s more, these systems need to be secure by design.
Organisations need to have the right data strategy in place, and they should already be well ahead on their path to full digitisation, where automation through RPA is being used to connect many disparate workflows. Agentic AI is the next stage of this automation, where an AI is tasked with making decisions in a way that would have previously been too clunky using RPA.
However, automation of workflows and business processes are just pieces of an overall jigsaw. There is a growing realisation that the conversation in the boardroom needs to move beyond the people and processes.
BCG’s Apotheker believes business leaders should reassess what is important to their organisation and what they want to focus on going forward. This goes beyond the build versus buy debate: some processes and tasks should be owned by the business; some may be outsourced to a provider that may well use AI; and some will be automated through agentic AI workflows internally.
It is rather like business process engineering, where elements powered by AI sit alongside tasks outsourced to an external service provider. For Apotheker, this means businesses need to have a firm grasp of what part of the business process is strategically important and can be transformed internally.
Business leaders then need to figure out how to connect the strategically important part of the workflow to what the business actually outsources or potentially automates in-house.
Three months ago, OpenAI cofounder Greg Brockman told me his concerns about a mounting public relations crisis facing artificial intelligence companies: Despite the popularity of tools like ChatGPT, an increasingly large share of the population said they viewed AI negatively. Since then, the backlash has only intensified.
College commencement speakers are now getting booed for talking about AI in optimistic terms. Last month, someone threw a Molotov cocktail at OpenAI CEO Sam Altman’s San Francisco home and wrote a manifesto advocating for crimes against AI executives. No one has more to lose from this reputation crisis than OpenAI.
The person tasked with trying to fix it is Chris Lehane, OpenAI’s chief of global affairs and a veteran political operative. I sat down with him this week to discuss what I’d argue are his two biggest challenges yet: convincing the world to embrace OpenAI’s technology, while at the same time persuading lawmakers to adopt regulations that won’t hamper the company’s growth. Lehane views these goals as one in the same.
“When I was in the White House, we always used to talk about how good policy equals good politics,” says Lehane. “You have to think about both of these things moving in concert.”
After working on crisis communications in Bill Clinton’s White House, Lehane gave himself the nickname “master of disaster.” He later helped Airbnb fend off regulators in cities that viewed short-term home rentals as existing in a legal gray area, or as he puts it, “ahead of the law.” Lehane also played an instrumental role in the formation of Fairshake, a powerful crypto industry super PAC that worked to legitimize digital currencies in Washington. Since joining OpenAI in 2024, he’s quickly become one of the company’s most influential executives and now oversees its communications and policy teams.
Lehane tells me public narratives about how AI will change society are often “artificially binary.” On one side is the “Bob Ross view of the world” that predicts a future where nobody has to work anymore and everyone lives in “beachside homes painting in watercolors all day.” On the other is a dystopian future in which AI has become so powerful that only a small group of elites have the ability to control it. Neither scenario, in Lehane’s opinion, is very realistic.
OpenAI is guilty of promoting this kind of polarizing speech in the past. CEO Sam Altman warned last year that “whole classes of jobs” will go away when the singularity arrives. More recently he has softened his tone, declaring that “jobs doomerism is likely long-term wrong.”
Lehane wants OpenAI to start conveying a more “calibrated” message about the promises of AI that avoids either of these extremes. He says the company needs to put forward real solutions to the problems people are worried about, such as potential widespread job loss and the negative impacts of chatbots on children. As an example of this work, Lehane pointed to a list of policy proposals that OpenAI recently published, which include creating a four-day work week, expanding access to health care, and passing a tax on AI-powered labor.
“If you’re going to go out and say that there are challenges here, you also then have an obligation—particularly if you’re building this stuff—to actually come up with the ideas to solve those things,” Lehane says.
Some former OpenAI employees, however, have accused the company of downplaying the potential downsides of AI adoption. WIRED previously reported that members of OpenAI’s economic research unit quit after they became concerned that it was morphing into an advocacy arm for the company. The former employees argued that their warnings about AI’s economic impacts may have been inconvenient for OpenAI, but they honestly reflected what the company’s research found.
Packing Punches
With public skepticism toward AI growing, politicians are under pressure to prove to voters they can rein in tech companies. To combat this, the AI industry has stood up a new group of super PACs that are boosting pro-AI political candidates and trying to influence public opinion about the technology. Critics say the move backfired, and some candidates have started campaigning on the fact that AI super PACS are opposing them.
Lehane helped set up one of the biggest pro-AI super PACs, Leading the Future, which launched last summer with more than $100 million in funding commitments from tech industry figures, including Brockman. The group has opposed Alex Bores, the author of New York’s strongest AI safety law who is running for Congress in the state’s 12th district.
Leah Feiger: —had that vibe. To me, someone so on the outside of this in every single way, I know about these layoffs because they’ve been, A) so chaotic, but B) in some ways, needlessly so. Not to say that other tech companies aren’t firing scores of workers all the time. That feels like something we discuss on this podcast frequently, but this is happening with such a large runway and in a way that’s making employees feel so terrible about themselves.
Brian Barrett: Well, because it’s not just the layoffs, right? It’s also, even if you stay there, if you’re not culled from the herd, you are going to have to deal with this world in which you’ve got spyware on your laptops training AI to probably take your job at some point, right?
Zoë Schiffer: Explain that a little bit.
Brian Barrett: Meta announced, and this was more public, that they were going to put software on employee laptops that would monitor their keystrokes and how they move their cursors and basically how they do their job as Meta engineers and use that as training data for their own internal models to try to make their AI models better because they’re running out of other sources.
Zoë Schiffer: And could you opt out of that, Brian?
Brian Barrett: That’s a great question. I’m so glad you asked. You could not opt out.
Zoë Schiffer: I felt you didn’t know the answer to that one.
Brian Barrett: In fact, when an employee asked in a very public forum within Meta, “Hey, could we not do this?” Zoë, the response was?
Zoë Schiffer: Oh, absolutely you’re going to do this and shame on you for asking. And some of the employees who are staying, actually thousands of the employees who are staying, are getting drafted into the AI ranks. We published a piece today that was kind of about the morale inside the company, but also how there’s been this mad dash to use up perks and stipends that employees have. But one of the things that’s said at the end was that remaining employees are being asked to join AI teams. So whatever your job was previously, they’re internally getting drafted. You’re getting drafted into the AI ranks, now your job is going to look quite different.
Brian Barrett: That’s like 7,000 people.
Zoë Schiffer: Yes.
Leah Feiger: I’ve actually heard people use the word raptured.
Zoë Schiffer: Oh, my gosh.
Leah Feiger: Isn’t that—
Zoë Schiffer: And I wish we had that in the story.
Leah Feiger: I’m so sorry, but raptured into other teams. All of a sudden one day they’ve just disappeared. After this layoff, has Zuckerberg and co proposed a sort of coherent leadership plan or proposal? What happens after this?
Atlantic hurricane season is almost upon us, and the early signs indicate it might be less active than usual. But that’s no reason to delete your weather app and ignore the forecast.
The National Oceanic and Atmospheric Administration is predicting eight to 14 named tropical systems, of which three to six will become hurricanes and one to three will be Category 3 or higher.
“What’s driving this forecast is largely an El Niño event,” said NOAA administrator Neil Jacobs.
Characterized by a tongue of hot water stretching across the Pacific, El Niño is likely to emerge this summer. That stretch of warm ocean rearranges weather patterns around the world. In the case of the tropical Atlantic, El Niño stirs up winds that make it hard for hurricanes to spin up. Those that do can sometimes be torn apart by what’s going on in the upper atmosphere. (The opposite is true in the Pacific, and NOAA is predicting a very active season in that ocean basin.)
During the three past super El Niños, accumulated cyclone energy—a metric that factors in storms’ strength and longevity—was well below normal.
That said, El Niño, even an extremely strong one, is only one of many factors that impact hurricane season. Hot local ocean temperatures can help storms form and gain strength, and the Atlantic is currently warmer than normal.
At the same time, Sahara dust can gum up the atmosphere and inhibit storms from forming. It’s also notoriously hard to predict when plumes of it will kick up. That’s what happened last year, when a below-average number of named storms formed despite an active forecast. Despite the lower-than-expected activity, last year still spawned Hurricane Melissa, one of the strongest storms to ever make landfall in the Atlantic basin.
All of which is to say that the seasonal forecast is a handy guide for what to expect, and it’s great for federal and state agencies to preposition supplies and resources. But it’s what happens with individual storms that ultimately matters.
“Even though we’re expecting a below average season in the Atlantic, it’s important to understand it only takes one,” Jacobs said, noting that even in quiet years, Category 5 storms have still made landfall.
The Trump administration has slashed staffing at NOAA and reduced the collection of some data, such as weather balloons, that can impact forecasts. Jacobs touted the value of new observations, including aerial drones that will be deployed operationally for the first time.
NOAA has also ramped up the use of artificial intelligence weather models trained on historical data. During the 2025 hurricane season, the agency tested an experimental hurricane model developed with Google DeepMind. Late last year, it also rolled out a suite of AI weather models to use in operational forecasting, in addition to traditional weather models that use equations to forecast the weather.
The agency says that the AI version of its flagship model provides better prediction of the tracks of tropical cyclones—the generic name for hurricanes—though it lags traditional weather models in predicting their intensity.
Fashion1 week ago
Entertainment6 days ago
Entertainment1 week ago
Fashion1 week ago
Politics6 days ago
Fashion1 week ago