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.
Renpho MorphoScan for $150: The Renpho MorphoScan full-body scanner looks surprisingly similar to the Runstar FG2015, including a near-identical display attached to the handlebars. Well, spoiler alert, they are basically the same scale. They even use the same app to collect data (and you can even use both scales simultaneously with it). The only reason this scale isn’t our top pick for the category is that it’s $15 more expensive. You can rest assured that a price war is looming.
Arboleaf Body Fat Scale CS20W
Photograph: Chris Null
Arboleaf Body Fat Scale CS20W for $40: This affordable Bluetooth scale isn’t the most eye-catching I’ve tested, owing to its big, silver electrodes and an oversized display that comes across as a bit garish. While weight is easy to make out, the six additional statistics showcased are difficult to read, all displayed simultaneously. I like the Arboleaf app better than the scale, where five more metrics can be found in addition to the seven above, each featuring a helpful explanation when tapping on it. It’s a solid deal at this price, but the upsell to get an “intelligent interpretation report” for an extra $40 per year is probably safe to skip.
Hume Health Body Pod
Photograph: Chris Null
Hume Health Body Pod for $183: Hume Health’s Body Pod, another full-body scanner with handles, is heavily advertised—at least to the apps on my phone—and touted (by Hume) as the Next Big Thing in the world of body management. While the app is indeed glossy and inviting, I was shocked to discover how flimsy the hardware felt, that it lacked Wi-Fi, and that some features are locked behind a $100-a-year Hume Plus subscription plan. It works fine enough, but you can get results that are just as good with a cheaper device.
Garmin Index S2 for $191: Five years after its release, the Index S2 is still Garmin’s current model, a surprise for a company otherwise obsessed with fitness. It’s still noteworthy for its lovely color display, which walks you through its six body metrics (for up to 16 users) with each weigh-in. The display also provides your weight trend over time in graphical form and can even display the weather. The scale connects directly to Wi-Fi and Garmin’s cloud-based storage system, so you don’t need a phone nearby to track your progress, as with Bluetooth-only scales. A phone running the Garmin Connect app (Android, iOS) is handy, so you can keep track of everything over time. Unfortunately, as health apps go, Connect is a bit of a bear, so expect a learning curve—especially if you want to make changes to the way the scale works. You can turn its various LCD-screen widgets on or off in the app, but finding everything can be difficult due to the daunting scope of the Garmin ecosystem. The color screen is nice at first, but ultimately adds little to the package.
Omron BCM-500 for $92: With its large LCD panel, quartet of onboard buttons, and oversize silver electrodes, the Omron BCM-500 is an eye-catching masterwork of brutalist design. If your bathroom is decked out in concrete and wrought iron, this scale will fit right in. The Bluetooth unit syncs with Omron’s HeartAdvisor app (Android, iOS), but it provides all six of its body metrics directly on the scale, cycling through them with each weigh-in (for up to four users). It can be difficult to read the label for each of the data points, in part because the LCD isn’t backlit, but the app is somewhat easier to follow, offering front-page graphs of weight, skeletal muscle, and body fat. On the other hand, the presentation is rather clinical, and the app is surprisingly slow to sync. For a scale without a Wi-Fi connection, it’s rather expensive too.
This applies not just to things you have to do, but also things you think you want to do. Maybe you think you should learn Spanish, but you haven’t done anything to actually learn Spanish. Admitting that you aren’t actually committed to the idea enough to do the work of learning Spanish can help close that loop. Letting go of that feeling that you should learn Spanish just might be the thing that frees up your mind enough that you decide to take up paddleboarding on a whim. The point is that the new year isn’t just a time for starting something new. It’s a time to let go of the things from that past that are no longer serving you.
In many ways this is the antidote to that ever-so-popular slogan “Just do it.” Just do it implies that you shouldn’t think about it, instead of deciding what you really want to do or should do. Maybe spend some time remembering why you wanted to do it in the first place, and if those reasons no longer resonate with you, just don’t do it.
If you like this idea, I highly recommend getting Allen’s book. It goes into much more detail on this idea and has some practical advice on letting go. You can still keep track of those things, in case you do decide, years from now, when you’re paddleboarding through the Sea of Cortez, that now you really do want to learn Spanish and are willing to do the work.
Remember to Live
I will confess, my enthusiasm for Getting Things Done has waned over the years. Not because the system doesn’t work, but because I have found my life more dramatically improved by doing less, not more. It’s not that I’ve stopped getting things done. It’s that I’ve found many of the things I felt like I should do were not really my idea; they were ideas I’d internalized from other places. I didn’t really want to do them, so I didn’t, then I felt guilty about it.
While everything I’ve written above remains good advice for starting a healthy habit and keeping it going, it’s worth spending some time and making sure you know why you want to do what you’re doing. I have been rereading Bertrand Russell’s In Praise of Idleness, and this line jumped out at me: “The modern man thinks that everything ought to be done for the sake of something else, and never for its own sake.”
I will admit to being afraid of scales—the kind that weigh you, not the ones on a snake. And so my first reaction to the idea I’d be getting a free body-scanning scale with a Factor prepared meal kit subscription was something akin to “Oh no!”
It’s always bad or shameful news, I figured, and maybe nothing I don’t already know. Though, as it turned out, I was wrong on both points.
Factor is, of course, the prepared meal brand from meal kit giant HelloFresh, which I’ve tested while reviewing dozens of meal kits this past year. Think delivery TV dinners, but actually fresh and never frozen. Factor meals are meant to be microwaved, but I found when I reviewed Factor last year that the meals actually tasted much better if you air-fry them (ideally using a Ninja Crispi, the best reheating device I know).
Especially, Factor excels at the low-carb and protein-rich diet that has become equally fashionable among people who want to lose weight and people who like to lift it. Hence, this scale. Factor would like you to be able to track your progress in gaining muscle mass, losing fat, or both. And then presumably keep using Factor to make your fitness or wellness goals.
While your first week of Factor comes at a discount right now, regular-price meals will be $14 to $15 a serving, plus $11 shipping per box. That’s less than most restaurant delivery, but certainly more than if you were whipping up these meals yourself.
If you subscribe between now and the end of March, the third Factor meal box will come with a free Withings Body Comp scale, which generally retails north of $200. The Withings doesn’t just weigh you. It scans your proportions of fat and bone and muscle, and indirectly measures stress levels and the elasticity of your blood vessels. It is, in fact, WIRED’s favorite smart scale, something like a fitness watch for your feet.
Anyway, to get the deal, use the code CONWITHINGS on Factor’s website, or follow the promo code link below.
Is It My Body
The scale that comes with the Factor subscription is about as fancy as it gets: a $200 Body Comp scale from high-tech fitness monitoring company Withings. The scale uses bioelectrical impedance analysis and some other proprietary methods in order to measure not just your weight but your body fat percentage, your lean muscle mass, your visceral fat, and your bone and water mass, your pulse rate, and even the stiffness of your arteries.
To get all this information, all you really need to do is stand on the scale for a few minutes. The scale will recognize you based on your weight (you’ll need to be accurate in describing yourself when you set up your profile for this to work), and then cycle through a series of measurements before giving you a cheery weather report for the day.
Withings
Body Comp Smart Scale
Your electrodermal activity—the “skin response via sweat gland stimulation in your feet”—provides a gauge of stress, or at least excitation. The Withings also purports to measure your arterial age, or stiffness, via the velocity of your blood with each heartbeat. This sounds esoteric, but it has some scientific backing.
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